The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) in future 6G networks (6G-IoT) promises to unlock unprecedented capabilities. However, the continuous collection and analysis of large-scale, low-density data pose significant threats to the reliability and trustworthiness of these systems, leading to high energy consumption and potential decision-making based on stale information. To address these critical challenges, this paper proposes a novel architecture for building reliable and trustworthy 6G-IoT services. Our approach involves three key contributions: 1) We leverage the multi-access edge computing (MEC) paradigm to locally process raw data, filtering redundancy and thereby ensuring that AI models operate on more meaningful information. 2) We design a decoupled, two-level (edge-cloud) decision-making mechanism that explicitly manages the trade-off between data trustworthiness (quantified by information freshness) and system energy consumption, a cornerstone of long-term reliability. 3) We implement these principles in a new, distributed end-edge-cloud framework named Quesada (Query-control-based safe and data-trustworthy acquisition), which coordinates edge and cloud decisions to enhance overall system performance. To validate our approach, we conduct a series of comparative experiments. The results demonstrate that the Quesada framework significantly improves both system reliability and data trustworthiness, making it a viable architecture for future 6G-IoT applications.

The persistent disparity between the slow growth of wide-area network bandwidth and the escalating demand for high-speed bulk data transfer poses a significant challenge for inter-datacenter data transmission. Existing approaches typically rely on optimized algorithms for dedicated networks and often struggle to meet the stringent deadline requirements of modern applications cost-effectively. To overcome these limitations, we propose a novel cloud-accelerated transfer approach that is intrinsically deadline-aware. We present a Profit-driven RelAxation-based TransfEr Scheduling (PRATER) system that leverages cloud proxies and multipath transmission to accelerate bulk data transfer. Specifically, we tackle the problem of profit maximization by jointly optimizing cloud proxy deployment and traffic allocation under transmission deadlines, a problem formally modeled and identified as NP-hard. To efficiently derive near-optimal solutions, we decompose this complex problem into two interconnected subproblems: cost minimization through proxy deployment optimization and revenue maximization via traffic allocation optimization. Our efficient iterative algorithm resolves these subproblems by minimizing cloud proxy and bandwidth costs while maximizing timely data transfer completions, thereby enhancing overall system profitability. Extensive experimental results demonstrate that our approach achieves a significant profit improvement of approximately 2.4×-6.1× compared to state-of-the-art existing algorithms.

The Internet of Vehicles (IoV) is emerging as a pivotal technology for enhancing traffic management and safety. Its rapid development demands solutions for enhanced communication efficiency and reduced latency. However, traditional centralized networks struggle to meet these demands, prompting the exploration of decentralized solutions such as blockchain. Addressing blockchain's scalability challenges posed by the growing number of nodes and transactions calls for innovative solutions, among which sharding stands out as a pivotal approach to significantly enhance blockchain throughput. However, existing schemes still face challenges related to a) the impact of vehicle mobility on blockchain consensus, especially for cross-shard transaction; and b) the strict requirements of low latency consensus in a highly dynamic network. In this paper, we propose a DAG (Directed Acyclic Graph) consensus leveraging Robust Dynamic Sharding and Tree-broadcasting (DRDST) to address these challenges. Specifically, we first develop a standard for evaluating the network stability of nodes, combined with the nodes' trust values, to propose a novel robust sharding model that is solved through the design of the Genetic Sharding Algorithm (GSA). Then, we optimize the broadcast latency of the whole sharded network by improving the tree-broadcasting to minimize the maximum broadcast latency within each shard. On this basis, we also design a DAG consensus scheme based on an improved hashgraph protocol, which can efficiently handle crossshard transactions. Finally, the simulation proves the proposed scheme is superior to the comparison schemes in latency, throughput, consensus success rate, and node traffic load.

Leveraging ubiquitous WiFi infrastructures, intrusion detection methods based on Received Signal Strength (RSS) offer compelling advantages, including cost-effectiveness and privacy protection. However, existing RSS-based intrusion detection solutions fall short of accurately estimating and extending the WiFi sensing bound. In this paper, we propose a novel model of motion-disturbed RSS and design an effective R-ratio indicator to extend the intrusion detection bound. Specifically, we first establish a general model of motion-disturbed RSS and derive the blocked area and reflection area in this RSS model. Then, we define the WiFi intrusion detection bound and propose a performance indicator called R-ratio to extend the bound with RSS. Furthermore, based on the statistical properties of noise, we design an efficient filter to further weaken the noise. We also propose two new methods to further extend intrusion detection bound. Extensive experimental results demonstrate that the proposed power sum ratio based intrusion detection method can approximately double the WiFi intrusion detection bound compared to other methods with raw RSS data, and our developed motion-disturbed RSS model can provide valuable insights and guidance to the intrusion detection system.

Through conducting equivalent model training within the paradigm of edge intelligence, the Digital Twin Edge Networks (DITEN) have been widely employed in the Industrial Internet-of-Things (IIoT) to facilitate the cost-effective execution without the operational disruption. However, due to the insufficient consideration of heterogeneity in computing and communication capabilities of distinct industrial terminals in the Digital Twin (DT) model training, the existing approaches of DT construction/update have unbalanced model training cost and loss in the whole life cycle of DT model, hindering the abilities of quick responding to complex and dynamic productions and ensuring the data consistency of virtual-real space. To address this issue, we define a global loss minimization problem with constraint, and propose an original approach of semi-asynchronous federated learning, named EDT-SaFL, as a promising solution. Considering the collaborative utilization of heterogeneous resources, and the contribution of local data quantity and quality to the global model update, the EDT-SaFL consists of three important operations,Terminal Selection for Model Training,Self-Adaptation of Local Training Iterations, andSemi-asynchronous Global Aggregation. With the analysis of convergence, complexity and communication overhead, the experiments have evidently demonstrated the superiority of EDT-SaFL on the datasets of CIFAR-10 and Industrial-Equipment.

Mobile devices (MDs) are increasingly incorporating deep neural network (DNN) inference into their systems due to the rapid growth of intelligent applications. Mobile edge computing-based distributed DNN collaborative inference has gained popularity due to limited on-device computation and energy budgets. However, the resource competition among MDs, along with the coupling of collaborative inference tasks across MDs and servers, creates significant challenges for efficient resource management. This issue is further exacerbated by the complexity of directed acyclic graph (DAG)-structured DNNs. Most prior studies do not jointly address the dual challenges of partitioning complex-structured DNNs and leveraging advanced optimization for collaborative inference, and their resilience to channel condition fluctuations remains underexplored. To address these challenges, we propose a novel task-aware collaborative inference framework. First, we devise a fine-grained partitioning point search algorithm based on a bidirectional graph linked list, which enables one-dimensional and flexible partitioning of DAG-structured DNNs. We then reformulate the problem of minimizing collaborative inference energy consumption and latency as a task-aware Markov decision process (MDP), which partitions each user's inference task queue into consecutive task windows for resource allocation. Building on this, we propose an Embedded Multi-Agent Hybrid Proximal Policy Optimization (EMH-PPO) algorithm to learn effective policies. Extensive experiments conducted across diverse network scenarios reveal that, compared to local DNN inference on MDs, our proposed method reduces inference latency by up to 64% and energy consumption by up to 46%.

In Wireless Sensor Networks (WSNs), relay sensor nodes can aggregate data from edge sensor node into a summary information before sending to the sink. Due to the vast number of sensor nodes in a distributed edge computing (DEC) network, these relay sensor nodes may receive a high number of aggregation requests. This increases the chance of conflicting transmissions, which further leads to unwanted latency. Designing a conflict-free and minimal latency data aggregation schedule remains an open question. Moreover, existing related works have been conducted in traditional WSNs. By leveraging multiple antennas, the Multiple Input Multiple Output (MIMO) and cooperative MIMO called virtual MIMO (V-MIMO) enable broadband wireless communication, thereby improving the performance of WSNs. However, compared with traditional WSNs, MIMO and V-MIMO introduce distinct interference models requiring careful consideration. The work proposes a solution to an NP-hard problem, addressing three challenges: (i) interference; (ii) latency; and (iii) dynamic changes in network topology. Firstly, to counter interference, we propose a model where multiple nodes can simultaneously send data to the same parent by connecting different antennas. Secondly, to minimize latency, we propose a novel distributed heuristic data aggregation scheduling method, which intertwines the construction of an optimal data aggregation tree and conflict-free scheduling. Finally, to handle dynamic network topology changes, we propose lightweight adaptive strategies that do not increase data aggregation latency. Simulation results and theoretical analysis demonstrate superior performance in reducing data aggregation latency. When compared with state-of-the-art solutions, our proposed method decreases data aggregation latency by at least 2.6× on average.

This work presents a comprehensive evaluation of neural network graph compilers across heterogeneous hardware platforms, addressing the critical gap between theoretical optimization techniques and practical deployment scenarios. We demonstrate how vendor-specific optimizations can invalidate relative performance comparisons between architectural archetypes, with performance advantages sometimes completely reversing after compilation. Our systematic analysis reveals that graph compilers exhibit performance patterns highly dependent on both neural architecture and batch sizes. Through fine-grained block-level experimentation, we establish that vendor-specific compilers can leverage repeated patterns in simple architectures, yielding disproportionate throughput gains as model depth increases. We introduce novel metrics to quantify a compiler's ability to mitigate performance friction as batch size increases. Our methodology bridges the gap between academic research and practical deployment by incorporating compiler effects throughout the research process, providing actionable insights for practitioners navigating complex optimization landscapes across heterogeneous hardware environments.

The exponential growth of data generated at the network edge has driven a paradigm shift from centralized cloud computing to local edge processing, accelerating the widespread adoption of edge computing across diverse applications. In this context, continual test-time adaptation (CTTA) on edge devices, which enables models to adapt to evolving target domains without access to source data or labeled samples, has become an emerging research focus due to its practical importance in dynamic environments with changing data distributions. However, limited computational and memory resources severely restrict CTTA on edge devices. Moreover, since adaptation relies on noisy unsupervised losses without access to labels, prolonged CTTA can lead to error accumulation. Additionally, the model is susceptible to catastrophic forgetting, an intrinsic challenge in continual adaptation. In this paper, we propose MccTTA, a memory efficient collaborative continual test-time adaptation framework for edge devices. Specifically, MccTTA incorporates a generative model to synthesize images as a replacement for replay data on the cloud, and a lightweight side network attached to the frozen original network to reduce memory consumption during edge adaptation. We further introduce 2SR (Two-Stage Rehearsal), which decouples active forgetting and knowledge integration into two separate stages to address the plasticity–stability dilemma caused by distributional discrepancies between synthetic and real task data during continual adaptation. Finally, extensive experiments are conducted to evaluate the effectiveness of MccTTA. The results show that, compared with conventional TTA methods, MccTTA achieves superior accuracy and mitigates forgetting while requiring less memory.

Efficient container image distribution is crucial for enabling machine learning inference at the network edge, where resource limitations and dynamic network conditions create significant challenges. In this paper, we presentPeerSync, a decentralized P2P-based system designed to optimize image distribution in edge environments.PeerSyncemploys a popularity- and network-aware download engine that dynamically adapts to content popularity and real-time network conditions.PeerSyncfurther integrates automated tracker election for rapid peer discovery and dynamic cache management for efficient storage utilization. We implementPeerSyncwith 8000+ lines of Rust code and test its performance extensively on both large-scale Docker-based emulations and physical edge devices. Experimental results show thatPeerSyncdelivers a remarkable speed increase of 2.72×, 1.79×, and 1.28× compared to the Baseline solution, Dragonfly, and Kraken, respectively, while significantly reducing cross-network traffic by 90.72% under congested and varying network conditions.

Federated edge learning (FEEL) greatly facilitates the development of ubiquitous intelligence by combining federated learning and edge computing. However, traditional FEEL implementations assume fixed-sized local datasets, neglecting the potential of edge devices to acquire sensory information actively. Such a simplistic scenario leads to overestimating data availability and underestimating resource utilization in networks with varying resource capacity. Moreover, the existing FEEL-oriented systems with integrated sensing, communication, and computation (ISCC) have separate-based designs, leading to an inefficient use of wireless resources. To alleviate these issues, we propose a novel FEEL-oriented ISCC framework in edge collaborative networks, by leveraging the integrated sensing and communication (ISAC) technique to achieve the dual purpose of data sensing and parameter transmission. Then, over the designed framework, we present FEEL convergence analysis under non-independent and identically distributed (non-iid) and iid data. Correspondingly, we formulate a joint beamforming and flexible time duration optimization problem to maximize the convergence speed of FEEL, subject to limited resources on the devices and requirements for data sensing and communication. To address the problem efficiently, we propose an alternative optimization framework, in which the successive convex approximation (SCA) method is adopted to solve the nonconvex beamforming design subproblem, and a low-complexity method is derived for optimal time allocation. Extensive results reveal that the proposed framework can achieve excellent performance in model training accuracy by efficiently utilizing limited resources in edge collaborative networks, under iid and non-iid data.

Distributed applications now span sensors, edge nodes, fog clusters, and hyperscale clouds.Meeting service-level objectives across this "Distributed Computing Continuum" persistently fails when management is reactive, centralized, and blind to uncertainty.I argue for predictive equilibrium as the control objective and for a concrete diagnostic: the Kullback-Leibler divergence between a system's expected and observed causal behavior under perturbations, each modeled with a Bayesian network.This perspective draws from predictive regulation in neuroscience and the fluctuation-dissipation view of equilibrium in physics, and it sets the stage for antifragilitysystems that get better because they were stressed, not despite it.

The increasing complexity of IoT applications and the continuous growth in data generated by connected devices have led to significant challenges in managing resources and meeting performance requirements in computing continuum architectures. Traditional cloud solutions struggle to handle the dynamic nature of these environments, where both infrastructure demands and data analytics requirements can fluctuate rapidly. As a result, there is a need for more adaptable and intelligent resource management solutions that can respond to these changes in real-time. This paper introduces a framework based on multi-dimensional elasticity, which enables the adaptive management of both infrastructure resources and data analytics requirements. The framework leverages an orchestrator capable of dynamically adjusting architecture resources such as CPU, memory, or bandwidth and modulating data analytics requirements, including coverage, sample, and freshness. The framework has been evaluated, demonstrating the impact of varying data analytics requirements on system performance and the orchestrator's effectiveness in maintaining a balanced and optimized system, ensuring efficient operation across edge and head nodes.

This paper offers a solution to generate concrete goals for automating the fulfillment of user intents in the computing continuum. The computing continuum guarantees a flexible infrastructure for services at the cost of more complex handling. Our proposed method innovates the state of the art, helping build performative automated strategies through the translation of service owner intents into concrete targets. We improve on existing intent-based systems by offering support for multi-domain infrastructures. Furthermore, we go beyond current computing continuum management solutions, offering full automation by generating concrete targets for the continuum of automated agents. We achieve that through a multi-agent system built on Large Language Models (LLMs) that translates high-level business intents into executable Reinforcement Learning (RL) environments. By leveraging infrastructure representations in the form of Knowledge Graphs, the framework identifies which system components require adaptation and estimates the target metric values needed to fulfill the intent. We evaluate it on a realistic use case with promising results. We can deploy a fully working RL agent to manage network and computing resources, achieving a success rate higher than 80% after preliminary training.

The computing continuum aims to break the isolation of edge and cloud computing, creating a smooth and heterogeneous infrastructure surface for deploying applications. However, the continuum is practically fragmented, with infrastructure managed in isolation by various parties in a vertical dimension, i.e., edge, fog, and cloud layers, and horizontally, i.e., computing, network, and storage domains. This scenario negatively impacts the fulfillment of the application objectives. We propose inCoord, an intent-aware solution that enables the creation of a unified computing continuum by coordinating its instances to fulfill application objectives. Each instance represents a cluster of (compute, storage, or network) nodes with their own manager component. Traditionally, systems take low-level actions on these instances. In contrast, inCoord learns their emerging behaviors and adapts their managers’ objectives to fulfill the application’s intents. Here, through a Reinforcement-Learning-based Proof of Concept, we show the potential of this system to understand emerging behavior and manage multi-domain, independently managed instances.

Serverless computing is a cloud computing model that abstracts server management, allowing developers to focus solely on writing code without concerns about the underlying infrastructure. This paradigm shift is transforming application development by reducing time to market, lowering costs, and enhancing scalability. In serverless computing, functions are event-driven and automatically scale in response to events such as data changes or user requests. Despite its advantages, serverless computing presents several research challenges, including managing state for ephemeral functions, mitigating cold start delays, optimizing function composition, debugging, efficient auto-scaling, resource management, and ensuring security and compliance. This special issue focused on addressing these challenges by promoting research on innovative solutions and exploring the potential of serverless computing in new application domains.

Blockchain, as an innovative distributed ledger technology, has attracted considerable attention in recent years from both academic circles and industry sectors. Its applications span a diverse range of domains, including finance and the Internet of Things (IoT). However, the scalability of blockchain technology is still a critical limitation with the increasing volume of data. To address this limitation, a directed acyclic graph (DAG) data structure has been proposed to improve scalability by supporting asynchronous process of transactions. IOTA is a well-known DAG-based blockchain that theoretically offers faster confirmation speeds with an increasing number of transactions. However, in practice, IOTA still faces the challenge of balancing scalability and security. In this article, we propose a scalable and secure transaction attachment algorithm for the DAG-based blockchain IOTA. We determine two critical parameters through our experimental analysis: one for calculating the selection probability and the other for setting the threshold for abnormal transactions. First, we calculate the selection probability of unconfirmed transactions. Then, we select abnormal transactions whose selection probability falls below the predefined threshold to maintain the security. Finally, new transactions attach randomly to former transactions with a time computational complexity$O(n)$, ensuring the scalability. Through experiments comparing the proposed algorithm to the current transaction attaching algorithm, we demonstrate the scalability and security of our proposed algorithm.

This article investigates a joint optimization framework for intelligent reflecting surface (IRS)-enhanced integrated sensing, communication, and powering systems. In this framework, the base station transmits signals for simultaneous radar sensing, as well as multi-user information and power transmissions. We aim at maximizing the minimum harvested power among all users, while satisfying beampattern gain requirements for multi-target sensing and signal-to-interference-plus-noise constraints of users. To tackle this strictly non-convex problem, we employ the block coordinate descent technique to iteratively optimize the transmit beamformer of the base station, the phase shift matrix of the IRS, and the power splitting ratios of users. The semi-definite relaxation method is utilized to obtain the optimal transmit beamformer of the base station, and the tightness of the rank-one relaxation is demonstrated. Furthermore, we develop a penalty function-based algorithm and use successive convex approximation techniques to determine the optimal phase shift matrix of the IRS. Additionally, closed-form expressions are derived for the optimal power splitting ratios. Moreover, by exploiting the Bernstein-type inequality, we further designed the robust beamforming and power splitting scheme for considered systems under stochastic channel estimation errors. Numerical results demonstrate that the proposed IRS-enhanced method outperforms several benchmark methods in terms of the minimum harvested power among all users.

As a cutting-edge technology of low-altitude Artificial Intelligence of Things (AIoT), autonomous aerial vehicle object detection significantly enhances the surveillance services capabilities of low-altitude AIoT. However, the difficulty of object detection is exacerbated by the high proportion of small and obscure objects in the captured images. To address the mentioned challenges, we present an efficient multiband infrared small object detection approach for low-altitude intelligent surveillance services. First, we propose the multiband infrared image fusion algorithm based on cascade-GAN (MIF-CGAN), which produces fused images with high information entropy and high contrast. Then, the Transformer-based multiscale dense small object detection (MsDSOD) algorithm is proposed. The algorithm consists of the global-local object detection (G-LOD) network, the object dense area extraction (O-DAE) module, and the weighted boxes fusion (WBF) module. It extracts small objects features at different scales from infrared images and fuses the global and local detection results to accurately identify small objects in dense scenes. Furthermore, compared to the traditional algorithms, the mean average precision (mAP) of MsDSOD is improved by 0.80% and the average precision in small object detection$({\mathrm { AP}}_{s})$is improved by 0.72%. The proposed algorithm is optimally suited to deal with complex scenes with dense small objects and background occlusion.

Machine learning (ML) typically relies on the assumption that training and testing distributions are identical and that data are centrally stored for training and testing. However, in real-world scenarios, distributions may differ significantly, and data are often distributed across different devices, organizations, or edge nodes. Consequently, it is to develop models capable of effectively generalizing across unseen distributions in data spanning various domains. In response to this challenge, there has been a surge of interest in federated domain generalization (FDG) in recent years. FDG synergizes federated learning (FL) and domain generalization (DG) techniques, facilitating collaborative model development across diverse source domains for effective generalization to unseen domains, all while maintaining data privacy. However, generalizing the federated model under domain shifts remains a complex, underexplored issue. This article provides a comprehensive survey of the latest advancements in this field. Initially, we discuss the development process from traditional ML to domain adaptation (DA) and DG, leading to FDG, as well as provide the corresponding formal definition. Subsequently, we classify recent methodologies into four distinct categories: federated domain alignment (FDAL), data manipulation (DM), learning strategies (LSs), and aggregation optimization (AO), detailing appropriate algorithms for each. We then overview commonly utilized datasets, applications, evaluations, and benchmarks. Conclusively, this survey outlines potential future research directions.

Nanosatellite constellations equipped with sensors capturing large geographic regions provide unprecedented opportunities for Earth observation. As constellation sizes increase, network contention poses a downlink bottleneck. Orbital Edge Computing (OEC) leverages limited onboard compute resources to reduce transfer costs by processing the raw captures at the source. However, current solutions have limited practicability due to reliance on crude filtering methods or over-prioritizing particular downstream tasks. This work presents an OEC-native and task-agnostic feature compression method that preserves prediction performance and partitions high-resolution satellite imagery to maximize throughput. Further, it embeds context and leverages inter-tile dependencies to lower transfer costs with negligible overhead. While the encoding prioritizes features for downstream tasks, we can reliably recover images with competitive scores on quality measures at lower bitrates. We extensively evaluate transfer cost reduction by including the peculiarity of intermittently available network connections in low earth orbit. Finally, we test the feasibility of our system for standardized nanosatellite form factors. We demonstrate that the proposed approach permits downlinking over 100× the data volume without relying on prior information on the downstream tasks.

WiFi based respiratory detection has attracted increasing attentions due to its ubiquity and convenience. In Non-Line-of-Sight (NLoS) scenarios, WiFi signals reflected from human target are blocked by obstacles and become much weaker, thus limiting the sensing range and hindering the practical deployment. The existing best respiratory detection system extended the sensing range by scaling and aligning dynamic components in WiFi signals. However, its dynamic component scaling causes the amplification of noise, while its dynamic component alignment increases computation complexity due to the traversal on all possible rotation angles. To address the above issues, in this paper we first build WiFi sensing range models for respiratory detection in NLoS scenario, find factors that limit the sensing range, and then propose a new respiratory detection system named RaliSense which can further rapidly extend the sensing range in NLoS scenario. The main idea of RaliSense is rapidly aligning dynamic components without amplifying noise, based on change direction vector and CSI ratio sum polarity of dynamic components. The proposed change direction vector is obtained by calculating the direction on which the noisy dynamic components have the maximum variance, and CSI ratio sum polarity is then obtained by summing the dynamic components which have been rotated by the change direction vector. According to the CSI ratio sum polarity, the rotation angle is quickly adjusted for aligning dynamic components. Extensive simulation and experiment results verify the effectiveness of our proposed sensing range models. The results also demonstrate that our proposed system RaliSense can effectively extend sensing range in NLoS scenario, achieving a 22.7% improvement over the best existing work but spending only a quarter of its computation time.

Federated Learning (FL) allows multiple clients to collaboratively train machine learning models without the need to share their local private data. As a result, it can effectively address the issue of data fragmentation. Nevertheless, insufficient evaluation of individual contributions and the lack of protections for both the intellectual property rights (IPR) of models and client privacy can greatly reduce clients' motivations in federated training. To address these challenges, this paper introduces the Traceable Contribution Evaluation and Model Ownership Protection (TraCemop) framework for federated learning, which allows each client to swiftly assess the contributions of others in each round, with integrated support for the traceability of evaluation results. To safeguard the intellectual property of models, a collective watermark is embedded in the global model. Additionally, a secure mechanism for verifying model ownership is also available in case of disputes. Security analysis indicates that TraCemop is capable of resisting data reconstruction attacks as well as various types of model copyright infringements. Finally, we evaluate the proposed framework using two commonly-used datasets, and the experimental results show a significant improvement in the efficiency of contribution evaluation compared to existing methods. Meanwhile, IPR infringement tests on TraCemop reveal that the proposed framework is resilient against malicious efforts to monopolize model ownership.

As a key technology of intelligent satellite-enabled services in B5G or 6G networks, deploying Deep Neural Networks (DNN) models on satellites has been a notable trend, catering to the daily demand for extensive computing-intensive and latency-sensitive tasks. The computing resources are strategically deployed on satellites where sensor data is generated or collected, facilitating the fine-grained computational inference of DNN-based tasks. However, no prior study has comprehensively explored the crucial inference challenges – e.g., the trade-off between the number of tasks completed and accuracy and partitioning models in multi-exit models – in the resource-constrained space environment. Effective scheduling frameworks cater to various streams of inference tasks are scarce because inference performance may deviate from the ideal situation due to changes in task system status, such as task profiles and network state. To this end, we first formulate a gain-aware in-orbit computing inference problem to strike a proper trade-off between inference latency and the number of tasks completed by dynamically selecting optimal early exit points and model partitioning points. We propose an offline dynamic programming-based algorithm that provides an effective solution when comprehensive system details are to be predicted. We have developed an online learning-based method to schedule inference tasks with uncertain and dynamic system statuses in real-world situations. Our evaluation shows that, compared to baseline methods, the online learning-based algorithm can improve task gain by an average of 87.3% across various tasks.

Federated learning stands out as a promising approach within the domain of edge computing, providing a framework for collaborative training on distributed datasets without necessitating data sharing. However, federated learning involves the frequent transmission of machine learning model updates between the server and clients, resulting in high communication costs. Additionally, heterogeneous clients can further complicate the Federated Learning process and deteriorate performance. To address these challenges, we propose Adaptive Self-Knowledge Distillation-based Quality- and Reputation-Aware Cross-Device Federated Learning (ASDQR) - an efficient communication and inference framework designed for heterogeneous clients. ASDQR initiates the process by selecting high-reputation and high-quality clients to be involved in federated learning, significantly impacting communication efficiency and inference effectiveness. ASDQR also introduces a model of adaptive local self-knowledge distillation that incorporates multiple local personalized historical knowledge for more accurate inference, allowing the historical level to be dynamically adjusted across time. Finally, we present an inference-effective aggregation scheme that assigns higher weights to important and reliable local model updates based on clients’ contribution degrees when performing global model aggregation. ASDQR consistently outperforms baseline methods across all datasets and communication rounds, achieving 9.0% higher accuracy than FedAvg, 6.59% higher than MOON, 0.29% higher than FedProx, 0.2% higher than PFedSD, and 0.08% higher than FedMD on the MNIST dataset at 100 communication rounds. Similar improvements are observed on CIFAR, HAR, and WISDM datasets, demonstrating the robustness and efficiency of ASDQR in federated learning with non-IID data.

Mobile edge computing (MEC) pushes cloud computing capabilities to the network edge, which provides real-time processing and caching flexibility for service-based applications. Conventionally, the individual node solution is insufficient to tackle the increasing computation workload and provide diverse services, especially for unpredictable spatiotemporal service request patterns. To address this, we first propose a hierarchical collaborative computing (HCC) framework to serve users’ demands by reaping sufficient computing capability in Cloud, ubiquitous service area in edge layer, and idle resources in device layer. To better unleash the benefits of HCC and pursue long-term performance, we investigate heterogeneity-aware resource management by collaborative service placement, task scheduling, and resource allocation both in-node and cross-node. We then propose an online optimization framework that first decouples the decisions across different slots. For each instant mixed integer non-linear programming problem, we introduce the surrogate Lagrangian relaxation method to reduce complexity and design hybrid numerical techniques to solve the subproblems. Theoretical analysis and extensive simulation results demonstrate the efficiency of the HCC framework in decreasing system cost on devices, and our proposed algorithms can effectively utilize the resources in the collaborative space to achieve the trade-off between system cost minimization and service placement cost stability.

Digital service networks often face the challenge ofService-OrientedFairness (SOF), where service nodes with varying levels of activity may receive unequal treatment. This article takes the recommendation service system as a representative case to explore and mitigate the impact of SOF. The SOF issue in the recommendation service system can be abstracted asUser-OrientedFairness (UOF), where service models often exhibit bias toward a small group of users, resulting in significant unfairness in the quality of recommendations. Existing research on UOF faces three major limitations, and no single approach effectively addresses all of them.Limitation 1:Post-processing methods fail to address the root cause of the UOF issue.Limitation 2:Some in-processing methods rely heavily on unstable user similarity calculations under severe data sparsity problems.Limitation 3:Other in-processing methods overlook the disparate treatment of individual users within user groups. In this article, we propose a novelIndividualReweighting forUser-OrientedFairness framework, namely IR-UOF, to address all the aforementioned limitations. The motivation behind IR-UOF is tointroduce an in-processing strategy that addresses the UOF issue at the individual level without the need to explore user similarities.We first conduct extensive experiments on three real-world recommendation service datasets using four backbone recommendation models to demonstrate the effectiveness of IR-UOF in mitigating UOF and improving recommendation fairness. Furthermore, we select two general digital service datasets to prove that IR-UOF can be extended to tackle the general SOF issue in other types of digital service networks. In summary, the IR-UOF framework achieves optimal model performance across all datasets, while improving fairness by at least 3.8% in recommendation systems and 24.7% in general service systems.

The rapid expansion of Low Earth Orbit (LEO) satellite constellations presents immense potential for in-orbit services. However, the large-scale and dynamic nature of LEO constellations creates unstable communication environments, where traditional methods struggle to ensure the efficiency and stability of onboard inference services. This highlights the need for an advanced knowledge service framework capable of both inference and root cause analysis of service disruptions. To address this, we propose a novel knowledge service framework that integrates data-driven and knowledge-driven models through satellite-ground collaboration. The framework leverages lightweight onboard models for real-time data processing and ground-based knowledge graphs for advanced inference and cause analysis. To further enhance stability within complex interconnected onboard systems, we propose a prediction-based algorithm for LEO satellite networks that uses joint spatio-temporal modeling to achieve accurate link prediction. Additionally, we formulate an optimization problem aimed at minimizing path distance variance and maximizing path stability across LEO topologies, and we propose a heuristic path selection strategy to ensure efficient inter-satellite routing. Extensive in-orbit deployments and simulation experiments demonstrate the feasibility and effectiveness of the proposed framework. Satellite-ground verification on the BUPT-1 satellite shows its ability to provide real-time services, while inter-satellite simulations using real constellation data indicate significant improvements in response latency and path stability. Compared with baseline methods, our proposed method significantly reduces path jitter by up to 62.6% and improves path availability by up to 17.3% across various LEO constellations.

Artificial Intelligence of Things (AIoT) utilizes a combination of computing, storage, and networking resources to provide highly reliable and low-latency information services to the industrial production processes. However, with the increasing integration of numerous smart terminals into real-time sensing, autonomous decision-making, and precision manufacturing execution systems, the current task scheduling pattern appears to be insufficient to meet the latency requirements of computationally intensive tasks. To address the above challenge, this paper presents a collaborative edge-terminal task offloading scheme. First, the Task Backlog and Multi-slot Scheduling (TBMS) problem is converted from a long-term offloading problem to a single timeslot scheduling problem by Lyapunov optimization. Then, to simplify the problem, the single timeslot problem is decomposed into three subproblems: the local resource allocation problem, the server resource allocation problem, and the indicator weight selection problem. The two resource allocation problems are proved to be convex, which have been solved by using the Bisection method and the Karush-Kuhn-Tucker (KKT) method, respectively. For the indicator weight selection problem, we proposed the enhanced jumping spider optimization algorithm that integrates the elite opposition-based learning strategy. Extensive experiments show that the proposed algorithm can alleviate the computing pressure of the terminal device. Compared with the traditional methods, the offload system cost is effectively reduced by at least 58.8% and the average execution success rate is increased by at least 6%.

The rapid development of satellite technology has significantly enhanced satellite computing service capabilities, particularly in terms of its application potential for complex tasks such as hyperspectral image (HSI) processing. Satellite edge computing (SEC) substantially improves processing efficiency by transferring task processing to the satellite. At the same time, intelligent reflective surfaces (IRS) reduce the pressure on ground service center communication resources by optimizing communication links between satellites on the ground. However, existing works mainly optimize general computing tasks, resulting in limited performance when processing HSI tasks. This paper proposes an IRS-assisted HSI processing SEC system to achieve the optimal balance between HSI processing accuracy and system energy consumption. We formulate an optimization problem as a joint task covering HSI offloading, band selection, and IRS phase shift optimization to achieve optimal overall performance. To address the problem, we propose the joint feature iterative optimization (JFIO) framework for HSI processing, which generates optimized task offloading solutions through graph attention networks, utilizes multi-feature attention capsule networks to achieve efficient band selection, and combines this with IRS modules to optimize communication link conditions. Extensive experiments on various datasets demonstrate that the proposed framework achieves an excellent balance between accuracy and energy consumption, with its performance significantly outperforming other baseline methods.

Computing ecosystems are shifting toward a computing continuum paradigm designed to handle the diverse and dynamic nature of computing resources spread across various locations. It demonstrates significant potential in providing high-bandwidth and low-latency services for users. However, as a large number of users request services from distributed computing continuum systems, it is critical to schedule numerous delay-sensitive, fractional workloads and maximum parallelism-bound jobs to appropriate backend resources,e.g., cloud container instances. In addition, the scheduling strategy also needs to maximize the social welfare that incorporates the utilities of jobs and the revenue of service providers. However, current workload scheduling algorithms are based on simple heuristics and lack performance guarantees. Due to the unpredictability of online requests, the distribution of requests should not be assumed. Therefore, designing an online workload scheduling strategy without assumptions on request distributions is essential for balancing the online workload. This work first establishes a spatiotemporal integrated resource pool to reflect the computational resources provided by distributed computing continuum systems. Then, several pseudo-social welfare functions and marginal cost functions are constructed, where the latter is used to estimate the marginal cost of provisioning services to each newly arrived job based on the current resource surplus. We propose an online workload scheduling strategy namedOnSocMaxto solve the above problems. It operates by following the solutions to several convex pseudo-social welfare maximization problems and is proven to be$\alpha$-competitive for some$\alpha$with a value of at least 2. The evaluation results demonstrate thatOnSocMaxoutperforms several benchmark strategies in maximizing social welfare.

Modularized plant factories, characterized by machines executing intelligent control requests to automatically take care of crops, have emerged as a sustainable agricultural paradigm, garnering the attention of Internet-of-Things and agricultural researchers for their production stability and energy efficiency. However, the diversity and pluralism of the plant factory components make it difficult to cooperate and produce crops with better qualities. Therefore, appropriate resource allocation and task scheduling strategies become the key points to optimize the quality of production in the factories by immediately telling which component is more suitable to do what in taking care of the crops. To address this challenge, this paper investigates how the machines of the factory can use their unique services and resource to help improve the crops’ quality and model the machine cooperation as an online decision-making problem. An$\alpha$-competitive approach called$\textsc {OnATS}$is designed based on the transformation of the original problem, and the experiments show that the proposed algorithm is superior to the baselines. Additionally, this paper explores the impact of different system configurations on the proposed method and shows that the proposed approach has broad applicability.

Serverless Edge Computing is growing in popularity, and while commercial providers are starting to offer edge-oriented products, much research is still being done on orches-trating functions (e.g., autoscaling). These approaches range from threshold- to AI-based strategies and support various Service Level Objectives (SLOs), such as Round-Trip-Time (RTT) and re-source usage. However, the Quality of Service (Qo$S$) continuously deteriorates due to the dynamic edge-cloud continuum and static parameterization of orchestration strategy parameters. Platforms must adapt the orchestration parameters during runtime to counteract this drift that causes SLO violations. To this end, we introduce the Orchestration Parameter Optimization Prob-lem (OPOP), which aims to find parameters for orchestration strategies to minimize SLO violations. We propose a novel self-adaptive Simulation-based Scaling (SimuScale) approach that uses co-simulation to solve OPOP for autoscalers during runtime. SimuScale uses live monitoring data to feed the simulation and perform parameter optimization. Our Proof of Concept is inte-grated with Kubernetes and evaluated on a real-world edge-cloud testbed. While this work focuses on a threshold-based autoscaler, it can be extended to optimize other orchestration components (e.g., schedulers). Our experimental results show that SimuScale finds parameters that decrease RTT SLO violations between 15% and 40%. SimuScale also can reduce resource usage by 29.87% while maintaining the target 95th RTT percentile. Moreover, it can reduce variance caused by different request patterns, making orchestration strategies more resilient in realistic scenarios.

Reducing the energy consumption of data centers is critical to meeting international climate goals and lowering operation costs. Container orchestration platforms can help counteract this trend by optimally placing applications across the infrastructure to increase resource utilization and reduce energy consumption. But platforms in use today are still energy-agnostic and do not offer any insights into energy consumption. In this paper, we present a monitoring framework and a new modeling approach for resource usage in data centers. The model captures heterogeneous hardware and software and acts as input for a Graph Neural Network (GNN) to predict power consumption. Based on this model, we derive a set of container scheduling algorithms that opportunistically schedule applications based on the estimated energy impact of incoming containers. Our results show that the GNN-based prediction model is very accurate and achieves an average RMSE (Root Mean Square Error) of 7.5%. We have implemented a custom scheduler to demonstrate the benefits of using our prediction, and our scheduler can decrease energy consumption on average by 6.2% without any code changes for the application and without increasing workload completion time compared to the default Kubernetes scheduler.

As a driving force in the advancement of intel-ligent in-orbit applications, DNN models have been gradually integrated into satellites, producing daily latency-constraint and computation-intensive tasks. However, the substantial computation capability of DNN models, coupled with the instability of the satellite-ground link, pose significant challenges, hindering the timely completion of tasks. It becomes necessary to adapt to task stream changes when dealing with tasks requiring latency guarantees, such as dynamic observation tasks on the satellites. To this end, we consider a system model for a collaborative inference system with latency constraints, leveraging the multi-exit and model partition technology. To address this, we propose an algorithm, which is tailored to effectively address the trade-off between task completion and maintaining satisfactory task accuracy by dynamically choosing early-exit and partition points. Simulation evaluations show that our proposed algorithm signif-icantly outperforms baseline algorithms across the task stream with strict latency constraints.

With the rapid proliferation of large Low Earth Orbit (LEO) satellite constellations, a huge amount of in-orbit data is generated and needs to be transmitted to the ground for processing. However, traditional LEO satellite constellations, which downlink raw data to the ground, are significantly restricted in transmission capability. Orbital edge computing (OEC), which exploits the computation capacities of LEO satellites and processes the raw data in orbit, is envisioned as a promising solution to relieve the downlink burden. Yet, with OEC, the bottleneck is shifted to the inelastic computation capacities. The computational bottleneck arises from two primary challenges that existing satellite systems have not adequately addressed: the inability to process all captured images and the limited energy supply available for satellite operations. In this work, we seek to fully exploit the scarce satellite computation and communication resources to achieve satellite-ground collaboration and present a satellite-ground collaborative system named TargetFuse for onboard object detection. TargetFuse incorporates a combination of techniques to minimize detection errors under energy and bandwidth constraints. Extensive experiments show that TargetFuse can reduce detection errors by 3.4× on average, compared to onboard computing. TargetFuse achieves a 9.6× improvement in bandwidth efficiency compared to the vanilla baseline under the limited bandwidth budget constraint.

Hierarchical federated learning (HFL) designs introduce intermediate aggregator nodes between clients and the global federated learning server in order to reduce communication costs and distribute server load. One side effect is that machine learning model replication at scale comes “for free” as part of the HFL process: model replicas are hosted at the client end, intermediate nodes, and the global server level and are readily available for serving inference requests. This creates opportunities for efficient model serving but simultaneously couples the training and serving processes and calls for their joint orchestration. This is particularly important for continual learning, where serving a model while (re)training it periodically, upon specific triggers, or continuously, takes place over shared infrastructure spanning the computing continuum. Consequently, training and inference workloads can interfere with detrimental effects on performance. To address this issue, we propose an inference load-aware HFL orchestration scheme, which makes informed decisions on HFL configuration, considering knowledge about inference workloads and the respective processing capacity. Applying our scheme to a continual learning use case in the transportation domain, we demonstrate that by optimizing aggregator node placement and device-aggregator association, significant inference latency savings can be achieved while communication costs are drastically reduced compared to flat centralized federated learning.

Computing Continuum (CC) systems are challenged to ensure the intricate requirements of each computational tier. Given the system’s scale, the Service Level Objectives (SLOs), which are expressed as these requirements, must be disaggregated into smaller parts that can be decentralized. We present our framework for collaborative edge intelligence, enabling individual edge devices to (1) develop a causal understanding of how to enforce their SLOs and (2) transfer knowledge to speed up the onboarding of heterogeneous devices. Through collaboration, they (3) increase the scope of SLO fulfillment. We implemented the framework and evaluated a use case in which a CC system is responsible for ensuring Quality of Service (QoS) and Quality of Experience (QoE) during video streaming. Our results showed that edge devices required only ten training rounds to ensure four SLOs; furthermore, the underlying causal structures were also rationally explainable. The addition of new types of devices can be done a posteriori; the framework allowed them to reuse existing models, even though the device type had been unknown. Finally, rebalancing the load within a device cluster allowed individual edge devices to recover their SLO compliance after a network failure from 22% to 89%.

Self-service libraries need self-service book collection and monitoring of book quality to improve user experience This article proposes a privacy-preserving alternative RFbook, a book classification and moisture sensing system formed from an array of passive commercial RFID tags. We have three key observations in designing RFbook for such benefits. The first observation is that when tags are in the vicinity, their interrogation currents can alter each other’s circuit properties, based on which unique phase and amplitude signatures can be obtained from the backscattered signal. The second observation is that books with different thicknesses and sizes of material will have different signal features. Finally, we found that changes in book humidity are reflected in the reader’s received signal strength (RSS). To turn the high-level idea into a practical system, we built a prototype of RFbook and conducted comprehensive experiments to evaluate the system’s performance. The experimental results show that RFbook can distinguish different types of books with an average accuracy rate higher than 96% and monitor the humidity change of the book.

Accurate head movement tracking is crucial for virtual reality and Metaverse in ubiquitous human-computer interaction (HCI) applications. Existing works for head tracking with wearable VR kits and wireless signals require expensive devices and heavy algorithmic processing. To resolve this problem, we propose HeadTrack, a low-cost, high-precision head motion tracking system that uses commercially available wireless earphones to capture the user’s head motion in real-time. HeadTrack uses smartphones as ‘sound anchors’ and emits inaudible chirps picked up by the user’s wireless earphones. By measuring the time-of-flight of these signals from the smartphone to each microphone on the earphone, we can deduce the user’s face orientation and distance relative to the smartphone, enabling us to accurately track the user’s head movement. To realize HeadTrack, we use the cross-correlation method to optimize the Frequency Modulated Continuous Wave (FMCW) based acoustic ranging method, which solves the problem of insufficient wireless earphone bandwidth. Moreover, we solve the problems of asynchronous startup time between devices and the existence of sampling frequency offset. We conduct excessive experiments in real scenarios, and the results prove that HeadTrack can continuously track the direction of the user’s head, with an average error under 6.3° in pitch and 4.9° in yaw.

The development of cloud computing delivery models inspires the emergence of cloud-native computing. Cloud-native computing, as the most influential development principle for web applications, has already attracted increasingly more attention in both industry and academia. Despite the momentum in the cloud-native industrial community, a clear research roadmap on this topic is still missing. As a contribution to this knowledge, this article surveys key issues during the life cycle of cloud-native applications, from the perspective of services. Specifically, we elaborate on the research domains by decoupling the life cycle of cloud-native applications into four states: building, orchestration, operation, and maintenance. We also discuss the fundamental necessities and summarize the key performance metrics that play critical roles during the development and management of cloud-native applications. We highlight the key implications and limitations of existing works in each state. The challenges, future directions, and research opportunities are also discussed.

Special issue on efficient management of microservice-based systems and applicationsThe advent of microservice architecture marks a transition from conventional monolithic applications to a landscape of loosely linked, lightweight, and autonomous microservice components.The primary objective is to ensure strong environmental uniformity, portability across various operating systems, and robust resource isolation.Leading cloud service providers such as Amazon, Microsoft, Google, and Alibaba have widely embraced microservices within their infrastructures.This adoption is geared toward automating application management and optimizing system performance.Consequently, addressing the automation of tasks like deployment, maintenance, auto-scaling, and networking of microservices becomes pivotal.This underscores the importance of efficient management of systems and applications built on microservices as a critical research challenge.Efficient management methods must not only ensure the quality of service (QoS) across multiple microservices units (containers) but also provide greater control over individual components.However, the dynamic and varied nature of microservice applications and environments significantly amplifies the complexity of these management approaches.Each microservice unit can be deployed and operated independently, catering to distinct functionalities and business objectives.Furthermore, microservices can interact and combine through lightweight communication techniques to form a complete application.The expanding scale of microservice-based systems and their intricate interdependencies pose challenges in terms of load distribution and resource management at the infrastructure level.Furthermore, as cloud workloads surge in resource demands, bandwidth consumption, and QoS requirements, the traditional cloud computing environment extends to fog and edge infrastructures that are in close proximity to end users.As a result, current microservice management approaches need further enhancement to address the mounting resource diversity, application distribution, workload profiles, security prerequisites, and scalability demands across hybrid cloud infrastructures.Keeping this in mind, this special issue addressed some of the aspects related to efficient management of microservice-based systems and applications with the focus on various challenges faced, and promising solutions to address such challenges by using software engineering, machine learning and deep learning techniques.We have received 21 submissions in this issue, and we accepted six high-quality submissions for publication after a rigorous review process with at least three reviewers for each paper.The authors are from diverse countries, including the USA, China, UK, Germany, India, Brazil, etc.Each of the accepted papers is summarized as follows.In the first article, Batista et al. 1 presented two strategies for handling asynchronous workloads associated with tax integration in a multi-tenant microservice architecture specific to the company's context.The initial approach utilizes polling, employing a queue as a distributed lock.The second approach, named the single active consumer, adopts a push-based technique, leveraging the message broker's logic for message delivery.These methodologies are designed to optimize resource allocation in scenarios involving an increasing number of container replicas and tenants.In the second article, Kumar et al. 2 introduced a resource allocation model designed to enhance the QoS in microservice deployment.Utilizing a Fine-tuned Sunflower Whale Optimization Algorithm, the model strategically deploys container-based services on physical machines, optimizing their execution capacity by efficiently utilizing CPU and memory resources.The primary objective of this proposed technique is to achieve an efficient distribution of workload, preventing resource wastage and ultimately enhancing QoS parameters.In the third paper, Zhu et al. 3 introduced RADF, a semi-automatic approach for decomposing a monolith into serverless functions by analyzing the inherent business logic present in the application's interface.The proposed method employs a two-stage refactoring strategy, initially performing a coarse-grained decomposition followed by a fine-grained one.This approach streamlines the decomposition process into smaller, more manageable steps, providing adaptability to generate a solution at either the microservice or function level.In the fourth paper, Würz et al. 4 identified the principal tasks and subtasks of the application for the purpose of partitioning.Subsequently, they outlined the program flow to ascertain which application tasks could be transformed into functions and elucidated their interdependencies.In the concluding step, they precisely specified individual functions

Trajectory recovery aims to restore missing data for reconstructing high-quality human mobility trajectory, which benefits a wide range of intelligent transportation system applications ranging from urban planning to travel recommendation. Inspired by the inherent regularity of human mobility, existing approaches capture spatial-temporal transition regularities in historical trajectory for data recovery. Although promising, existing solutions suffer from two limitations.i)These methods fail to recover occasionally-visited points (OVP) due to the lack of semantic information when learning spatial-temporal transition regularities.ii)The information before and after missing data is not be fully utilized for trajectory recovery. To overcome the limitations, we propose a novel semantic-aware trajectory recovery framework. First, we leverage heterogeneous information network (HIN) to encode various semantic correlations for obtaining rich semantic embeddings, which are fused with temporal information to form spatial-temporal semantic context. Then, we develop a behavior attention mechanism to capture semantic behavior transition regularities for trajectory recovery based on the bidirectional spatial-temporal semantic context before and after missing data. Extensive experiments on four real-world datasets show that our proposed method outperforms the state-of-the-arts by 7%-11% in term of recall, F1-score and mean average precision.

The rise of mobile AI accelerators allows latency-sensitive applications to execute lightweight Deep Neural Networks (DNNs) on the client side. However, critical applications require powerful models that edge devices cannot host and must therefore offload requests, where the high-dimensional data will compete for limited bandwidth. Split Computing (SC) alleviates resource inefficiency by partitioning DNN layers across devices, but current methods are overly specific and only marginally reduce bandwidth consumption. This work proposes shifting away from focusing on executing shallow layers of partitioned DNNs. Instead, it advocates concentrating the local resources on variational compression optimized for machine interpretability. We introduce a novel framework for resource-conscious compression models and extensively evaluate our method in an environment reflecting the asymmetric resource distribution between edge devices and servers. Our method achieves 60% lower bitrate than a state-of-the-art SC method without decreasing accuracy and is up to 16x faster than offloading with existing codec standards.

Head motion tracking is a promising research field with vast applications in ubiquitous human-computer interaction (HCI) scenarios. Unfortunately, solutions based on vision and wireless sensing have shortcomings in user privacy and tracking range, respectively. To address these issues, we propose IA-Track, a novel head motion tracking system that combines inertial measurement units (IMU) and acoustic sensing. Our wireless earphone-based method balances flexibility, computational complexity, and tracking accuracy, requiring only an earphone with an IMU and a smartphone. However, we still face two challenges. First, wireless earphones have limited hardware resources, making acoustic Doppler effect-based method unsuitable for acoustic tracking. Second, traditional Kalman filter-based trajectory restoration methods may introduce significant cumulative errors. To tackle these challenges, we rely on IMU sensor data to recover the trajectory and use smartphones to emit ”inaudible” acoustic signals that the earphone receives to adjust the IMU drift track. We conducted extensive experiments involving 50 volunteers in various potential IA-Track usage scenarios, demonstrating that our well-designed system achieves satisfactory head motion tracking performance.

Passenger counting is crucial for many applications such as vehicle scheduling and traffic capacity assessment. However, most of the existing solutions are either high-cost, privacy invasive or not suitable for passengers the vehicle scenarios. In this work, we propose thePa-Count, an effective real-timePassengerCounting system deployed inside the vehicle via using Wi-FiCSI(Channel State Information). Specifically, in Pa-Count, we design a set of combined filters to eliminate environmental interference and enhance CSI quality. In so doing, we can identify the fluctuation of weak CSI caused by passengers’ subtle movement, i.e., the fidgeting, and then obtain the distribution of fidgeting period and silent period. Following that, we describe the subtle movements of passengers via power law with exponential cutoff distribution and establish a counting model based on the queuing theory. A mathematical inference method with a priori probability is devised to calculate the number of real-time passengers through CSI. We evaluate the performance of the Pa-Count by conducting a set of experiments in real-world vehicle scenarios (including private car and subway). Experimental results show that Pa-Count can achieve robust performance with an average accuracy of over 92$\%$.

Recently, computation offloading methods have greatly improved the Quality of Experience (QoE) in Multi-access Edge Computing (MEC) by offloading tasks to the edge servers. Since well-coordinated actions of Terminal Devices (TDs) are critical to improving the performance of the entire individual system, many practical MEC-based applications, i.e., firefighting robots and unmanned aerial vehicles, require great teamwork among TDs. However, real-world scenarios are usually bound by resource conditions. For instance, network connectivity may weaken or experience interruptions during emergency situations. In cases where the communication medium is utilized by multiple TDs, achieving effective coordination poses a significant challenge. In this paper, we propose a computation offloading scheme based on Scheduled Multi-agent Deep Reinforcement Learning (SMDRL) to make the most efficient decision in a resource-constrained scenario. First, we design a virtual energy queue based on the MEC system and maximize the QoE (related to service delay and energy consumption) in a real-time manner. Subsequently, we propose a scheduled multi-agent deep reinforcement learning algorithm to support each TD in learning how to encode messages, select actions, and schedule itself based on the received messages. Furthermore, a TopK mechanism is introduced. This mechanism chooses the most crucial TDs to broadcast their messages, and then the computation offloading problem in a communication-constrained MEC environment can be solved in a low-communication manner. Also, we prove that even under limited communication conditions, our proposed methods can still lead to the close-to-optimal performance. The final performance analysis shows that the developed scheme has significant advantages over other representative schemes.

Spatial Crowdsourcing (SC) has been an indispensable Location-based Service where the SC server assigns tasks to workers based on the locations of task requesters and workers, raising strong privacy concerns. Limited by the computational and time complexity, existing works prefer differential privacy-based methods to protect location privacy. However, most differential privacy-based works ignore the road network, perturbing locations on two-dimensional plane, resulting in more failures in tasks and moreover extensive privacy disclosure in practice. This paper aims to implement a multi-task assignment with both high utility and efficiency while protecting the location privacy of both task requesters and workers on road networks. Specifically, we design a Road Network-aware Exponential Mechanism and propose an Obfuscated Locations Selection algorithm to guarantee location privacy of all participants and extensive privacy. Then, we propose region distance. Based on this, we further formulate multi-task assignment as a Binary Linear Programming problem and a utility-aware optimization problem. We solve the first problem to obtain optimal efficiency and then propose a utility-aware optimization algorithm for the second problem to improve the utility. Our experiments demonstrate sufficient and stable privacy guarantee and the well-performance on both utility and efficiency of our framework.

Deploying deep learning (DL) on mobile devices has been a notable trend in recent years. To support fast inference of on-device DL, DL libraries play a critical role as algorithms and hardware do. Unfortunately, no prior work ever dives deep into the ecosystem of modern DL libraries and provides quantitative results on their performance. In this paper, we first build a comprehensive benchmark that includes 6 representative DL libraries and 15 diversified DL models. Then we perform extensive experiments on 10 mobile devices, and the results reveal the current landscape of mobile DL libraries. For example, we find that the best-performing DL library is severely fragmented across different models and hardware, and the gap between DL libraries can be rather huge. In fact, the impacts of DL libraries can overwhelm the optimizations from algorithms or hardware, e.g., model quantization and GPU/DSP-based heterogeneous computing. Motivated by the fragmented performance of DL libraries across models and hardware, we propose an effective DL Library selection framework to obtain the optimal library on a new dataset that has been created. We evaluate the DL Library selection algorithm, and the results show that the framework at it can improve the prediction accuracy by about 10% than benchmark approaches on average.

Multi-access edge computing (MEC) is a promising architecture to provide low-latency applications for future Internet of Things (IoT)-based network systems. Together with the increasing scholarly attention on task offloading, the problem of servers’ resource allocation has been widely studied. The limited computational resources of edge servers (ESs) cannot meet the different demands of terminal entities (TEs). This makes it a challenge to efficiently schedule computational tasks on ESs. In this paper, we consider a MEC resource transaction market with multiple ESs and multiple TEs, which are interdependent and mutually influence each other. This paper aims to investigate the dynamic tasks allocation problem between TEs and ESs and to meet the optimal benefits for both parties in MEC system. However, this many-to-many interaction requires resolving several problems, including task allocation, TEs’ selection on ESs and conflicting interests of both parties. A bilateral game framework is applied to tackle the tasks allocation problem by modeling the problem as two noncooperative games: the supplier and customer side games. The existence and uniqueness of the Nash equilibrium in the aforementioned games are proved. Adistributedtaskoutsourcingalgorithm (DTOA) is designed to determine the equilibrium. Our simulation results have demonstrated the superior performance of DTOA in increasing the ESs’ profit and TEs’ payoffs, as well as flattening the peak and off-peak loads.

Utilizing the stable underlying and cloud-native functions of vehicle platoons allows for flexible resource provisioning in environments with limited infrastructure, particularly for dynamic and compute-intensive applications. To maximize this potential, we propose the creation of a trading market to encourage interactions between service supporters (vehicle platoons) and requesters (task vehicles). Current trading decisions based on game and negotiations can lead to unpredicted handover costs and increased communication overhead in dynamic environments. Moreover, existing research tends to overlook a mutually beneficial trading philosophy by focusing on either the service supporters’ profitability or the user experience of resource-restrained requesters. Addressing these issues, we introduce a multi-objective optimization problem to model environmental dynamics and uncertainty, aiming to maximize both platoons’ and task vehicles’ long-term utilities while maintaining a satisfactory service access ratio. To tackle the problem within acceptable time frames, we develop a global-local training architecture, incorporating a hybrid action space and prioritized sampling into a multi-agent reinforcement learning algorithm that utilizes a twin delayed deep deterministic gradient (GL-HPMATD3). This approach facilitates consensus in the trading market on key issues, including service request selection, resource allocation, and trading pricing. Through extensive experimentation and comparison, we demonstrate our mechanism’s superior performance in convergence, service access ratio, player utility, execution latency, and trading pricing relative to several state-of-the-art and baseline methods.

The property of Internet of Things (IoT) applications is their capability to execute tasks through the collaboration of interconnected IoT objects. However, IoT collaborations face significant challenges due to security threats that undermine their reliability. An uncertified task publisher may deceive IoT devices into executing illegal tasks, while malicious attackers may intercept and modify transmitted data. Existing works on IoT trusted management issues tend to concentrate on individual aspects, such as authentication, privacy protection, and access control. However, trusted management for IoT collaboration is a multifaceted and intricate endeavor that necessitates a comprehensive approach. To fill this gap, we propose a lightweight authentication-driven trusted management framework that includes a novel authentication and key agreement scheme to guarantee the validity of task publishers, with greatly reduced overheads compared to recent works. The framework also incorporates a distributed data storage scheme and a fine-grained access control mechanism. We record the interactive messages on the blockchain to ensure behavior traceability. We evaluate the authentication scheme through comparative experiments and formal security analysis, demonstrating its efficiency and effectiveness. The experimental results of data storage and acquisition in real-world IoT environments indicate that the proposed framework is a feasible solution for reliable IoT collaboration.

Since the introduction of Bitcoin in 2008, blockchain technology has found widespread applications across various domains. While blockchain offers convenience and immense research value, it also raises privacy and security concerns among users and society at large. Notably, numerous studies have demonstrated the vulnerability of blockchain anonymity. Existing solutions based on bloom filters and SGX(Software Guard Extensions) may safeguard users' access patterns but remain susceptible to novel attacks, including protocol-level and side-channel attacks. To address these issues, we propose a Tree-based Oblivious Random Access Protocol (Tree-ORAP) that not only provides access pattern protection in privacy-preserving blockchain systems but also preserves the original blockchain performance. Furthermore, we design a Tree-ORAP State Version Controller to manage state synchronization across nodes in a multi-client blockchain network. We also analyze the system's security and implement a Tree-ORAP prototype, conducting a series of experiments to demonstrate its efficiency and technical feasibility. In summary, our protocol offers enhanced protection for blockchain systems against a wider range of attacks compared to previous methods, all while maintaining superior security performance and equal or better efficiency.

Client-edge-cloud Federated Learning (CEC-FL) is emerging as an increasingly popular FL paradigm, alleviating the performance limitations of conventional cloud-centric Federated Learning (FL) by incorporating edge computing. However, improving training efficiency while retaining model convergence is not easy in CEC-FL. Although controlling aggregation frequency exhibits great promise in improving efficiency by reducing communication overhead, existing works still struggle to simultaneously achieve satisfactory training efficiency and model convergence performance in heterogeneous and dynamic environments. This paper proposes FedAda, a communication-efficient CEC-FL training method that aims to enhance training performance while ensuring model convergence through adaptive aggregation frequency adjustment. To this end, we theoretically analyze the model convergence under aggregation frequency control. Based on this analysis of the relationship between model convergence and aggregation frequencies, we propose an approximation algorithm to calculate aggregation frequencies, considering convergence and aligning with heterogeneous and dynamic node capabilities, ultimately achieving superior convergence accuracy and speed. Simulation results validate the effectiveness and efficiency of FedAda, demonstrating up to 4% improvement in test accuracy, 6.8× shorter training time and 3.3× less communication overhead compared to prior solutions.

Multi-server jobs that request multiple computing resources and hold onto them during their execution dominate modern computing clusters. When allocating the multi-type resources to several co-located multi-server jobs simultaneously in online settings, it is difficult to make the tradeoff between the parallel computation gain and the internal communication overhead, apart from the resource contention between jobs. To study the computation-communication tradeoff, we model the computation gain as the speedup on the job completion time when it is executed in parallelism on multiple computing instances, and fit it with utilities of different concavities. Meanwhile, we take the dominant communication overhead as the penalty to be subtracted. To achieve a better gain-overhead tradeoff, we formulate an cumulative reward maximization program and design an online algorithm, namedOgaSched, to schedule multi-server jobs.OgaSchedallocates the multi-type resources to each arrived job in the ascending direction of the reward gradients. It has several parallel sub-procedures to accelerate its computation, which greatly reduces the complexity. We proved that it has a sublinear regret with general concave rewards. We also conduct extensive trace-driven simulations to validate the performance ofOgaSched. The results demonstrate thatOgaSchedoutperforms widely used heuristics by 11.33%, 7.75%, 13.89%, and 13.44%, respectively.

The complexity inherent in managing cloud computing systems calls for novel solutions that can effectively enforce high-level Service Level Objectives (SLOs) promptly. Unfortunately, most of the current SLO management solutions rely on reactive approaches, i.e., correcting SLO violations only after they have occurred. Further, the few methods that explore predictive techniques to prevent SLO violations focus solely on forecasting low-level system metrics, such as CPU and Memory utilization. Although valid in some cases, these metrics do not necessarily provide clear and actionable insights into application behavior. This paper presents a novel approach that directly predicts high-level SLOs using low-level system metrics. We target this goal by training and optimizing two state-of-the-art neural network models, a Short-Term Long Memory - LSTM, and a Transformer-based model. Our models provide actionable insights into application behavior by establishing proper connections between the evolution of low-level workload-related metrics and the high-level SLOs. We demonstrate our approach to selecting and preparing the data. We show in practice how to optimize LSTM and Transformer by targeting efficiency as a high-level SLO metric and performing a comparative analysis. We show how these models behave when the input workloads come from different distributions. Consequently, we demonstrate their ability to generalize in heterogeneous systems. Finally, we operationalize our two models by integrating them into the Polaris framework we have been developing to enable a performance-driven SLO-native approach to Cloud computing.

The promises of the computing continuum paradigm motivate a paradigm change for Internet-distributed computing systems. Unfortunately, we are still far from being able to develop computing continuum systems. We try to move one step forward in the direction of the computing continuum systems by defining design phases for the interconnection of the application with its underlying infrastructure. We assume that SLOs are critical to that endeavor. Hence, we analyze its usage in the scientific literature. Based on the learnings obtained, we define 9 design phases to provide homogeneity and common behaviors in large-scale, heterogeneous, distributed, and complex systems.

The growing amount of data generated at the edge of the network, e.g., by Internet of Things (IoT) devices, made it indispensable to relocate computational power close to the data source. Meanwhile, data tends to accumulate in chunks and is frequently subject to resource-intensive transformations, such as privacy enforcement. These phenomena, which are summed up as “data gravity” and “data friction”, have an impact on data processing and the overall system. However, whereas cloud centers are able to dynamically adapt services, e.g., by provisioning additional resources, edge devices provide fewer options to react to changing workloads. To retain the option to process data locally, we present the idea of controlling data gravity and friction with Service Level Objectives (SLOs). We introduce Markov SLO Configurations (MSCs) as a novel approach to organizing performance metrics and elasticity strategies. MSCs, in conjunction with our presented architecture, enable the evaluation of SLOs, the context-based selection of elasticity strategy (i.e., corrective measures), and the execution of strategies directly on edge devices. Thus, we lay the foundation for a new generation of SLOs that can operate across multiple elasticity dimensions, e.g., by scaling quality of service (QoS).

In the era of big data and artificial intelligence, distributed machine learning has emerged as a promising solution to address privacy and security concerns while fostering collaboration between multiple parties. However, with the data increased in terms of volume, velocity, veracity and variety, ensuring effective data management and responsible data sharing in these systems remains a challenge. In this paper, we explore the potential solutions and propose a system architecture that incorporates FAIR data principles (Findable, Accessible, Interoperable, and Reusable) to promote effective and secure collaboration in federated learning. A minimum set of metadata schemes tailored for distributed machine learning and a decentralized authentication and authorization mechanism based on self-sovereign identity and policy-based access control architecture are proposed. To demonstrate the effectiveness of the proposed system, we conduct a FAIRness assessment and evaluate the model performance with a federated learning use case. Our work contributes to the development of an efficient, secure, and collaborative data ecosystem, fostering innovation in artificial intelligence and machine learning.

The amalgamation of multiple Edge and Cloud clusters into an Edge-Cloud continuum requires efficient scheduling techniques to cope with high numbers of infrastructure nodes and computing jobs. Since monolithic schedulers typically do not scale well beyond a certain cluster size, distributed scheduling approaches are usually employed to address such scalability issues. Distributed schedulers are often designed for Cloud environments and lack support for the Edge. Conversely, many Edge schedulers focus on single clusters and provide limited support to deal with the scale of the Edge-Cloud continuum. In this paper, we present the Vela Distributed Scheduler, a globally distributed scheduler, which is specifically tailored for the Edge-Cloud continuum. The main contributions of our work include: i) A novel, globally distributed and orchestrator-independent scheduler with a 3-phase scheduling workflow; ii) A two-level, informed sampling mechanism, which reduces latency for globally distributed sampling and leverages job requirements to produce high quality node samples; And iii) a MultiBind mechanism that significantly reduces job evictions and rescheduling due to scheduling conflicts. We implement Vela on top of Kubernetes and evaluate it in a realistic large-scale setup using multiple interconnected, globally distributed, and production-ready MicroK8s clusters with up to 20,000 total simulated nodes. Our results show that Vela’s performance scales linearly with infrastructure size and that it reduces scheduling conflicts by a factor of 10.

IOTA is a recent distributed ledger technology that relies on Directed Acyclic Graph (DAG) for its ledger organization. To improve IOTA mechanisms, the state of the art methodology employs graph analysis and, for that, heavily relies on synthetic graph generation. Herein, the most popular generation method simulates IOTA protocol execution. Although this method produces realistic IOTA ledgers, it requires too much memory and time due to repeated random walks on the DAG. In this paper, we propose an alternative Graph Generation and Refinement (GraGR) algorithm designed to generate realistic IOTA ledgers while strongly relaxing memory and timing constraints. The evaluations show that, compared to the state of the art, GraGR can generate a ledger with the same properties with only half of memory and up to 10 times faster.

Middle ear effusion is a common symptom of otitis media, the reactive physical manifestation of otitis media (OM) in children's middle ear. However, diagnosing MEE for little children at home is troublesome due to their difficulty cooperating and the caregiver's lack of medical knowledge. To this end, we propose EarSonar, a novel acoustic-based MEE diagnostic system. The principle behind EarSonar is that the acoustic absorption effect exists in ear scenarios, and the volume of middle ear fluid can markedly affect the absorbed spectrum energy. By automatically eliminating the impact of potential interference factors and identifying the representative frequency range with the typical reaction of acoustic absorption, EarSonar captures fine-grained signal features on absorbed spectrum energy and models the intrinsic relationship between acoustic absorption and the volume of the filler fluid in the eardrum. On that basis, EarSonar extracts the features of the MEE signal segment and uses k-means clustering to classify middle ear effusion status. We conducted a test on 112 adolescents aged 4–6. We divided the degree of middle ear effusion into three grades. The final average detection accuracy rate exceeds 92%, which is 8 % higher than the previous method. We have implemented a proof-of-concept prototype of EarSonar by building upon earphones embedded with a microphone and speaker. Experimental results demonstrate a feasible and effective way to turn earphones into potential home-use MEE screening tools.

Generative Pre-trained Transformer (GPT) is a state-of-the-art machine learning model capable of generating human-like text through natural language processing (NLP). GPT is trained on massive amounts of text data and uses deep learning techniques to learn patterns and relationships within the data, enabling it to generate coherent and contextually appropriate text. This position paper proposes using GPT technology to generate new process models when/if needed. We introduce ProcessGPT as a new technology that has the potential to enhance decision-making in data-centric and knowledge-intensive processes. ProcessGPT can be designed by training a generative pre-trained transformer model on a large dataset of business process data. This model can then be fine-tuned on specific process domains and trained to generate process flows and make decisions based on context and user input. The model can be integrated with NLP and machine learning techniques to provide insights and recommendations for process improvement. Furthermore, the model can automate repetitive tasks and improve process efficiency while enabling knowledge workers to communicate analysis findings, support evidence, and make decisions. ProcessGPT can revolutionize business process management (BPM) by offering a powerful tool for process automation and improvement. Finally, we demonstrate how ProcessGPT can be a powerful tool for augmenting data engineers in maintaining data ecosystem processes within large bank organizations. Our scenario highlights the potential of this approach to improve efficiency, reduce costs, and enhance the quality of business operations through the automation of data-centric and knowledge-intensive processes. These results underscore the promise of ProcessGPT as a transformative technology for organizations looking to improve their process workflows.

Abstract Modelling the dynamics of a growing financial environment is a complex task that requires domain knowledge, expertise and access to heterogeneous information types. Such information can stem from several sources at different scales, complicating the task of forming a holistic impression of the financial landscape, especially in terms of the economical relationships between firms. Bringing this scattered information into a common context is, therefore, an essential step in the process of obtaining meaningful insights about the state of an economy. In this paper, we present Sabrina 2.0, a Visual Analytics (VA) approach for exploring financial data across different scales, from individual firms up to nation‐wide aggregate data. Our solution is coupled with a pipeline for the generation of firm‐to‐firm financial transaction networks, fusing information about individual firms with sector‐to‐sector transaction data and domain knowledge on macroscopic aspects of the economy. Each network can be created to have multiple instances to compare different scenarios. We collaborated with experts from finance and economy during the development of our VA solution, and evaluated our approach with seven domain experts across industry and academia through a qualitative insight‐based evaluation. The analysis shows how Sabrina 2.0 enables the generation of insights, and how the incorporation of transaction models assists users in their exploration of a national economy.

Voice assistants support contactless smart device control and thus act as a holy grail of human–computer interaction. However, recent studies reveal that an adversary can manipulate devices by vicious voice commands. This security risk is caused by only executing one-time liveness detection and lacking safeguard modules after service activation. Therefore, identifying speaker type (i.e., human articulators or loudspeakers) is critical in protecting voice-driven services during an entire interaction session. In this article, we propose a continuous voice liveness detection approach LiveProbe, leveraging unique energy response patterns in frequency bands induced by distinct voice generation mechanisms. The rationality behind LiveProbe is presented in two aspects: human articulator reshapes initial voices by exquisitely coordinated movements of vocal organs, which act as band-pass filters generating unique energy responses; nevertheless, the internal modules of loudspeakers are position fixed and cannot reproduce this response characteristic. To that end, we first work on voice generation mechanisms behind two-type speakers that cause spectrum differences. Then, we elaborately construct signal processing and deep-learning modules to extract liveness features. Especially, our approach does not interfere with normal voice interaction and need not to carry customized sensors. The experiment presents its effectiveness against potential attacks with a false acceptance rate of 0.51%.

Continuous identity authentication is critical for privacy protection throughout an entire user login session. In this article, we propose a continuous user authentication mechanism, namely, HandPass, which employs the vibration responses from hand biometrics and is passively activated by natural user-device interaction. Hand vibration responses are embedded in the mechanical vibration of a force-bearing body consisting of one mobile device and one user hand. A built-in accelerometer of the device can capture hand-dependent vibration signals. Considering the concealment of vibration generation and the nonreplicability of hand structure, it is difficult for attackers to counterfeit user identity. Moreover, for ensuring the robustness of authentication performance to tapping behavior interference, we construct a data augmentation module jointly leveraging a signal processing and learning-based pipeline. It can generate enough vibration responses representing hand structure biometrics under various behaviors, thereby making HandPass comprehensively understand vibration response variation. We prototype HandPass on smartphones, and extensive experiments demonstrate that HandPass can achieve satisfactory authentication accuracy.

Vehicle-to-vehicle (V2V) computation offloading has emerged as a promising solution to facilitate computing-intensive vehicular task processing, where task vehicles (i.e., TaVs) will be requested to offload computing-intensive tasks to server vehicles (i.e., SeVs) in order to keep task delay low. However, it is challenging for TaVs to obtain the optimal V2V computation offloading decisions (i.e., realizing the minimal task delay) due to the constraints, including: 1) incomplete offloading information; 2) degraded Quality-of-Service (QoS) of SeVs; and 3) privacy leakage risks. In this article, we develop a learning-based V2V computation offloading algorithm enhanced by SeV’s ability & trustfulness awareness to solve these problems. We emphasize that the proposed algorithm learns the offloading performance of candidate SeVs based on history offloading selections, without requiring the complete offloading information in advance. Additionally, both the QoS of SeVs and safe V2V computation offloading are enhanced in the proposed learning-based algorithm. Furthermore, we conduct extensive simulation experiments to validate the proposed algorithm. The results demonstrate that the proposed algorithm reduces the average task delay by 35% and 40%, and at the same time decreases the learning regret by 39% and 41%, compared to the algorithms without SeV’s ability and trustfulness awareness.

IOTA blockchain system is lightweight without heavy proof-of-work mining phases, which is considered a promising service platform of Internet of Things applications. IOTA organizes ledger data in a directed acyclic graph (DAG), called Tangle, rather a chain structure as in traditional blockchains. With arriving messages, IOTA tangle grows in a special way, as multiple messages can be attached to the tangle at different locations in parallel. Hence, the network dynamics of an operational IOTA system would justify a thorough study, which is currently unexplored in the literature. In this article, we present the first theoretical modeling for the evolving IOTA tangle based on stochastic analysis. After analyzing snapshots of the real-world IOTA ledger data, our key finding suggests that IOTA tangle follows a rather atypical double Pareto Lognormal (dPLN) degree distribution. In contrast, typical power-law and exponential distributions do not accurately reflect the fact. For model parameter estimation, we further realize that using generic optimization solvers cannot yield quality fitting results. Thus, we design an alternative algorithm based on expectation-maximization (EM) framework. We evaluate the proposed model and fitting algorithm with official data provided by the IOTA Foundation. Quantitative comparisons confirm the fitting quality of our proposed model and algorithm. The whole analysis reveals a deeper understanding of the internal mechanism of the IOTA network.

Energy consumption for large amounts of delay-sensitive applications brings serious challenges with the continuous development and diversity of Industrial Internet of Things (IIoT) applications in fog networks. In addition, conventional cloud technology cannot adhere to the delay requirement of sensitive IIoT applications due to long-distance data travel. To address this bottleneck, we design a novel energy–delay optimization framework called transmission scheduling and computation offloading (TSCO), while maintaining energy and delay constraints in the fog environment. To achieve this objective, we first present a heuristic-based transmission scheduling strategy to transfer IIoT-generated tasks based on their importance. Moreover, we also introduce a graph-based task-offloading strategy using constrained-restricted mixed linear programming to handle high traffic in rush-hour scenarios. Extensive simulation results illustrate that the proposedTSCOapproach significantly optimizes energy consumption and delay up to 12%–17% during computation and communication over the traditional baseline algorithms.

Multiaccess edge computing (MEC) provides task offloading services to facilitate the integration of idle resources with the network and bring cloud services closer to the end user. By selecting suitable servers and properly managing resources, task offloading can reduce task completion latency while maintaining the Quality of Service (QoS). Prior research, however, has primarily focused on tasks with strict time constraints, ignoring the possibility that tasks with soft constraints may exceed the bound limits and failing to analyze this complex task constraint issue. Furthermore, considering additional constraint features makes convergent optimization algorithms challenging when dealing with such complex and high-dimensional situations. In this article, we propose a new computational offloading decision framework by minimizing the long-term payment of computational tasks with mixed bound constraints. In addition, redundant experiences are gotten rid of before the training of the algorithm. The most advantageous transitions in the experience pool are used for training in order to improve the learning efficiency and convergence speed of the algorithm as well as increase the accuracy of offloading decisions. The findings of our experiments indicate that the method we have presented is capable of achieving fast convergence rates while also reducing sample redundancy.

Cross-silo federated learning (FL) is a privacy-preserving distributed machine learning where organizations acting as clients cooperatively train a global model without uploading their raw local data. Recently, the cross-silo FL in multiaccess edge computing (MEC) is used in increasing industrial applications. Most existing research on cross-silo FL pays attention to the performance aspect, ignoring the incentive mechanism for high-quality client selection and long participation in model training for efficient and stable FL, which has prevented the widespread adoption of cross-silo FL in MEC. In this article, we propose an incentive mechanism with quality-Aware and reputation-Aware based on the infinitely repeated game for cross-silo FL named VARF. VARF selects high-quality and high-reputation edge nodes (ENs) as candidates for model training in the cross-silo FL by a heuristic algorithm and then motivates the selected ENs to actively contribute their resources. VARF also models the long-term behavior of ENs in cross-silo FL as an infinitely repeated game and derives a stable and long-term cooperative strategy for clients while maximizing the amount of local data for model learning in cross-silo FL. Extensive simulations with real-world data sets demonstrate that the performance of VARF is more beneficial than other benchmarks. Meanwhile, experimental results show that cloud platforms (CPs) and ENs eventually form a long and stable cooperative relationship under the trigger strategy.

As a promising approach to extending cloud resources and services, blockchain-enabled Internet of Things (IoT)-based smart grid edge computing has attracted much attention. However, the edge node’s resource-constraint nature makes it difficult to store the entire chain as the sensing IoT data volume increases. To address this issue, we propose an FS scheme, a fast and secure multithreshold trapdoor Chameleon hash scheme which serves as the basis for block substitution at the edge nodes to solve the storage limitation problem. The FS scheme is used to achieve a consensus-based block substitution, which allows$t$-out-of-$n$edge nodes to compute a hash collision collaboratively to reliably substitute a historical block without leaking the randomness$R$. Also, inspired by the rationale of fast polynomial interpolation, we optimize the FS scheme to FS-I to reduce the time complexity from$\mathcal {O}(nt)$to$\mathcal {O}(t{\mathrm{ log}}^{2}t)$. In addition, we further optimize FS-I to FS-II by using a fast Fourier transform (FFT) to dramatically improve the computational efficiency of Lagrange interpolation, which leads to a significant improvement in terms of block substitution performance. Finally, We provide security analysis and evaluate the performance through comprehensive experiments and the results show that FS can achieve up to several magnitudes better than DTTCH. The results also demonstrate that the FS scheme can provide high service quality for large-scale IoT-based smart grid systems.

Heart disease has now become a very common and impactful disease, which can actually be easily avoided if treatment is intervened at an early stage. Thus, daily monitoring of heart health has become increasingly important. Existing mobile heart monitoring systems are mainly based on seismocardiography (SCG) or photoplethysmography (PPG). However, these methods suffer from inconvenience and additional equipment requirements, preventing people from monitoring their hearts in any place at any time. Inspired by our observation of the correlation between pupil size and heart rate variability (HRV), we consider using the pupillary response when a user unlocks his/her phone using facial recognition to infer the user’s HRV during this time, thus enabling heart monitoring. To this end, we propose a computer vision-based mobile HRV monitoring framework-PupilHeart, designed with a mobile terminal and a server side. On the mobile terminal, PupilHeart collects pupil size change information from users when unlocking their phones through the front-facing camera. Then, the raw pupil size data is preprocessed on the server side. Specifically, PupilHeart uses a 1-D convolutional neural network (1-D CNN) to identify time series features associated with HRV. In addition, PupilHeart trains a recurrent neural network (RNN) with three hidden layers to model pupil and HRV. Employing this model, PupilHeart infers users’ HRV to obtain their heart condition each time they unlock their phones. We prototype PupilHeart and conduct both experiments and field studies to fully evaluate effectiveness of PupilHeart by recruiting 60 volunteers. The overall results show that PupilHeart can accurately predict the user’s HRV.

With deepening urbanization and Internet of Vehicles (IoV) applications, the number of private cars has been increasing in recent years. However, because the surging number of private cars is not compatible with limited road resources, private car users have had unsatisfactory commute experiences during their daily travel. In this work, we focus on improving private car users’ commute experience based on an analysis of IoV trajectory data in a privacy-preserving way. Our idea is based on the following observations: 1) the commute experience of private car users is closely related to the departure time and the travel cost and 2) most travel costs are spent on urban hot zones. Motivated by these findings, we propose a novel blockchain-enabled model named Deep Improving Commute Experience (DeepICE) to improve private car users’ commute experience by predicting when to depart and when to arrive. In this model, a blockchain with a consensus mechanism is developed to address private car user privacy concerns. In addition, we propose a multitask learning-enabled graph convolution network (GCN) method to capture the highly complex features and relations between two tasks, i.e., the departure time and travel cost, and then develop the model to predict these two tasks. The experimental results demonstrate the superior performance of our proposed model compared to existing approaches. Our model can be applied to efficiently enhance private car users’ commute experience.

Abstract This paper presents faas‐sim , a simulation framework tailored to serverless edge computing platforms. In serverless computing, platform operators are tasked with efficiently managing distributed computing infrastructure completely abstracted from application developers. To that end, platform operators and researchers need tools to design, build, and evaluate resource management techniques that efficiently use of infrastructure while optimizing application performance. This challenge is exacerbated in edge computing scenarios, where, compared to cloud computing, there is a lack of reference architectures, design tools, or standardized benchmarks. faas‐sim bridges this gap by providing (a) a generalized model of serverless systems that builds on the function‐as‐a‐service abstraction, (b) a simulator that uses trace data from real‐world edge computing testbeds and representative workloads, and (c) a network topology generator to model and simulate distributed and heterogeneous edge‐cloud systems. We present the conceptual design, implementation, and a thorough evaluation of faas‐sim . By running experiments on both real‐world test beds and replicating them using faas‐sim , we show that the simulator provides accurate results and reasonable simulation performance. We have profiled a wide range of edge computing infrastructure and workloads, focusing on typical edge computing scenarios such as edge AI inference or data processing. Moreover, we present several instances where we have successfully used faas‐sim to either design, optimize, or evaluate serverless edge computing systems.

Decentralized Storage Networks (DSNs) can gather storage resources from mutually untrusted providers and form worldwide decentralized file systems. Compared to traditional storage networks, DSNs are built on top of blockchains, which can incentivize service providers and ensure strong security. However, existing DSNs face two major challenges. First, deduplication can only be achieved at the directory-level. Missing file-level deduplication leads to unavoidable extra storage and bandwidth cost. Second, current DSNs realize file indexing by storing extra metadata while blockchain ledgers are not fully exploited. To overcome these problems, we propose FileDAG, a DSN built on DAG-based blockchain to support file-level deduplication in storing multi-versioned files. When updating files, we adopt an increment generation method to calculate and store only the increments instead of the entire updated files. Besides, we introduce a two-layer DAG-based blockchain ledger, by which FileDAG can provide flexible and storage-saving file indexing by directly using the blockchain database without incurring extra storage overhead. We implement FileDAG and evaluate its performance with extensive experiments. The results demonstrate that FileDAG outperforms the state-of-the-art industrial DSNs considering storage cost and latency.

Fog computing enables the execution of IoT applications on compute nodes which reside both in the cloud and at the edge of the network. To achieve this, most fog computing systems route the IoT data on a path which starts at the data source, and goes through various edge and cloud nodes. Each node on this path may accept the data if there are available resources to process this data locally. Otherwise, the data is forwarded to the next node on path. Notably, when the data is forwarded (rather than accepted), the communication latency increases by the delay to reach the next node. To avoid this, we propose a routing mechanism which maintains a history of all nodes that have accepted data of each context in the past. By processing this history, our mechanism sends the data directly to the closest node that tends to accept data of the same context. This lowers the forwarding by nodes on path, and can reduce the communication latency. We evaluate this approach using both prototype- and simulation-based experiments which show reduced communication latency (by up to 23 percent) and lower number of hops traveled (by up to 73 percent), compared to a state-of-the-art method.

Mobile edge computing (MEC) and device-to-device (D2D) offloading are two promising paradigms in the industrial Internet of Things (IIoT). In this article, we investigate task co-offloading, where computing-intensive industrial tasks can be offloaded to MEC servers via cellular links or nearby IIoT devices via D2D links. This co-offloading delivers small computation delay while avoiding network congestion. However, erratic movements, the selfish nature of devices and incomplete offloading information bring inherent challenges. Motivated by these, we propose a co-offloading framework, integrating migration cost and offloading willingness, in D2D-assisted MEC networks. Then, we investigate a learning-based task co-offloading algorithm, with the goal of minimal system cost (i.e., task delay and migration cost). The proposed algorithm enables IIoT devices to observe and learn the system cost from candidate edge nodes, thereby selecting the optimal edge node without requiring complete offloading information. Furthermore, we conduct simulations to verify the proposed co-offloading algorithm.

In enterprise management systems (EMS), augmented Intelligence of Things (AIoT) devices generate delay-sensitive and energy-intensive tasks for learning analytics, articulate clarifications, and immersive experiences. To guarantee effective task processing, in this work, we present a cloud-assisted fog computing framework with task offloading and service caching. In the framework, tasks make offloading decisions to determine local processing, fog processing, and cloud processing with the goal of minimal task delay and energy consumption, conditioned on dynamic service caching. To this end, we first propose a distributed task offloading algorithm based on noncooperative game theory. Then, we adopt the 0–1 knapsack method to realize dynamic service caching. At last, we adjust the offloading decisions for the tasks offloaded to the fog server but without caching service support. In addition, we conduct extensive experiments and the results validate the effectiveness of our proposed algorithms.

Road hazards (RH) have always been the cause of many serious traffic accidents. These have posed a threat to the safety of drivers, passengers, and pedestrians, and have also resulted in significant losses to people and even to the economies of countries. Hence, road hazards detection (RHD) could play an essential role in intelligent transportation systems (hypertarget ITSITS). The cooperative vehicle-infrastructure systems (CVIS) coordinate the communication between vehicles and roadside infrastructures. Onboard computing devices (OCD), then, make fast analyses and decisions based on road conditions. In this study, an RHD solution based on CVIS is proposed. Firstly, a high-performance heavy action detection model is selected. Using a meta-learning paradigm, critical features are generalized from a few-shot RH data. Secondly, we designed a lightweight RHD model to ensure its smooth inference on an OCD. Thirdly, we use a knowledge distillation (KD) framework to progressively distill the features of the complex model and the privileged information of the data into the lightweight one. Experimental results demonstrate that the model can effectively detect RH and obtain an accuracy of 90.2% with an inference time of 14.7ms.

Urban region heat refers to the extent of which people congregate in various regions when they travel to and stay in a specified place. Predicting urban region heat facilitates broad applications ranging from location-based services to intelligent transportation management. The region heat is essentially characterized by the ‘arrive-stay-leave (ASL)’ behaviors, while it is a challenging task to well capture the spatial-temporal evolution of region heat since the following issues remain: i) ASL behaviors of private cars is usually heterogeneous resulting in a hierarchical distribution of region heat. ii) Urban region heat contains complex spatial-temporal correlations hidden in ASL behaviors and how to collaboratively integrate them is challenging. To address these challenges, we propose a Hierarchical Spatial-Temporal Network (HierSTNet) to forecast urban region heat, which contains two representations, namely, grid region from micro perspective and node region from macro perspective. For the grids, three-dimension spatial and temporal convolutional network (3D-STCNN) is proposed to model multi-scale properties in temporal dimension of ASL behaviors. For the nodes, multi-head graph attention networks are utilized to model the periodicity and spatial heterogeneity among macro region. Hierarchical structures are designed for multi-view modeling spatial-temporal distribution of ASL behaviors, by which they capture small-scale features in micro regions and embeds the global representation into graph propagation. Finally, we design an interaction decoder layer to integrate the external factors and aggregate spatial-temporal information across hierarchical structures. Extensive experiments based on real-world private car trajectory dataset demonstrate the superiority and effectiveness of proposed framework.

The aim of this paper is to present a groundwork on the delay-minimized routing problem in a vehicular ad-hoc network (VANET) where some of the vehicles are equipped with full-duplex (FD) radios. We first give the generalized delay calculation model for a multi-hop path, and prove that the Dijkstra algorithm is unable to get the delay-minimized routing path from source to destination. Then we propose two routing methods: graph-based method and deep reinforcement learning (DRL)-based method. In the graph-based method, the network topology is reformulated as an equivalent graph and then an evolved-Dijkstra algorithm is proposed. In the DRL-based method, the deep Q network (DQN) is employed to learn the shortest end-to-end path, wherein the delay is modeled as the rewards for routing actions. The graph-based method can achieve the exact minimum end-to-end delay, while the DRL-based method is more feasible due to its acceptable complexity. Finally, extensive simulations demonstrate that the DRL-based approach with proper hyper-parameters can achieve near minimum end-to-end delay, and the achieved delay has a notably decline as the number of FD nodes increases.

This article presents our vision on the need of developing new managing technologies to harness distributed “computing continuum” systems. These systems are concurrently executed in multiple computing tiers: Cloud, Fog, Edge and IoT. This simple idea develops manifold challenges due to the inherent complexity inherited from the underlying infrastructures of these systems. This makes inappropriate the use of current methodologies for managing Internet distributed systems, which are based on the early systems that were based on client/server architectures and were completely specified by the application software. We present a new methodology to manage distributed “computing continuum” systems. This is based on a mathematical artifact called Markov Blanket, which sets these systems in a Markovian space, more suitable to cope with their complex characteristics. Furthermore, we develop the concept of equilibrium for these systems, providing a more flexible management framework compared with the one based on thresholds, currently in use for Internet-based distributed systems. Finally, we also link the equilibrium with the development of adaptive mechanisms. However, we are aware that developing the entire methodology requires a big effort and the use of learning techniques, therefore, we finish this article with an overview of the techniques required to develop this methodology.

In device to device (D2D) aided mobile edge computing (MEC) networks, by implementing content caching and D2D links, the edge server and nearby mobile devices can provide task offloading platforms. For parallel tasks, proper decisions on content caching and task offloading help reduce delay and energy consumption. However, what is often ignored in the previous works is the joint optimization of parallel task offloading and content caching. In this paper, we aim to find optimal content caching and parallel task offloading strategies, so as to minimize task delay and energy consumption. The minimization problem is formulated as a multi-objective optimization problem, concerning both content caching and parallel task offloading. The content caching is formulated as an integer knapsack problem (IKP). To solve the IKP problem, an enhanced Binary Particle Swarm Optimization algorithm is proposed. The parallel task offloading problem is formulated as a constrained multi-objective optimization problem, an improved multi-objective bat algorithm is proposed to address the problem. Experimental results show that our algorithm can decrease delay and energy cost by at most 45% and 56%, respectively. In addition, the parallel task offloading ratio remains over 91% even with large number of mobile devices (MDs).

Volumetric video provides a more immersive holographic virtual experience than conventional video services such as 360-degree and virtual reality (VR) videos. However, due to ultra-high bandwidth requirements, existing compression and transmission technology cannot handle the delivery of real-time volumetric video. Unlike traditional compression methods and the approaches that extend 360-degree video streaming, we propose AITransfer, an AI-powered compression and semantic-aware transmission method for point cloud video data (a popular volumetric data format). AITransfer targets the semantic-level communication beyond transmitting raw point cloud video or compressed video with two outstanding contributions: (1) designing an integrated end-to-end architecture with two fundamental contents of feature extraction and reconstruction to reduce the bandwidth consumption and alleviate the computational pressure; and (2) incorporating the dynamic network condition into end-to-end architecture design and employing a deep reinforcement learning-based adaptive control scheme to provide robust transmission. We conduct extensive experiments on the typical datasets and develop a case study to demonstrate the efficiency and effectiveness. The results show that AITransfer can provide extremely efficient point cloud transmission while maintaining considerable user experience with more than 30.72x compression ratio under the existing network environments.

The multi-access edge computing (MEC) paradigm has emerged as a critical solution to address the exponential growth in mobile web services and devices. By implementing an edge-based service provisioning system (EPS) with servers located at the network’s edge, both transmission and computation efficiency can be significantly enhanced. Nevertheless, it is also essential to carefully consider the resource allocation for services, the traffic management of requests, and the path arrangement for data delivery to ensure the cost-effective operation of the EPS. Therefore, we investigate and quantify the relationship between the performance and cost of the EPS in this paper, and model the cost-effective service provisioning problem as a multi-phase convex optimization problem. An online algorithm whose name isRDCbased on the Lyapunov framework is proposed to decompose this problem into several sub-problems.Additionally, a heuristic approach that partitions edge servers into several clusters, calledRDC-NePand based onRDC, has also been proposed to reduce computational complexity. A series of experiments were conducted to evaluate the proposed approach. The results demonstrate thatRDCcan effectively balance expense and performance, whileRDC-NePsignificantly simplifies the processing ofRDCwhen the problem scale increases.

Multi-server jobs are imperative in modern cloud computing systems. A noteworthy feature of multi-server jobs is that, they usually request multiple computing devices simultaneously for their execution. How to schedule multi-server jobs online with a high system efficiency is a topic of great concern. First, the scheduling decisions have to satisfy the service locality constraints. Second, the scheduling decisions needs to be made online without the knowledge of future job arrivals. Third, and most importantly, the actual service rate experienced by a job is usually in fluctuation because of the dynamic voltage and frequency scaling (DVFS) and power oversubscription techniques when multiple types of jobs co-locate. A majority of online algorithms with theoretical performance guarantees are proposed. However, most of them require the processing speeds to be knowable, thereby the job completion times can be exactly calculated. To present a theoretically guaranteed online scheduling algorithm for multi-server jobs without knowing actual processing speeds apriori, in this article, we proposeEsdp(Efficient Sampling-based Dynamic Programming), which learns the distribution of the fluctuated processing speeds over time and simultaneously seeks to maximize the cumulative overall utility. The cumulative overall utility is formulated as the sum of the utilities of successfully serving each multi-server job minus the penalty on the operating, maintaining, and energy cost.Esdpis proved to have a polynomial complexity and a logarithmic regret, which is a State-of-the-Art result. We also validate it with extensive simulations and the results show that the proposed algorithm outperforms several benchmark policies with improvements by up to 73%, 36%, and 28%, respectively.

We consider the problem of dependent task offloading in edge computing with unknown system-side information (e.g., edge transmission rate and computation resources). In this problem, tasks have complicated dependency relationships and have no prior knowledge of system-side information to assist offloading decision-making. Although existing learning-based approaches can help to address unknown system-side information, the impact of inherent task dependency on such approaches has not been formally explored. To bridge the gap, we first use a breadth-first-search (BFS) method to decouple task dependency, and then leverage the Lyapunov optimization technique to transfer the long-term offloading problem to an online optimization problem. Furthermore, we employ the multi-armed bandit (MAB) theory to develop theonlinelearning-baseddependenttaskoffloading algorithm, called OL-DTO. The algorithm can address the unknown system-side information and is augmented with task dependency awareness. We present a rigorous theoretical analysis to evaluate the performance of this algorithm in terms of application delay and UD energy consumption. Our extensive experimental results demonstrate that the OL-DTO algorithm significantly reduces application delay while satisfying the long-term energy budget constraint of the UD.

This article investigates how to enhance the Multi-access Edge Computing (MEC) systems performance with the aid of device-to-device (D2D) communication computation offloading. By adequately exploiting a novel computation offloading mechanism based on D2D collaboration, users can efficiently share computational resources with each other. However, it is challenging to distinguish valuable information that truly promotes a collaborative decision, as worthless information can hinder collaboration among users. In addition, the transmission of large volumes of information requires high bandwidth and incurs significant latency and computational complexity, resulting in unacceptable costs. In this article, we propose an efficient D2D-assisted MEC computation offloading framework based on Attention Communication Deep Reinforcement Learning (ACDRL), which simulates the interactions between related entities, including device-to-device collaboration in the horizontal and device-to-edge offloading in the vertical. Second, we developed a distributed cooperative reinforcement learning algorithm that includes an attention mechanism that skews computational resources towards active users to avoid unnecessary resource wastage in large-scale MEC systems. Finally, to improve the effectiveness and rationality of cooperation among users, we introduce a communication channel to integrate information from all users in a communication group, thus facilitating cooperative decision-making. The proposed framework is benchmarked, and the experimental results show that the proposed framework can effectively reduce latency and provide valuable insights for practical design compared to other baseline approaches.

Many mobile device applications require low end-to-end latency to edge computing infrastructure when offloading their computation tasks in order to achieve real-time perception and cognition for users. User mobility brings significant challenges in providing low-latency offloading due to the limited coverage area of cloudlets. Virtual machine (VM)/container handoff is a promising solution to seamlessly transfer services from one cloudlet to another to maintain low latency as users move. However, an inefficient path planning for the handoff can result in system congestion and consequently poor quality of service (QoS). The situation can even worsen by selfish users who intentionally lie about their true parameters to achieve better service at the cost of degrading the whole system's performance. To fill this research gap, we propose an Online Service Handoff Mechanism (OSHM) to provide an efficient path dynamically for transferring VM/container from the current serving cloudlet to a nearby cloudlet at the destination of a mobile user. Our proposed path planning algorithm is based on a label correction methodology, leading to polynomial time complexity. OSHM is accompanied by our proposed payment determination function to discourage misreporting of unknown parameters. We discuss the theoretical properties of our proposed mechanism in implementing a system equilibrium and ensuring truthfulness. We also perform a comprehensive assessment through extensive experiments which show the efficiency of OSHM in terms of workload, handoff time, consumed energy, and other metrics compared to several benchmarks. Experimental results show that OSHM outperforms other algorithms, reducing at least 61% in average workload, 33% in average handoff time, and 29% in average energy consumption.

Serverless computing has become a popular part of the cloud computing model, thanks to abstracting away infrastructure management and enabling developers to write functions that auto-scale in a polyglot environment, while only paying for the used compute time. While this model is ideal for handling unpredictable and bursty workloads, cold-start latencies of hundreds of milliseconds or more still hinder its support for latency-critical IoT services, and may cancel the latency benefits that come with proximity, when serverless functions are deployed at the edge. Moreover, CPU power and memory limitations which often characterize edge hosts drive latencies even higher. The root of the problem lies in the de facto runtime environments for serverless functions, namely container technologies such as Docker. A radical approach is thus to replace them with a more light-weight alternative. For this purpose, we examine WebAssembly's suitability for use as a serverless container runtime, with a focus on edge computing settings, and present the design and implementation of a WebAssembly-based runtime environment for serverless edge computing. WOW, our prototype for WebAssembly execution in Apache QpenWhisk, reduces cold-start latency by up to 99.5%, can improve on memory consumption by more than 5×, and increases function execution throughput by up to 4.2× on low-end edge computing equipment compared to the standard Docker-based container runtime for various serverless workloads.

IOTA blockchain is a new type of distributed ledger systems that is lightweight without mining and feeless-of-using. Rather than using a chain structure as in traditional blockchains, IOTA organizes ledger records with a directed acyclic graph (DAG), called Tangle. When message entries are committed into the ledger, the ledger tangle grows in a special way where multiple messages could be attached by different processing nodes in parallel. Such a unique evolution process motivates us to study the ledger tangle dynamics, which is unexplored so far. In this paper, we present the first generative modeling for IOTA tangle based on stochastic analysis. A key finding is that IOTA tangle renders a double Pareto Lognormal (dPLN) distribution, rather not typical network models (e.g., Power-Law and Exponential distributions). Quantitative comparisons show that the fitting quality of our model outperforms existing popular models on official real world datasets published by IOTA Foundation. Estimated model parameters are provided, which is immediately instrumental for a more realistic IOTA network generator design. The proposed generative model also provides a deeper understanding of the internal mechanics of IOTA network.

With the rapid evolution of blockchain technology, a clear trend is that new blockchain systems (e.g., IOTA) tend to use a Directed Acyclic Graph (DAG) rather a chain structure to organize ledger records. Such a DAG-based blockchain system shows higher scalability as multiple locations are available in the ledger for new message attachment. To decide an attachment location, a popular type of tip selection algorithms follow an approach using weighted random walks on the DAG ledger. In a burst message arrival scenario, however, a processing node deployed at edge using such a method may become a bottleneck because sequentially repeating random walks significantly increases processing delay. In this paper, we propose a new tip selection algorithm for the burst message arrival scenario on an edge node. Our solution abandons the weighted random walk approach, instead, with similar efforts we transfer to calculate in advance the tip selection probability distribution of the DAG ledger. Such a new scheme reduces tip selection to a probability distribution sampling task, which can be done extremely fast. We implement our solution and demonstrate the benefits of our approach by comparing with the random walk approach. We believe our attempt can effectively mitigate the congestion at the edge node and inspire tip selection algorithm design with a new vision for DAG-based blockchain systems.

This paper presents a framework for defining, performing, and analyzing distributed load testing experiments for benchmarking edge-cloud clusters. This end-to-end workflow helps researchers build reproducible environments to evaluate cluster management techniques. Our implementation extends the open source tool Galileo by adding support for distributed execution on Kubernetes clusters, additional system monitoring instruments, as well as out-of-the box experiment workloads. We focus on providing tools that run across popular CPU architectures and provide a set of representative workloads, such as edge AI functions. We demonstrate our framework's capabilities in a set of experiments based on use cases commonly found in edge computing systems research. Additionally, we show that the resource usage of our system is minimal and that it can run on resource-constrained devices.

The eye-blink pattern is crucial for drowsy driving diagnostics, which has become an increasingly serious social issue. However, traditional methods (e.g., with EOG, camera, wearable, and acoustic sensors) are less applicable to real-life scenarios due to the disharmony between user-friendliness, monitoring accuracy, and privacy-preserving. In this work, we design and implement BlinkRadar as a low-cost and contact-free system to conduct fine-grained eye-blink monitoring in a driving situation using a customized impulse-radio ultra-wideband (IR-UWB) radar which has superior spatial resolution with the ultra-wide bandwidth. BlinkRadar leverages an IR-UWB radar to achieve contact-free sensing, and it fully exploits the complex radar signal for data augmentation. BlinkRadar aims to single out the eye-blink induced waveforms modulated by body movements and vehicle status. It solves the serious interference caused by the unique characteristics of blinking (i.e., subtle, sparse, and non-periodic) and from the human target itself and surrounding objects. We evaluate BlinkRadar in a laboratory environment and during actual road testing. Experimental results show that BlinkRadar can achieve a robust performance of drowsy driving with a median detection accuracy of 92.2% and eye blink detection of 95.5%.

With the increasing complexity, requirements, and variability of cloud services, it is not always easy to find the right static/dynamic thresholds for the optimal configuration of low-level metrics for autoscaling resource management decisions. A Service Level Objective (SLO) is a high-level commitment to maintaining a specific state of a service in a given period, within a Service Level Agreement (SLA): the goal is to respect a given metric, like uptime or response time within given time or accuracy constraints. In this paper, we show the advantages and present the progress of an original SLO-aware autoscaler for the Polaris framework. In addition, the paper contributes to the literature in the field by proposing novel experimental results comparing the Polaris autoscaling performance, based on highlevel latency SLO, and the performance of a low-level average CPU-based SLO, implemented by the Kubernetes Horizontal Pod Autoscaler.

The growing number of Internet of Things (IoT) devices generates massive amounts of diverse data, including personal or confidential information (i.e., sensory, images, etc.) that is not intended for public view. Traditionally, predefined privacy policies are usually enforced in resource-rich environments such as the cloud to protect sensitive information from being released. However, the massive amount of data streams, heterogeneous devices, and networks involved affects latency, and the possibility of having data intercepted grows as it travels away from the data source. Therefore, such data streams must be transformed on the IoT device or within available devices (i.e., edge devices) in its vicinity to ensure privacy. In this paper, we present a privacy-enforcing framework that transforms data streams on edge networks. We treat privacy close to the data source, using powerful edge devices to perform various operations to ensure privacy. Whenever an IoT device captures personal or confidential data, an edge gateway in the device’s vicinity analyzes and transforms data streams according to a predefined set of rules. How and when data is modified is defined precisely by a set of triggers and transformations - a privacy model - that directly represents a stakeholder’s privacy policies. Our work answered how to represent such privacy policies in a model and enforce transformations on the edge.

Dynamic reconfiguration is commonly used to accommodate the dynamic behavior of today’s applications. As cloud-based systems become increasingly complex, it is hard and cost-ineffective to manage them manually. Dynamic routers, such as API Gateways or Message Brokers, in combination with auto-scalers can adapt the system to the resource demands, e.g., when a sudden load spike for a specific part of the system is observed. Without taking costs of cloud resources into account, this reconfiguration can lead to significant increase of charges. We propose a self-adaptive and cost-aware dynamic routing architecture called Adaptive Dynamic Routers. The novel architecture performs a multi-criteria optimization analysis to automatically reconfigure the routers and the services of a cloud-based system considering the costs of reconfiguration. This multidimensional auto-scaling of resources takes incoming load as an input, and uses queuing theory to find an optimal reconfiguration solution. We systematically evaluated our architecture with an extensive number of evaluation cases (9600). On average over cases where an overload is predicted, our approach reduces the overload rate by 46.7% and 61.8% for routers and services, respectively.

Employing today’s deep neural network (DNN) into the cross-platform web with an offloading way has been a promising means to alleviate the tension between intensive inference and limited computing resources. However, it is still challenging to directly leverage the distributed DNN execution into web apps with the following limitations, including (1) how special computing tasks such as DNN inference can provide fine-grained and efficient offloading in the inefficient JavaScript-based environment? (2) lacking the ability to balance the latency and mobile energy to partition the inference facing various web applications’ requirements. (3) and ignoring that DNN inference is vulnerable to the operating environment and mobile devices’ computing capability, especially dedicated web apps. This paper designs AoDNN, an automatic offloading framework to orchestrate the DNN inference across the mobile web and the edge server, with three main contributions. First, we design the DNN offloading based on providing a snapshot mechanism and use multi-threads to monitor dynamic contexts, partition decision, trigger offloading, etc. Second, we provide a learning-based latency and mobile energy prediction framework for supporting various web browsers and platforms. Third, we establish a multi-objective optimization to solve the optimal partition by balancing the latency and mobile energy.

As humans, things, software and AI continue to become the entangled fabric of distributed systems, systems engineers and researchers are facing novel challenges. In this talk, we analyze the role of IoT, Edge, and Cloud, as well as AI in the co-evolution of distributed systems for the new decade. We identify challenges and discuss a roadmap that these new distributed systems have to address. We take a closer look at how a cyber-physical fabric will be complemented by AI operationalization to enable seamless end-to-end distributed systems.

In this panel contribution, I will discuss my vision on the need of developing new managing technologies to harness distributed “computing continuum” systems. These systems are concurrently executed in multiple computing tiers: Cloud, Fog, Edge and IoT. This simple idea develops manifold challenges due to the inherent complexity inherited from the underlying infrastructures of these systems. This makes inappropriate the use of current methodologies for managing Internet distributed systems, which are based on the early systems that were based on client/server architectures and were completely specified by the application software.

Demanding latency-sensitive IoT applications have stringent requirements, such as low latency, better privacy, and security. To meet such requirements, researchers proposed a new paradigm, i.e., edge computing. Edge computing consists of distributed computational resources and enables the execution of IoT applications closer to the edge of the network. However, the distributed nature of this paradigm makes the application deployment and development process more challenging since the developer must divide the application’s functionality into multiple parts, assigning for each a set of requirements. As a result, the developer must: 1) define the application’s requirements and validate them at design time and 2) find a deployment strategy on the target edge computing platform. In this article, we propose EdgeFlow, a new IoT framework capable of assisting the developer in the application development process. Specifically, we introduce a methodology for latency-sensitive IoT applications development and deployment, consisting of three different stages, i.e., the development, validation, and deployment. To this end, we propose an extension of the flow-based programming paradigm with new timing requirements and provide a resource allocation technique to assist with the deployment and validation of latency-sensitive IoT applications. Finally, we evaluate EdgeFlow by: 1) presenting the application development methodology and 2) performing a quantitative evaluation demonstrating our resource allocation technique’s capabilities to find feasible and optimal deployment strategies. The experimental results illustrate the effectiveness of our methodology to assist the developer throughout the entire application development process.

Deep neural network (DNN) shows great promise in providing more intelligence to ubiquitous end devices. However, the existing partition-offloading schemes adopt data-parallel or model-parallel collaboration between devices and the cloud, which does not make full use of the resources of end devices for deep-level parallel execution. This article proposes eDDNN (i.e., enabling Distributed DNN), a collaborative inference scheme over heterogeneous end devices using cross-platform Web technology, moving the computation close to ubiquitous end devices, improving resource utilization, and reducing the computing pressure of data centers. eDDNN implements D2D communication and collaborative inference among heterogeneous end devices with WebRTC protocol, divides the data and corresponding DNN model into pieces simultaneously, and then executes inference almost independently by establishing a layer dependency table. Besides, eDDNN provides a dynamic allocation algorithm based on deep reinforcement learning to minimize latency. We conduct experiments on various data sets and DNNs and further employ eDDNN into a mobile Web AR application to illustrate the effectiveness. The results show that eDDNN can achieve the latency decrease by$2.98\times $, reduce mobile energy by$1.8\times $, and relieve the computing pressure of the edge server by$2.57\times $, against a typical partition-offloading approach.

Running computer vision applications, such as 3-D simultaneous localization and mapping (SLAM), on mobile devices requires low-latency responses and a massive amount of computation. Edge computing has been introduced to move Cloud features closer to end users, providing necessary computing and network resources for end devices. The heterogeneous edge devices, with different hardware architectures (e.g., CPUs and GPUs) and runtime environments, provide diverse resources to support processing tasks from end devices, resulting in different costs and quality of services. How to partition these computing tasks and distribute them over these heterogeneous hardware nodes is still an open research question. Considering these inherently heterogeneous hardware architectures, new approaches for service orchestration and task scheduling are required to meet the service-level agreement and reduce the overall cost of the system (e.g., facility utilization cost). This article presents a system framework, EDGE VISION, for computer vision applications partitioning and orchestration on heterogeneous edge computing platforms considering both CPUs and GPUs. EDGE VISION abstracts the heterogeneous hardware resources and the task runtime environments and divides the application into separate tasks to be orchestrated and deployed into the heterogeneous edge nodes. We also propose two scheduling algorithms in our framework, minimum latency task scheduling and minimum cost task scheduling, aiming to minimize the processing latency and the overall system cost. We evaluate our framework by implementing the edge-based 3-D SLAM application in our real testbed with ten heterogeneous edge devices. Evaluations show that EdgeVision can efficiently minimize the processing latency and the system overall cost and achieve up to 30% decrease in task processing latency and 15% more cost saving compared to the State-of-the-Art baselines.

As mobile shopping has gradually become the mainstream shopping mode, recommendation systems are gaining an increasingly wide adoption. Existing recommendation systems are mainly based on explicit and implicit user behaviors. However, these user behaviors may not directly indicate users’ inner feelings, causing erroneous user preference estimation and thus leading to inaccurate recommendations. Inspired by our key observation on the correlation between pupil size and users’ inner feelings, we consider using the change of pupil size when browsing to model users’ preferences, so as to achieve targeted recommendations. To this end, we propose PupilRec as a computer-vision-based recommendation framework involving a mobile terminal and a server side. On the mobile terminal, PupilRec collects users’ pupil size change information through the front camera of smartphones; it then preprocesses the raw pupil size data before transmitting them to the server. On the server side, PupilRec utilizes the Tsfresh package and Random Forest algorithm to figure out the key time-series features directly implying user preferences. PupilRec then trains a neural network to fit a user preference model. Using this model, PupilRec predicts user preference to obtain a user–product matrix and further simplifies it by singular value decomposition. Finally, the real-time recommendation is achieved by a collaborative filtering module that retrieves recommended contents to users smartphones. We prototype PupilRec and conduct both experiments and field studies to comprehensively evaluate the effectiveness of PupilRec by recruiting 67 volunteers. The overall results show that PupilRec can accurately estimate users’ preference and can recommend products users interested in.

The rapid developments in hardware, software, and communication technologies have facilitated the spread of interconnected sensors, actuators, and heterogeneous devices such as single board computers, which collect and exchange a large amount of data to offer a new class of advanced services characterized by being available anywhere, at any time, and for anyone. This ecosystem is widely referred to as the Internet of Things (IoT). In the past years, the number of deployments both for sensor networks and the IoT grew significantly. This continuous and exponential growth is facilitated by investments and research activities originating from industry, academia, and governments while the penetration of these technologies is also driven by the high technology acceptance rates of both consumers and technologists across disciplines. Such networks collect, store, and exchange a large volume of heterogeneous data. Nevertheless, their rapid and widespread deployment, along with their participation in the provisioning of potentially critical services (e.g., safety applications, healthcare, and manufacturing) raise numerous issues related to the security, data analysis, and energy awareness of the performed operations and provided services.

There are billions of devices worldwide deployed, connected, and communicating to other systems. Sensors and actuators, which can be stationary or movable devices. These Edge devices are considered part of the Internet of Things (IoT) devices, which can be referred to as a tier of the Computing Continuum paradigm. There are two main concerns at stake in the success of this ecosystem. The interoperability between devices and systems is the first. Mainly, because most of them communicate uniquely and differently from each other, leading to heterogeneous data. The second issue is the lack of decision-making capacity to conduct actuations, such as communicating through different computing tiers based on latency constraints due to a certain measured factor. In this article, we propose an ontology to improve device interoperability in the IoT. In addition, we also explain how to ease data communication between Computing Continuum devices, providing tools to enhance data management and decision-making. A use case is also presented, using the automotive industry, where quickness in maneuver determination is key to avoid accidents. It is exemplified using two Raspberry Pi devices, connected using different networks and choosing the appropriate one depending on context-aware conditions.

Current spatial-keyword publish/subscribe systems need to handle spatial-keyword skyline queries over geo-textual streams to continuously obtain good results. The skyline queries in such systems face two main problems: (1) query problems, because the powerful query capability is required for the strict limit of the response time and the large number of items concerned by the users, and (2) scalability issue, because millions of active users are maintained simultaneously with many network-connected machines. Unfortunately, the current approach is towards static data. Thus, this paper first proposes a distributed skyline query processing framework. Then, we optimize the skyline computing by introducing MF-R$^t$-tree, which is an update-efficient and space-saving indexing structure and a fast approach for processing a continuous spatial-keyword skyline query called$eager^*$. Finally, a spatial and textual signature-based communication optimization method is proposed to support scalability. The experimental results indicate that (1) MF-R$^t$-tree can significantly reduce update costs, while maintaining a low storage cost, and a query performance comparable to IL-Quadtree, (2)$eager^*$can averagely accelerate 79.72 × faster than the method based on BNL, (3) the communication optimization method significantly reduces the communication cost, and (4) the distributed framework can efficiently support large-scale skyline queries.

Multi-user mobile Augmented Reality (AR) has been successfully used in various fields as a novel visual interaction technology. But current mainstream wearable device-based and app-based solutions are still facing cross-platform, real-time communication, and intensive computing requirements. Mobile Web technology is envisioned to be a promising supporting technology for cross-platform application of mobile AR especially in 5G networks, which provide pervasive communication and computing resources thereby forming a formidable framework for the practical application of multi-user mobile Web AR. However, the problem of how to use these new techniques properly to achieve efficient communication and computing collaboration is obviously paramount in order for multi-user mobile Web AR to be realized in 5G networks. In this article, we propose the first edge-assisted multi-user collaborative framework for mobile Web AR in the 5G era. First, we propose a heuristic mechanism BA-CPP for efficient communication planning, which allows multi-user interaction synchronization to be achieved. Second, we introduce a motion-aware key frame selection mechanism called Mo-KFP to optimize the computational efficiency of the edge system, and simultaneously alleviate the initialization problem by collaborating with nearby mobile devices using the Device-to-Device (D2D) communication technique. Experiments are conducted in a real-world 5G network, and the results demonstrate the superiority of our proposed collaborative framework.

Enabling deep learning technology on the mobile web can improve the user’s experience for achieving web artificial intelligence in various fields. However, heavy DNN models and limited computing resources of the mobile web are now unable to support executing computationally intensive DNNs when deploying in a cloud computing platform. With the help of promising edge computing, we propose a lightweight collaborative deep neural network for the mobile web, named LcDNN, which contributes to three aspects: (1) We design a composite collaborative DNN that reduces the model size, accelerates inference, and reduces mobile energy cost by executing a lightweight binary neural network (BNN) branch on the mobile web. (2) We provide a jointly training method for LcDNN and implement an energy-efficient inference library for executing the BNN branch on the mobile web. (3) To further promote the resource utilization of the edge cloud, we develop a DRL-based online scheduling scheme to obtain an optimal allocation for LcDNN. The experimental results show that LcDNN outperforms existing approaches for reducing the model size by about 16x to 29x. It also reduces the end-to-end latency and mobile energy cost with acceptable accuracy and improves the throughput and resource utilization of the edge cloud.

Network slicing is the key to enable virtualized resource sharing among vertical industries in the era of 5G communication. Efficient resource allocation is of vital importance to realize network slicing in real-world business scenarios. To deal with the high algorithm complexity, privacy leakage, and unrealistic offline setting of current network slicing algorithms, in this paper we propose a fully decentralized and low-complexity online algorithm, DPoS, for multi-resource slicing. We first formulate the problem as a global social welfare maximization problem. Next, we design the online algorithm DPoS based on the primal-dual approach and posted price mechanism. In DPoS, each tenant is incentivized to make its own decision based on its true preferences without disclosing any private information to the mobile virtual network operator and other tenants. We provide a rigorous theoretical analysis to show that DPoS has the optimal competitive ratio when the cost function of each resource is linear. Extensive simulation experiments are conducted to evaluate the performance of DPoS. The results show that DPoS can not only achieve close-to-offline-optimal performance, but also have low algorithmic overheads.

Edge computing offers the possibility of deploying applications at the edge of the network. To take advantage of available devices’ distributed resources, applications often are structured as microservices, often having stringent requirements of low latency and high availability. However, a decentralized edge system that the application may be intended for is characterized by high volatility, due to devices making up the system being unreliable or leaving the network unexpectedly. This makes application deployment and assurance that it will continue to operate under volatility challenging. We propose an adaptive framework capable of deploying and efficiently maintaining a microservice-based application at runtime, by tackling two intertwined problems: (i) finding a microservice placement across device hosts and (ii) deriving invocation paths that serve it. Our objective is to maintain correct functionality by satisfying given requirements in terms of end-to-end latency and availability, in a volatile edge environment. We evaluate our solution quantitatively by considering performance and failure recovery.

Recent advancements in distributed systems have enabled deploying low-latency and highly resilient edge applications close to the IoT domain at the edge of the network. The broad range of edge application requirements combined with heterogeneous, resource-constrained, and dynamic edge networks make it particularly challenging to configure and deploy them. Besides that, missing elastic capabilities on the edge makes it difficult to operate such applications under dynamic workloads. To this end, this article proposes a lightweight, self-adaptive, and decentralized mechanism (DECENT) for (1) deploying edge applications on edge resources and on premises of Edge-Cloud infrastructure and (2) controlling elasticity requirements. DECENT enables developers to characterize their edge applications by specifying elasticity requirements, which are automatically captured, interpreted, and enforced by our decentralized elasticity interpreters. In response to dynamic workloads, edge applications automatically adapt in compliance with their elasticity requirements. We discuss the architecture, processes of the approach, and the experiment conducted on a real-world testbed to validate its feasibility on low-powered edge devices. Furthermore, we show performance and adaptation aspects through an edge safety application and its evolution in elasticity space (i.e., cost, resource, and quality).

In mobile edge computing (MEC) systems, mobile users (MUs) are capable of allocating local resources (CPU frequency and transmission power) and offloading tasks to edge servers in the vicinity in order to enhance their computation capabilities and reduce back-and-forth transmission over backhaul link. Nevertheless, mobile environment makes it hard to draw offloading and resource allocation decisions under dynamical wireless channel state and users’ locations. In real life, social relationship is also provably a significant factor affecting integral performance in collaborative work, which results in MUs decisions strongly coupled and renders this problem further intractable. Most of previous works ignore the impact of inter-user dependency (or data dependency among IoT devices). To bridge this gap, we study the service collaboration with master-slave dependency among service chains of MUs and formulate this combinational optimization problem as a mixed integer non-linear programming (MINLP) problem. To this end, we derive the closed-form expression of resource allocation solution by convex optimization and transform it to integer linear programming (ILP) problem. Subsequently, we propose a distributed algorithm based on Markov approximation which has polynomial computation complexity. Experimental result on real-world dataset substantiates the usefulness and superiority of our scheme, in terms of reducing latency and energy consumption.

Edge computing is booming as a promising paradigm to extend service provisioning from the centralized cloud to the network edge. Benefit from the development of serverless computing, an edge server can be configured as a carrier of limited serverless functions, in the way of deploying Docker runtime and Kubernetes engine. Meanwhile, an application generally takes the form of directed acyclic graphs (DAGs), where vertices represent dependent functions and edges represent data traffic. The status quo of minimizing the completion time (a.k.a. makespan) of the application motivates the study on optimal function placement. However, current approaches lose sight of proactively splitting and mapping the traffic to the logical data paths between the heterogeneous edge servers, which could affect the makespan significantly. To remedy that, we propose an algorithm, termed as Dependent Function Embedding (DPE), to get the optimal edge server for each function to execute and the moment it starts executing. DPE finds the best segmentation of each data traffic by exquisitely solving several infinity norm minimization problems. DPE is theoretically verified to achieve the global optimality. Extensive experiments on Alibaba cluster trace show that DPE significantly outperforms two baseline algorithms in makespan by 43.19% and 40.71%, respectively.

Satisfying the software requirements of emerging service-based Internet of Things (IoT) applications has become challenging for cloud-centric architectures, as applications demand fast response times and availability of computational resources closer to end-users. Meeting application demands must occur at runtime, facing uncertainty and in a decentralized manner, something that must be reflected in system deployment. We propose a decentralized resource management technique and accompanying technical framework for the deployment of service-based IoT applications at the edge. Faithful to services engineering, applications we consider are composed of interdependent tasks, which in the IoT setting may be concretized as containerized microservices or serverless functions. A deployment for an arbitrary application is found at runtime through satisfiability; the mapping produced is compliant with tasks’ individual resource requirements and latency constraints by construction. Our approach ensures seamless deployment at runtime, assuming no design-time knowledge of device resources or the current network topology. We evaluate the applicability and realizability of our technique over single-board computers as edge devices, particularly in the absence of cloud resources.

Recent advancements in distributed systems have enabled deploying low-latency edge applications (i.e., IoT applications) in proximity to the end-users, respectively, in edge networks. The stringent requirements combined with heterogeneous, resource-constrained and dynamic edge networks make the deployment process a challenging task. Besides that, the lack of resource discovery features make it particularly difficult to fully exploit available resources (i.e., computational, storage, and IoT resources) provided by low-powered edge devices. To that end, this article proposes a decentralized resource discovery mechanism that enables discovering resources in an automatic manner in edge networks. Through replicating resource descriptions (i.e., metadata), edge devices exchange information about available resources within their scope in a peer-to-peer manner. To handle the resource discovery complexity, we propose a solution to built edge networks as a flat model and enable edge devices to be organized in clusters. Our approach supports the system in coping with the dynamicity and uncertainty of edge networks. We discuss the architecture, processes of the approach, and the experiments we conducted on a testbed to validate its feasibility on resource-constrained edge networks.

Web-based Deep Neural Networks (DNNs) enhance the ability of object recognition and has attracted considerable attention in mobile Web AR and other services. However, neither performing the DNN inference on mobile Web browsers locally nor offloading computations to the cloud can strike a balance between accuracy and efficiency; generally, rude methods are often accompanied by unsatisfactory accuracy. Collaborative approaches seem to fill this gap by coordinating the distributed hierarchical computing resources, especially in the 5G era, but it still faces challenges in the current solutions, such as the lack of (1) full use of 5G resources for the one point DNN computation partitioning schemes; (2) fine-grained branching mechanism; (3) efficient partitioning method; and (4) multi-objective optimization. To this end, we present the fine-grained elastic computation partitioning mechanism for distributed DNN in 5G networks. First, we elaborate two collaborative scenarios. Second, we study the DNN branching mechanism at layer granularity. Next, we propose a DNN computation partitioning algorithm based on deep reinforcement learning. Finally, we develop a mobile Web AR application as a proof of concept. The experiments were conducted in an actually deployed 5G trial network, and the results show the superiority of this collaborative approach. The common theme is, under the premise that Quality of Service (QoS) is satisfied, to balance multiple interests by orchestrating computations across heterogeneous computing platforms.

Complex distributed systems such as the ones induced by Internet of Things (IoT) deployments, are expected to operate in compliance to their requirements. This can be checked by inspecting events flowing throughout the system, typically originating from end-devices and reflecting arbitrary actions, changes in state or sensing. Such events typically reflect the behavior of the overall IoT system – they may indicate executions which satisfy or violate its requirements. This article presents a service-based software architecture and technical framework supporting runtime verification for widely deployed, volatile IoT systems. At the lowest level, systems we consider are comprised of resource-constrained devices connected over wide area networks generating events. In our approach, monitors are deployed on edge components, receiving events originating from end-devices or other edge nodes. Temporal logic properties expressing desired requirements are then evaluated on each edge monitor in a runtime fashion. The system exhibits decentralization since evaluation occurs locally on edge nodes, and verdicts possibly affecting satisfaction of properties on other edge nodes are propagated accordingly. This reduces dependence on cloud infrastructures for IoT data collection and centralized processing. We illustrate how specification and runtime verification can be achieved in practice on a characteristic case study of smart parking. Finally, we demonstrate the feasibility of our design over a testbed instantiation, whereupon we evaluate performance and capacity limits of different hardware classes under monitoring workloads of varying intensity using state-of-the-art LPWAN technology.

Multi-access edge computing (MEC) is booming as a promising paradigm to push the computation and communication resources from cloud to the network edge to provide services and to perform computations. With container technologies, mobile devices with small memory footprint can run composite microservice-based applications without time-consuming backbone. Service placement at the edge is of importance to put MEC from theory into practice. However, current state-of-the-art research does not sufficiently take the composite property of services into consideration. Besides, although Kubernetes has certain abilities to heal container failures, high availability cannot be ensured due to heterogeneity and variability of edge sites. To deal with these problems, we propose a distributed redundant placement framework SAA-RP and a GA-based Server Selection (GASS) algorithm for microservice-based applications with sequential combinatorial structure. We formulate a stochastic optimization problem with the uncertainty of microservice request considered, and then decide for each microservice, how it should be deployed and with how many instances as well as on which edge sites to place them. Benchmark policies are implemented in two scenarios, where redundancy is allowed and not, respectively. Numerical results based on a real-world dataset verify that GASS significantly outperforms all the benchmark policies.

The papers in this special section focus on sustainability of edge computing systems. Edge computing is an emerging architectural and technical approach aimed at addressing various shortcomings in traditional cloud computing paradigms and responding to today’s constantly increasing data-demanding services such as Internet-of-Things, 5G embedded artificial intelligence and smart cities. In Fog/Edge Computing, nodes at the edge of a network are equipped with processing, storage, networking, etc. capabilities to take over several tasks that were used to be sent to cloud services. Pre-filtering and aggregation of data as well as online processing and actuation are sample procedures envisaged/dedicated to fog/ edge nodes.

Deep Neural Networks (DNNs) are an application class that benefit from being distributed across the edge and cloud. A DNN is partitioned such that specific layers of the DNN are deployed onto the edge and the cloud to meet performance and privacy objectives. However, there is limited understanding of: whether and how evolving operational conditions (increased CPU and memory utilization at the edge or reduced data transfer rates between the edge and cloud) affect the performance of already deployed DNNs, and whether a new partition configuration is required to maximize performance. A DNN that adapts to changing operational conditions is referred to as an ‘adaptive DNN’. This paper investigates whether there is a case for adaptive DNNs by considering four questions: (i) Are DNNs sensitive to operational conditions? (ii) How sensitive are DNNs to operational conditions? (iii) Do individual or a combination of operational conditions equally affect DNNs? (iv) Is DNN partitioning sensitive to hardware architectures? The exploration is carried out in the context of 8 pre-trained DNN models and the results presented are from analyzing nearly 8 million data points. The results highlight that network conditions affect DNN performance more than CPU or memory related operational conditions. Repartitioning is noted to provide a performance gain in a number of cases, but a specific trend is not noted in relation to the underlying hardware architecture. Nonetheless, the need for adaptive DNNs is confirmed.

Application workload scheduling in hybrid Cloud-Edge-IoT infrastructures has been extensively researched over the last years. The recent trend of containerizing application workloads, both in the cloud and on the edge, has further fueled the need for more advanced scheduling solutions in these hybrid infrastructures. Unfortunately, most of the current approaches are not fully sensitive to the edge properties and also lack adequate support for Service Level Objective (SLO) awareness. Previously, we introduced software defined gateways (SDGs), which enable managing novel edge resources at scale. At the same time Kubernetes was initially released. In spite of not being specifically developed for the edge, Kubernetes implements many of the design principles introduced by our SDGs, making it suitable for building SDG extensions on top of it. In this paper we present Polaris Scheduler - a novel scheduling framework, which enables edge sensitive and SLO aware scheduling in the Cloud-Edge-IoT Continuum. Polaris Scheduler is being developed as a part of Linux Foundation's Centaurus project. We discuss the main research challenges, the approach, and the vision of SLO aware edge sensitive scheduling.

Service Level Objectives (SLOs) guide the elasticity of cloud applications, e.g., by deciding when and how much the resources provisioned to an application should be changed. Evaluating SLOs requires metrics, which can be directly measured on the application or system, or, more elaborately, be composed from multiple low-level metrics. The implementation of such metrics and SLOs, the triggering of elasticity strategies, and allowing configurability by the user deploying an application, requires a flexible middleware. In this paper, we present a middleware that provides an orchestrator-independent SLO controller for periodically evaluating SLOs and triggering elasticity strategies, while decoupling SLOs from the elasticity strategies to increase flexibility, and provider-independent services for obtaining low-level metrics and composing them into higher-level metrics. We evaluate our middleware by implementing a motivating use case, featuring a cost efficiency SLO for an application deployed on Kubernetes.

The microservice architectural style is changing the design of modern applications. Orchestration tools, such as Kubernetes, deploy them on computing nodes assuming that resources are interconnected through fast communication links. However, running microservices in the emerging edge computing environments requires considering the heterogeneity and nonnegligible network delays among edge resources. In this context, although the problem of scheduling synchronous microservice-based applications has been widely explored, scheduling asynchronous applications, where microservices interact using a queue system, has only recently started to be investigated. In this paper, we present Pogonip, an edge-aware scheduler for Kubernetes, designed for asynchronous microservices. We formulate an optimization problem and a heuristic for determining the placement of microservices, which is tailored for edge environments. We integrate them in Kubernetes by building custom scheduler plugins. Using a benchmark application, we show the advantages of the proposed network-aware solutions over other state-of-the-art solutions.

We present an orchestration scheme for Deep Neural Network (DNN) model serving, capable of computation distribution over the device-to-cloud continuum and low-latency inference. Our system allows automated layer-wise splitting of DNN structures and their adaptive distribution over compute hosts, providing an execution environment for collaborative inference. Model deployment and its self-adaptation at runtime are implemented by optimization algorithms supported in a plug-in manner. These follow service and infrastructure provider criteria and constraints, expressed via well-defined interfaces. Our framework can serve diverse neural architectures, including DNNs with early exits, with zero to minimal modifications.

The emerging Edge computing paradigm facilitates the deployment of distributed AI-applications and hardware, capable of processing video data in real time. AI-assisted video analytics can provide valuable information and benefits for parties in various domains. Face recognition, object detection, or movement tracing are prominent examples enabled by this technology. However, the widespread deployment of such mechanism in public areas are a growing cause of privacy and security concerns. Data protection strategies need to be appropriately designed and correctly implemented in order to mitigate the associated risks. Most existing approaches focus on privacy and security related operations of the video stream itself or protecting its transmission. In this paper, we propose a privacy preserving system for AI-assisted video analytics, that extracts relevant information from video data and governs the secure access to that information. The system ensures that applications leveraging extracted data have no access to the video stream. An attribute-based authorization scheme allows applications to only query a predefined subset of extracted data. We demonstrate the feasibility of our approach by evaluating an application motivated by the recent COVID-19 pandemic, deployed on typical edge computing infrastructure.

In this talk we discuss the challenges ahead when researching the confluence of Internet of Things, Edge Computing, Fog Computing, and Cloud Computing. In particular we discuss the topics related to research issues in the area of AI and Edge Computing.

Service Level Objectives (SLOs) allow defining expected performance of cloud services, such that cloud service providers know what they guarantee and service consumers know what to expect. Most approaches focus on low-level SLOs, closely related to resources, e.g., average CPU or memory usage, and are usually bound to specific elasticity controllers. We present SLO Script, a language and accompanying framework, motivated by real-world, industrial needs to allow service providers to define complex, high-level SLOs in an orchestrator-independent manner. The main features of SLO Script include: i) novel abstractions (StronglyTypedSLO) with type safety features, ensuring compatibility between SLOs and elasticity strategies, ii) abstractions that enable decoupling of SLOs from elasticity strategies, iii) a strongly typed metrics API, and iv) an orchestrator-independent object model that enables language extensibility. We present a case study about a real-world, cloud-native application and evaluate our language while implementing a realistic Cost Efficiency SLO.

Many different dynamic routing architectures are available, including sidecar-based routing, routing through a central entity such as an event store or gateway, or architectures with multiple routers. These architectures are currently based on vastly different implementation concepts, such as API Gateways, Message Brokers, or Service Proxies. We propose a new approach that abstracts all these architecture patterns using one Adaptive Dynamic Routers architecture. We hypothesize that a dynamic self-adaptation of the routing architecture is beneficial over any fixed architecture selections for reliability and performance trade-offs. That is, if encountered with traffic and load changes, our approach dynamically self-adapts between more central or distributed routing to optimize system reliability and performance. We evaluate our approach by analyzing our previously-measured data during an experiment of 1200 hours of runtime. Our extensive systematic evaluation with 1089 cases confirms that our hypothesis holds and our approach is beneficial in terms of reliability and performance. Moreover, we empirically validate our results on Google Cloud Platform infrastructure.

Complex distributed systems such as the ones induced by Internet of Things (IoT) deployments, are expected to operate in compliance to their requirements. This can be checked by inspecting events flowing throughout the system, typically originating from end-devices and reflecting arbitrary actions, changes in state or sensing. Such events typically reflect the behavior of the overall IoT system – they may indicate executions which satisfy or violate its requirements.

Multi-access Edge Computing is booming as a promising paradigm to push the computation and communication resources from cloud to the edge to provision services and to perform computations. With container technologies, mobile devices with small memory footprint can run composite microservice-based applications without the time-consuming backbone transmission. Service placement at the edge is of importance to put MEC from theory into practice. However, current state-of-the-art research does not sufficiently take the composite property of services into consideration but study the to-be-placed services in an atomic way. Besides, although Kubernetes has certain abilities to heal container failures, high availability cannot be ensured due to heterogeneity and variability of edge sites.

Operating data-intensive applications on edge systems is challenging, due to the extreme workload and device heterogeneity, as well as the geographic dispersion of compute and storage infrastructure. Serverless computing has emerged as a compelling model to manage the complexity of such systems, by decoupling the underlying infrastructure and scaling mechanisms from applications. Although serverless platforms have reached a high level of maturity, we have found several limiting factors that inhibit their use in an edge setting. This paper presents a container scheduling system that enables such platforms to make efficient use of edge infrastructures. Our scheduler makes heuristic trade-offs between data and computation movement, and considers workload-specific compute requirements such as GPU acceleration. Furthermore, we present a method to automatically fine-tune the weights of scheduling constraints to optimize high-level operational objectives such as minimizing task execution time, uplink usage, or cloud execution cost. We implement a prototype that targets the container orchestration system Kubernetes, and deploy it on an edge testbed we have built. We evaluate our system with trace-driven simulations in different infrastructure scenarios, using traces generated from running representative workloads on our testbed. Our results show that (a) our scheduler significantly improves the quality of task placement compared to the state-of-the-art scheduler of Kubernetes, and (b) our method for fine-tuning scheduling parameters helps significantly in meeting operational goals.

When using the belief rule base (BRB) methodology to deal with the nonlinear causal inference problems, combinatorial explosion often occurs due to overnumbered antecedent attributes, resulting in poor performance. Therefore, this paper proposes a novel nonlinear causal inference approach based on vector-based BRB. In the modeling process of BRB, the original attributes are ranked by contribution rate and transformed into attribute vectors. Meanwhile, combined with the k-means method, appropriate referential vectors are obtained. Thereby a vector-based BRB can be established. In the inference process of BRB, the idea of full activation of vector-based rules is presented. By calculating the spatial matching degree of the testing sample and the referential vectors, activation weights of the rules which are used in the evidential reasoning algorithm are acquired. Experimental results of a nonlinear function with four-dimensional input and the pipeline leakage detection data show the effectiveness and superiority of the proposed approach.

Combining image segmentation with Web technology lays a good foundation for lightweight, cross-platform, and pervasive Web artificial intelligence applications, and further improves the capability of Web-of-Things (WoT) applications. However, no matter whether we use a Web real-time communication media server for advanced processing that views camera inputs as a video stream, or transfer continuous camera frames to the remote cloud for processing, we are unable to obtain a satisfactory real-time experience due to high resource consumption and unacceptable latency. In this article, we present EdgeBooster, a computational-efficient architecture that leverages a common edge server to minimize the communication costs, accelerates the camera frame segmentation, and guarantees an acceptable segmentation accuracy with the prior knowledge. EdgeBooster provides real-time segmentation by developing parallel technology that enables segmentation on slices of a camera frame and using presegmentation based on superpixels to accelerate the graph-based segmentation. It also introduces recent DNN-based segmentation results as the prior knowledge to improve the performance of the graph-based segmentation, especially in nonideal scenes, such as dark light and weak contrast. Finally, it creates a pure frontend segmentation that can provide continuous and stable services for mobile users in unstable networks, such as a weak network or with an unstable edge server. The experimental results show that EdgeBooster is able to achieve a considerable accuracy for the mobile Web, running at no less than 30 frames per second in real scenes.

Industry 4.0 have automated the entire manufacturing sector (including technologies and processes) by adopting Internet of Things and cloud computing. To handle the workflows from Industrial Cyber-Physical systems, more and more data centers have been built across the globe to serve the growing needs of computing and storage. This has led to an enormous increase in energy usage by cloud data centers, which is not only a financial burden but also increases their carbon footprint. The private software defined wide area network (SDWAN) connects a cloud provider's data centers across the planet. This gives the opportunity to develop new scheduling strategies to manage cloud providers workload in a more energy-efficient manner. In this context, this article addresses the problem of scheduling data-driven industrial workflow applications over a set of private SDWAN connected data centers in an energy-efficient manner while managing tradeoff of a cloud provider' revenue. Our proposed algorithm aims to minimize the cloud provider's revenue and the usage of nonrenewable energy by utilizing the real-world electricity prices with the availability of green energy on different cloud data centers, where the energy consumption consists of the usage of running application over multiple data centers and transferring the data among them through SDWAN. The evaluation shows that our proposed method can increase usage of green energy for the execution of industrial workflow up to 3× times with a slight increase in the cost when compared to cost-based workflow scheduling methods.

The dramatically increasing of mobile applications make it convenient for users to complete complex tasks on their mobile devices. However, the latency brought by unstable wireless networks and the computation failures caused by constrained resources limit the development of mobile computing. A popular approach to solve this problem is to establish a mobile service provisioning system based on a mobile edge computing (MEC) paradigm. In the MEC paradigm, plenty of machines are placed at the edge of the network so that the performance of applications can be optimized by using the involved microservice instances deployed on them. In this paper, we explore the deployment problem of microserivce-based applications in the MEC environment and propose an approach to help to optimize the cost of application deployment with the constraints of resources and the requirement of performance. We conduct a series of experiments to evaluate the performance of our approach. The result shows that our approach can improve the average response time of mobile services.

introduction Share on Introduction to the Special Section on Cognitive Robotics on 5G/6G Networks Authors: Huimin Lu Kyushu Institute of Technology, Japan Kyushu Institute of Technology, JapanView Profile , Liao Wu University of New South Wales, Australia University of New South Wales, AustraliaView Profile , Giancarlo Fortino University of Calabria (Unical), Italy University of Calabria (Unical), ItalyView Profile , Schahram Dustdar Vienna University of Technology, Austria Vienna University of Technology, AustriaView Profile Authors Info & Claims ACM Transactions on Internet TechnologyVolume 21Issue 4November 2021 Article No.: 91epp 1–3https://doi.org/10.1145/3476466Published:28 September 2021Publication History 1citation36DownloadsMetricsTotal Citations1Total Downloads36Last 12 Months26Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Destination prediction plays an important role as the basis for a variety of location-based services (LBSs). However, it poses many threats to users’ location privacy. Most related work ignores privacy preservation in destination prediction. Few studies focus on specific kinds of privacy-preserving destination prediction algorithms and thus are not applicable to other prediction methods. Furthermore, the third party involved in these studies is a potential privacy threat. Additionally, another line of related work regarding LBSs neither guarantees the utility of the predicted results nor provides quantifiable privacy preservation. To this end, in this paper, we propose a general framework that can provide quantifiable privacy preservation and obtain a trade-off between the privacy and the utility of the predicted results by utilizing differential privacy and a neural network model. Specifically, it first adopts a specially designed differential privacy to construct a data-driven privacy-preserving model that formulates the relationship between injected noise and privacy preservation. Then, it combines a Recurrent Neural Network and Multi-hill Climbing to add fine-grained noise to obtain the trade-off between the privacy preservation and the utility of the predicted results. Our extensive experiments on real-world datasets validate that the proposed framework can be applied to different prediction methods, provide quantifiable location privacy preservation, and guarantee the utility of the predicted results simultaneously.

Edge computing, a fast evolving computing paradigm, has spawned a variety of new system architectures and computing methods discussed in both academia and industry. Edge servers are directly deployed near users' equipment or devices owned by telecommunications companies. This allows for offloading computing tasks of various devices nearby to edge servers. Due to the shortage of computing resources in edge computing networks, they are often not as sufficient as the computing resources in a cloud computing center. This leads to the problem of service load imbalance once the load in the edge computing network increases suddenly. To solve the problem of “load evacuation” in edge environments, we introduce a strategy when the number of service requests for mobile devices or IoT devices increases rapidly within a short period of time. Therefore, to prevent poor QoS in edge computing, service load should be migrated to other edge servers to reduce the overall delay of these service requests. In this article, we have introduced a strategy with two stages during the burst load evacuation. Based on an optimal routing search at the dispatching stage, tasks will be migrated from the server in which the burst load occurs to other servers as soon as possible. Subsequently, with the assistance of the remote server and edge servers, these tasks are processed with the highest efficiency through the proposed parallel structure at the scheduling stage. Finally, we conduct numerical experiments to clarify the superiority of our algorithm in an edge environment simulation.

An increasing interest is growing around the idea of microservices and the promise of improving scalability when compared to monolithic systems. Several companies are evaluating pros and cons of a complex migration. In particular, financial institutions are positioned in a difficult situation due to the economic climate and the appearance of agile competitors that can navigate in a more flexible legal framework and started their business since day one with more agile architectures and without being bounded to outdated technological standard. In this paper, we present a real world case study in order to demonstrate how scalability is positively affected by re-implementing a monolithic architecture (MA) into a microservices architecture (MSA). The case study is based on theFX Coresystem, a mission critical system of Danske Bank, the largest bank in Denmark and one of the leading financial institutions in Northern Europe. The technical problem that has been addressed and solved in this paper is the identification of a repeatable migration process that can be used to convert a real world Monolithic architecture into a Microservices architecture in the specific setting of financial domain, typically characterized by legacy systems and batch-based processing on heterogeneous data sources.

IOTA organizes transactions in the ledger as a Directed Acyclic Graph (DAG) called Tangle, instead of a hash chain of transaction blocks used by most of traditional blockchains. IOTA is considered a promising platform to support Internet-of-Things (IoT) applications with its key features such as micropayment support and absence of transaction fees. While prior art shows extensive analysis based on synthetic data generated through simulations, an analysis based on empirical data from a deployed IOTA network is still missing. In this paper, we provide the first comprehensive analysis by using real transaction data officially published by IOTA Foundation. Our key finding is that neither the tangle's topological features nor the actual observed performance is consistent with the main conclusions from the literature. In particular, most of transactions take roughly 10 minutes to be officially confirmed, which is not exactly instant as commonly assumed; yet, what is arguably worse is that there is a certain amount (5%) of transactions experiencing exceptionally long confirmation time. This shows that IOTA still has gaps to meet the stringent requirements of IoT applications that are delay sensitive.

Demanding IoT application requirements such as high dependability and low latency, cannot be satisfied by centralized cloud computing when deploying these applications. Edge computing is emerging as an alternative to deploy demanding IoT applications closer to the edge of the network. However, with edge computing, available resources are distributed among different resource-constrained devices, which cannot host large monolithic applications. We propose a new hierarchical IoT application model, suitable for the distributed nature of edge computing. Thus, a task in the application is modeled using multiple configurations of smaller tasks, each with their own functionality level and resource requirements. For deployment we use a decentralized resource technical framework that finds a satisfiable task mapping on edge devices. Its functionality is inspired by an auction house, having the objectives of (i) deploying an application such that its requirements are met and (ii) empowers edge devices to be in control of their available resources. For the latter, we propose a new decision policy to help edge devices take better decisions regarding the use of local available resource. Our solution enables efficient device resource utilization when deploying IoT applications at the edge.

Engineering Internet of Things (IoT) systems is a challenging task partly due to the dynamicity and uncertainty of the environment including the involvement of the human in the loop. Users should be able to achieve their goals seamlessly in different environments, and IoT systems should be able to cope with dynamic changes. Several approaches have been proposed to enable the automated formation, enactment, and self-adaptation of goal-driven IoT systems. However, they do not address deployment issues. In this paper, we propose a goal-driven approach for deploying self-adaptive IoT systems in the Edge-Cloud continuum. Our approach supports the systems to cope with the dynamicity and uncertainty of the environment including changes in their deployment topologies, i.e., the deployment nodes and their interconnections. We describe the architecture and processes of the approach and the simulations that we conducted to validate its feasibility. The results of the simulations show that the approach scales well when generating and adapting the deployment topologies of goal-driven IoT systems in smart homes and smart buildings.

Along with the rapid developments in communication technologies and the surge in the use of mobile devices, a brand-new computation paradigm, edge computing, is surging in popularity. Meanwhile, the artificial intelligence (AI) applications are thriving with the breakthroughs in deep learning and the many improvements in hardware architectures. Billions of data bytes, generated at the network edge, put massive demands on data processing and structural optimization. Thus, there exists a strong demand to integrate edge computing and AI, which gives birth to edge intelligence. In this article, we divide edge intelligence into AI for edge (intelligence-enabled edge computing) and AI on edge (artificial intelligence on edge). The former focuses on providing more optimal solutions to key problems in edge computing with the help of popular and effective AI technologies while the latter studies how to carry out the entire process of building AI models, i.e., model training and inference, on the edge. This article provides insights into this new interdisciplinary field from a broader perspective. It discusses the core concepts and the research roadmap, which should provide the necessary background for potential future research initiatives in edge intelligence.

Summary With the proliferation of Internet of Things (IoT) and edge computing paradigms, billions of IoT devices are being networked to support data‐driven and real‐time decision making across numerous application domains, including smart homes, smart transport, and smart buildings. These ubiquitously distributed IoT devices send the raw data to their respective edge device (eg, IoT gateways) or the cloud directly. The wide spectrum of possible application use cases make the design and networking of IoT and edge computing layers a very tedious process due to the: (i) complexity and heterogeneity of end‐point networks (eg, Wi‐Fi, 4G, and Bluetooth); (ii) heterogeneity of edge and IoT hardware resources and software stack; (iv) mobility of IoT devices; and (iii) the complex interplay between the IoT and edge layers. Unlike cloud computing, where researchers and developers seeking to test capacity planning, resource selection, network configuration, computation placement, and security management strategies had access to public cloud infrastructure (eg, Amazon and Azure), establishing an IoT and edge computing testbed that offers a high degree of verisimilitude is not only complex, costly, and resource‐intensive but also time‐intensive. Moreover, testing in real IoT and edge computing environments is not feasible due to the high cost and diverse domain knowledge required in order to reason about their diversity, scalability, and usability. To support performance testing and validation of IoT and edge computing configurations and algorithms at scale, simulation frameworks should be developed. Hence, this article proposes a novel simulator IoTSim‐Edge, which captures the behavior of heterogeneous IoT and edge computing infrastructure and allows users to test their infrastructure and framework in an easy and configurable manner. IoTSim‐Edge extends the capability of CloudSim to incorporate the different features of edge and IoT devices. The effectiveness of IoTSim‐Edge is described using three test cases. Results show the varying capability of IoTSim‐Edge in terms of application composition, battery‐oriented modeling, heterogeneous protocols modeling, and mobility modeling along with the resources provisioning for IoT applications.

The Internet of Things (IoT) is becoming a backbone of sensing infrastructure to several mission-critical applications such as smart health, disaster management, and smart cities. Due to resource-constrained sensing devices, IoT infrastructures use Edge datacenters (EDCs) for real-time data processing. EDCs can be either static or mobile in nature, and this article considers both of these scenarios. Generally, EDCs communicate with IoT devices in emergency scenarios to evaluate data in real-time. Protecting data communications from malicious activity becomes a key factor, as all the communication flows through insecure channels. In such infrastructures, it is a challenging task for EDCs to ensure the trustworthiness of the data for emergency evaluations. The current communication security pattern of “communication before authentication” leaves a “black hole” for intruders to become part of communication processes without authentication. To overcome this issue and to develop security infrastructures for IoT and distributed Edge datacenters, this article proposes a user-centric security solution. The proposed security solution shifts from a network-centric approach to a user-centric security approach by authenticating users and devices before communication is established. A trusted controller is initialized to authenticate and establishes the secure channel between the devices before they start communication between themselves. The centralized controller draws a perimeter for secure communications within the boundary. Theoretical analysis and experimental evaluation of the proposed security model show that it not only secures the communication infrastructure but also improves the overall network performance.

This Special Section aims at providing a platform for sharing the state-of-the-art research and development on end-edge-cloud orchestration and publishing original research and peer-reviewed articles targeted to all readers of IEEE Transactions on Industrial Informatics. The content of the special issue focus on several topics that are recently concerned in the community, including the architectures and implementations, communication and networking protocols, computation offloading strategies, advanced machine learning and data analytical methods, performance modeling and optimization, and other enabling technologies for end-edge-cloud orchestrated systems and its industrial applications.

No abstract available.

Microservices have emerged as a new approach for developing and deploying cloud applications that require higher levels of agility, scale, and reliability. To this end, a microservice-based cloud application architecture advocates decomposition of monolithic application components into independent software components called "microservices". As the independent microservices can be developed, deployed, and updated independently of each other, it leads to complex run-time performance monitoring and management challenges. To solve this problem, we propose a generic monitoring framework, Multi-microservices Multi-virtualization Multi-cloud (M3) that monitors the performance of microservices deployed across heterogeneous virtualization platforms in a multi-cloud environment. We validated the efficacy and efficiency of M3 using a Book-Shop application executing across AWS and Azure.

Privacy and Security are one of the major research topics regarding the Internet of Things (IoT). Due to the vast amount of devices collecting and processing sensitive data, anonymity and privacy mechanism are needed. Source Location Privacy (SLP) plays a key role in prohibiting adversaries from tracing back this kind of data to its origin. In this paper we propose a SLP preserving system that leverages techniques from the well established Onion Routing paradigm. The system is specifically designed for resource constrained IoT devices, i.e., devices lacking computing power. It features combined encryption schemes and symmetric key exchanges via Elliptic-Curve Diffie- Hellman (ECDH). Our performance measurements, conducted on typical resource constrained IoT devices, show the feasibility of ORIOT and facilitate the integration into existing or planned IoT systems, depending on SLP features.

Edge AI and Human Augmentation are two major technology trends, driven by recent advancements in edge computing, IoT, and AI accelerators. As humans, things, and AI continue to grow closer together, systems engineers and researchers are faced with new and unique challenges. In this paper, we analyze the role of edge computing and AI in the cyber-human evolution, and identify challenges that edge computing systems will consequently be faced with. We take a closer look at how a cyber-physical fabric will be complemented by AI operationalization to enable seamless end-to-end edge intelligence systems.

Internet of Things (IoT) systems open up massive versatility and opportunity to our world. Providing solutions for smart cities, healthcare, energy, and mobility, such systems increasingly permeate critical aspects of human activity. In a flourish of growth, these complex systems run software, are dynamic, without stable spatial and temporal boundaries, and involve mostly independent software components with different lifespans and evolution models. IoT systems provide data-centric, device-centric and service-centric functionalities that are subject to continuous disruption, under limitations such as resource-constrained devices, platforms heterogeneity, deployment in adverse environments and administrative domains. As these systems evolve and gain complexity, resilience becomes a crucial system property. Bolstering resilience entails understanding and systematically managing dynamic behavior and decentralizing operations. We advocate that to systematically engineer resilience in IoT systems, a complete rethink is necessary regarding their design and operation. In this paradigm shift, systems demand conceptual frameworks, techniques, and mathematically-backed formalisms to treat change and achieve decentralization. We outline a vision for addressing fundamental challenges that software engineering and distributed systems research encounters when building resilient IoT systems. Within a roadmap, we identify techniques and methods that can be leveraged to maintain resilience in the face of disruption, especially in the absence of central control and persistently at the system's runtime.

Edge computing has been recently introduced as an intermediary between Internet of Things (IoT) deployments and the cloud, providing data or control facilities to participating IoT devices. This includes actively supporting IoT resource discovery, something particularly pertinent when building large-scale, distributed and heterogeneous IoT systems. Moreover, edge devices supporting resource discovery are required to meet the stringent requirements prevalent in IoT systems including high availability, low-latency, and privacy. To this end, we present a resource discovery platform for IoT resources situated at the edge of the network. Our approach aims at providing a seamless discovery process that is able to (i) extend the covered area by deploying additional edge nodes and (ii) assist in the development of new IoT applications that target already available resources. Within our proposed platform, devices located in a certain proximity connect and form an edge-to-edge network that we call an edge neighborhood - our edge-to-edge metadata replication platform enables participating devices to discover available resources. Our solution is characterized by absence of centralization, as edge nodes exchange metadata about available resources within their scope in a peer-to-peer manner.

This invited talk explores the research challenges in the domain of IoT from multiple angles and reflects on the urgently needed collective efforts from various research communities to collaborate on those. Our approach fundamentally challenges the current understanding of scientific, technological, and political paradigms in tackling the engineering of large-scale IoT systems. We discuss technical paradigms and research challenges in the domains of Cloud and Edge Computing as well as the requirements of people in such systems embedded in Smart Cities.

IoT systems are becoming an increasingly important component of the civil and industrial infrastructure. With the growth of these IoT ecosystems, their complexity is also growing exponentially. In this paper we explore the problem of testing and evaluating large scale IoT systems at design time. To this end we employ simulated sensors with the physical and geographical characteristics of real sensors. Moreover, we propose Sensyml, a simulation environment that is capable of generating big data from cyber-physical models and real-world data. To the best of our knowledge it is the first approach to use a hybrid integration of real and simulated sensor data, that is also capable of being integrated into existing IoT systems. Sensyml is a cloud based Infrastructure-as-a-Service (IaaS) system that enables users to test both functionality and scalability of their IoT applications.

Comprehensive privacy mechanisms are essential in the pervasive internet-of-things systems of today, which are comprised of multiple distributed devices and diverse software stacks, while located in different legal or administrative domains. In such systems, often consisting of resource-constrained devices, guarantees of correctness and conformance to privacy policies is required, while data need to be synchronized among different software components. Motivated by the "data protection by design and by default" principle, we propose a technical framework to support data synchronization among edge components tailored for pervasive IoT applications. Our privacy-driven synchronization approach is based on a generically applicable privacy model and able to capture roles and permissions, actions on data, conditions and obligations that arise in privacy requirements. For automated and correct reflection of synchronized data among components, we adopt bidirectional transformations, a mechanism where synchronization between models, consistency, and well-behavedness are formally guaranteed. Thus, automatically generated privacy-aware data transformations are correct by construction. We evaluate POET, our framework and accompanying tool with a case study on medical information privacy and demonstrate its performance in resource-constrained edge devices.

Summary Complex systems such as Collective Adaptive Systems that include a variety of resources, are increasingly being designed to include people in task‐execution. Collectives, encapsulating human resources/services, represent one type of an application within which people with different type of skills can be engaged to solve one common problem or work on the same project. Mechanisms of managing social collectives are dependent on functional and non‐functional parameters of members of social collectives. In this work, we investigate the benefits provenance can offer to social computing and trade‐off implications. We show experimental results of how provenance data can help better visualize interaction and performance data during a collective's run‐time. We present novel metrics that can be derived from provenance, and lastly, we discuss privacy implications. If utilized ethically, provenance can help in developing more efficient provisioning and management mechanisms in social computing.

Mobile augmented reality (Mobile AR) is gaining increasing attention from both academia and industry. Hardware-based Mobile AR and App-based Mobile AR are the two dominant platforms for Mobile AR applications. However, hardware-based Mobile AR implementation is known to be costly and lacks flexibility, while the App-based one requires additional downloading and installation in advance and is inconvenient for cross-platform deployment. In comparison, Web-based AR (Web AR) implementation can provide a pervasive Mobile AR experience to users thanks to the many successful deployments of the Web as a lightweight and cross-platform service provisioning platform. Furthermore, the emergence of 5G mobile communication networks has the potential to enhance the communication efficiency of Mobile AR dense computing in the Web-based approach. We conjecture that Web AR will deliver an innovative technology to enrich our ways of interacting with the physical (and cyber) world around us. This paper reviews the state-of-the-art technology and existing implementations of Mobile AR, as well as enabling technologies and challenges when AR meets the Web. Furthermore, we elaborate on the different potential Web AR provisioning approaches, especially the adaptive and scalable collaborative distributed solution which adopts the osmotic computing paradigm to provide Web AR services. We conclude this paper with the discussions of open challenges and research directions under current 3G/4G networks and the future 5G networks. We hope that this paper will help researchers and developers to gain a better understanding of the state of the research and development in Web AR and at the same time stimulate more research interest and effort on delivering life-enriching Web AR experiences to the fast-growing mobile and wireless business and consumer industry of the 21st century.

Software components within heterogeneous devices of the Internet of Things (IoT) systems use resources representing various computational capabilities, including sensing or actuation end points. However, components do not live in isolation and must be able to coordinate with others to fulfill their goals. Satisfaction of requirements-capturing their goals-must persist in environments that are changing, unpredictable, and potentially unknown at system design time. Edge computers placed near IoT devices can be leveraged for this sort of control-providing resource management for end devices within their operational context. We propose a methodology and technical framework for engineering resource coordination at runtime, tailored for the decentralized, pervasive systems of today. Our approach represents a paradigm shift in marrying distributed systems and formal aspects of software engineering. We adopt goal modeling to capture objectives within the system and use bounded model checking as the foundational technique to compute coordination plans that satisfy device goals. This occurs opportunistically at runtime without any knowledge about the operational status or presence of resources, but always in accordance with the edge's own goals. Our technical framework exhibits dependability guarantees regarding optimality and correctness of generated plans. We evaluate the resource coordination performance and its feasibility on low-powered ARM-based edge devices.

Summary With an ever‐increasing variety and complexity of Internet of Things (IoT) applications delivered by increasing numbers of service providers, there is a growing demand for an automated mechanism that can monitor and regulate the interaction between the parties involved in IoT service provision and delivery. This mechanism needs to take the form of a contract, which, in this context, is referred to as a service level agreement (SLA). As a first step toward SLA monitoring and management, an SLA specification is essential. We believe that current SLA specification formats are unable to accommodate the unique characteristics of the IoT domain, such as its multilayered nature. Therefore, we propose a grammar for a syntactical structure of an SLA specification for IoT. The grammar is built based on a proposed conceptual model that considers the main concepts that can be used to express the requirements for hardware and software components of an IoT application on an end‐to‐end basis. We followed the goal question metric approach to evaluate the generality and expressiveness of the proposed grammar by reviewing its concepts and their predefined lists of vocabularies against two use cases with a considerable number of participants whose research interests are mainly related to IoT. The results of the analysis show that the proposed grammar achieved 91.70% of its generality goal and 93.43% of its expressiveness goal.

No abstract available.

Individuals, organizations, and government agencies are increasingly relying on Internet-enabled collaboration among distributed teams of humans, computer applications, and autonomous entities such as robots to develop products and deliver services. Technology trends in areas such as networking, data analytics, and distributed systems have significantly shifted the landscape of Internet-based collaborative tools and services. This particular special issue contains articles describing novel and innovative Internet-based collaborative technologies that leverage emerging technologies and enable seamless collaboration.

Publish-subscribe middleware is a popular technology for facilitating device-to-device communication in large-scale distributed Internet of Things (IoT) scenarios. However, the stringent quality of service (QoS) requirements imposed by many applications cannot be met by cloud-based solutions alone. Edge computing is considered a key enabler for such applications. Client mobility and dynamic resource availability are prominent challenges in edge computing architectures. In this paper, we present EMMA, an edge-enabled publish-subscribe middleware that addresses these challenges. EMMA continuously monitors network QoS and orchestrates a network of MQTT protocol brokers. It transparently migrates MQTT clients to brokers in close proximity to optimize QoS. Experiments in a real-world testbed show that EMMA can significantly reduce end-to-end latencies that incur from network link usage, even in the face of client mobility and unpredictable resource availability.

Recent technological and societal developments, reflected in the appearance of Internet of Things, Cloud Computing, Crowdsourcing and the shift towards a sharing economy, put the humans in the position not only to consume the services, provide data or execute simple (computational) tasks, but also to actively engage and shape the hybrid collaborative activities. These changes are opening up the possibilities for novel forms of interaction, collaboration and organization of labor. This becomes especially relevant in the context of the Smart City, where the focus is shifting from optimizing physical infrastructure and resource savings to include empowerment of citizens and support for neighborhood-scale complex/creative human collaborations. The expectation is that such collective activities can bring a disruptive change to the society. In this paper we present our vision for initiating and managing socially-driven collaborations in a Smart City context by considering research challenges related primarily to the human-centric aspects of the said collaborations.

The papers in this special issue are designed to solicit innovative and promising methods and techniques related closely to cloud services in the era of Big Data. The concept of Cloud Service represents a prime facility and feature of services in a cloud computing environment that can be made available to users on demand. Due to the flexibility of cloud computing in scaling IT resources up and down, cloud services gradually become valuable to attract the gaze of researchers and engineers from both academia and industry when they are faced with dynamically changing business requirements. Different stakeholders, such as consumers, providers, and operators, are generating a vast amount of data on such services per minute on the Internet, which increasingly comes to show the “4V” characteristics of big data. Therefore, new methodologies and techniques are urgently required for designing, validating, developing, testing, and deploying cloud services on demand in this specific scenario based on big data, as well as for efficiently being adaptive to business dynamics and users’ explicit and implicit requirements.

This is the second part of a special issue on "Cloud Services Meet Big Data" organized to solicit innovative and promising methods and techniques related closely to cloud services in the era of Big Data. The seven papers included in this special investigate the most challenging issues in the areas of IaaS (Infrastructure as a Service) design and operations, requirements engineering and knowledge engineering for cloud services development, and service search and discovery.

Applications in the Internet of Things (IoT) domain need to integrate and manage large numbers of heterogenous devices. Traditionally, such devices are treated as external dependencies that reside at the edge of the infrastructure and mainly transmit sensed data or react to their environment. Recently however, a fundamental shift in the basic nature of these devices is taking place. More and more IoT devices emerge that are not only simple sensors or transmitters, but provide limited execution environments. This opens up an opportunity to utilize this previously untapped processing power in order to offload parts of the application logic directly to these edge devices. To effectively exploit this new type of device, the design of IoT applications needs to change to explicitly consider devices that are deployed in the edge of the infrastructure. This will not only increase the overall flexibility and robustness of IoT applications, but also reduce costs by cutting down expensive communication overhead. Therefore, to allow the flexible provisioning of applications whose deployment topology evolves over time, a clear separation of independently executable application components is needed. In this paper, we present a framework for the dynamic generation of optimized deployment topologies for IoT cloud applications that are tailored to the currently available physical infrastructure. Based on a declarative, constraint-based model of the desired application deployment, our approach enables flexible provisioning of application components on edge devices deployed in the field. Using our framework, applications can furthermore evolve their deployment topologies at runtime in order to react on environmental changes, such as changing request loads. Our framework supports different IoT application topologies and we show that our solution elastically provisions application deployment topologies using a cloud-based testbed.

Network Neutrality is essential for ensuring a level playing field for the development of new applications and services on the Internet. Laws and rules alone might not be enough to protect innovation, fair competition and consumer's freedom of choice online. The research community has the responsibility to propose solutions that reveal discriminatory traffic management mechanisms on the Internet. We present the potential risks of a non-neutral Internet, identify several open challenges for designing solutions that detect traffic differentiation, and propose a model that addresses such challenges by taking advantage of distributed systems technologies.

Fog computing provides a decentralized approach to data processing and resource provisioning in the Internet of Things (IoT). Particular challenges of adopting fog-based computational resources are the adherence to geographical distribution of IoT data sources, the delay sensitivity of IoT services, and the potentially very large amounts of data emitted and consumed by IoT devices. Despite existing foundations, research on fog computing is still at its very beginning. A major research question is how to exploit the ubiquitous presence of small and cheap computing devices at the edge of the network in order to successfully execute IoT services. Therefore, in this paper, we study the placement of IoT services on fog resources, taking into account their QoS requirements. We show that our optimization model prevents QoS violations and leads to 35% less cost of execution if compared to a purely cloud-based approach.

Recent emergence of IoT Cloud systems has fostered proliferation of various applications mainly driven by urgent need to respond to volume, velocity and variety of data generated by IoT Cloud, but also to enable timely propagation of actuation decisions, crucial for business operation, to the Edge of the infrastructure. In such systems, utilizing currently untapped Edge resources such as sensory gateways, and enabling the IoT devices as first-class execution environments plays a crucial role. However, enabling virtually exclusive access to the underlying devices, e.g., field bus sensors and supporting flexible, application-specific customizations for such devices still remain a challenge. In this paper, we introduce Data- and Control Points - a novel programming model and framework for developing applications specifically tailored for resource-constrained Edge devices. Our framework offers programming constructs that enable applications to define custom configurations for and their own view of the underlying devices. By providing an illusion of an exclusive access to the underlying sensors and actuators, our framework supports execution of multiple applications within a single Edge device.

The serverless paradigm has been rapidly adopted by developers of cloud-native applications, mainly because it relieves them from the burden of provisioning, scaling and operating the underlying infrastructure. In this paper, we propose a novel computing paradigm - Deviceless Edge Computing that extends the serverless paradigm to the edge of the network, enabling IoT and Edge devices to be seamlessly integrated as application execution infrastructure. We also discuss open challenges to realize Deviceless Edge Computing, based on our experience in prototyping a deviceless platform.

Summary A large number of cloud middleware platforms and tools are deployed to support a variety of internet‐of‐things (IoT) data analytics tasks. It is a common practice that such cloud platforms are only used by its owners to achieve their primary and predefined objectives, where raw and processed data are only consumed by them. However, allowing third parties to access processed data to achieve their own objectives significantly increases integration and cooperation and can also lead to innovative use of the data. Multi‐cloud, privacy‐aware environments facilitate such data access, allowing different parties to share processed data to reduce computation resource consumption collectively. However, there are interoperability issues in such environments that involve heterogeneous data and analytics‐as‐a‐service providers. There is a lack of both architectural blueprints that can support such diverse, multi‐cloud environments and corresponding empirical studies that show feasibility of such architectures. In this paper, we have outlined an innovative hierarchical data‐processing architecture that utilises semantics at all the levels of IoT stack in multi‐cloud environments. We demonstrate the feasibility of such architecture by building a system based on this architecture using OpenIoT as a middleware, and Google Cloud and Microsoft Azure as cloud environments. The evaluation shows that the system is scalable and has no significant limitations or overheads. Copyright © 2016 John Wiley & Sons, Ltd.

Summary Smart city applications generate large amounts of operational data during their execution, such as information from infrastructure monitoring, performance and health events from used toolsets, and application execution logs. These data streams contain vital information about the execution environment that can be used to fine‐tune or optimize different layers of a smart city application infrastructure. Current approaches do not sufficiently address the efficient collection, processing, and storage of this information in the smart city domain. In this paper, we present Ahab, a generic, scalable, and fault‐tolerant data processing framework based on the cloud that allows operators to perform online and offline analyses on gathered data to better understand and optimize the behavior of the available smart city infrastructure. Ahab is designed for easy integration of new data sources, provides an extensible API to perform custom analysis tasks, and a domain‐specific language to define adaptation rules based on analysis results. We demonstrate the feasibility of the proposed approach using an example application for autonomous intersection management in smart city environments. Our framework is able to autonomously optimize application deployment topologies by distributing processing load over available infrastructure resources when necessary based on both online analysis of the current state of the environment and patterns learned from historical data. Copyright © 2016 John Wiley & Sons, Ltd.

Emerging trends like Big Data and the Internet of Things pose new challenges to established data stream processing engines. Especially, with the advent of the Internet of Things, the data that has to be processed can become very large. Since companies usually aim for cost efficiency, engines need to support resource elasticity to minimize the operational cost while maintaining real-time processing capabilities. In the work at hand, we propose and realize the distributed Platform for Elastic Stream Processing (PESP). An extensive evaluation demonstrates the practical feasibility and efficiency of the system design. The evaluation shows that PESP is able to reduce cost by 20% with minimal effects on the Quality of Service in comparison to an over-provisioning baseline. Compared to an under-provisioning baseline, PESP allows a Quality of Service improvement of 72%.

The Internet of Things is getting more and more traction, nevertheless, state-of-the-art approaches only focus on specific aspects, like the integration of heterogeneous devices or the processing of sensor data emitted by these devices. However, such domain-specific approaches slow the adoption rate of the Internet of Things, because users need to select and integrate different approaches in order to build a solution that fits all their requirements. To resolve this shortcoming, we have designed and implemented the VISP ecosystem, which provides a holistic approach for elastic data stream processing in Internet of Things scenarios by supporting the complete lifecycle of designing, deploying, and executing such scenarios. VISP further tackles the challenges of data privacy as well as software reuse, including monetization aspects in today's service landscapes. This paper analyzes challenges for creating solutions for the Internet of Things, presents the VISP ecosystem, and discusses its applicability for use case specific data stream processing topologies.

Scalable applications deployed in public clouds can be built from a combination of custom software components and public cloud services. To meet performance and/or cost requirements, such applications can scale-out/in their components during run-time. When higher performance is required, new component instances can be deployed on newly allocated cloud services (e.g., virtual machines). When the instances are no longer needed, their services can be deallocated to decrease cost. However, public cloud services are usually billed over predefined time and/or usage intervals, e.g., per hour, per GB of I/O. Thus, it might not be cost efficient to scale-in public cloud applications at any moment in time, without considering their billing cycles. In this work we aid developers of scalable applications for public clouds to monitor their costs, and develop cost-aware scalability controllers. We introduce a model for capturing the pricing schemes of cloud services. Based on the model we determine and evaluate the application's costs depending on its used cloud services and their billing cycles. We further evaluate cost efficiency of cloud applications, analyzing which application component is cost efficient to deallocate and when. We evaluate our approach on a scalable platform for IoT, deployed in Flexiant, one of the leading European public cloud providers. We show that cost-aware scalability can achieve higher application stability and performance, while reducing its operation costs.

The rise of elastically scaling applications that frequently deploy new machines has led to the adoption of DevOps practices across the cloud engineering stack. So-called configuration management tools utilize scripts that are based on declarative resource descriptions and make the system converge to the desired state. It is crucial for convergent configurations to be able to gracefully handle transient faults, e.g., network outages when downloading and installing software packages. In this paper we introduce a conceptual framework for asserting reliable convergence in configuration management. Based on a formal definition of configuration scripts and their resources, we utilize state transition graphs to test whether a script makes the system converge to the desired state under different conditions. In our generalized model, configuration actions are partially ordered, often resulting in prohibitively many possible execution orders. To reduce this problem space, we define and analyze a property called preservation, and we show that if preservation holds for all pairs of resources, then convergence holds for the entire configuration. Our implementation builds on Puppet, but the approach is equally applicable to other frameworks like Chef, Ansible, etc. We perform a comprehensive evaluation based on real world Puppet scripts and show the effectiveness of the approach. Our tool is able to detect all idempotence and convergence related issues in a set of existing Puppet scripts with known issues as well as some hitherto undiscovered bugs in a large random sample of scripts.

Recent developments of computing systems allow humans to participate not only as service consumers but also as service providers. The interweaving of human-based computing into machine-based computing systems becomes apparent in smart city settings, where human-based services together with software-based services and thing-based services (e.g., sensor-as-a-service) are orchestrated for solving complex problems, leading to the creation of the so-called Cyber-PhySical-Social Systems (CPSSs). Monitoring such CPSSs is essential for system planning, management, and governance. However, due to the diversity of the involved building blocks, it is challenging to monitor such systems. In this paper, we present metric models and the associated Quality of Data (QoD) to elastically monitor the execution metrics of a centralized coordinated CPSS. We develop a monitoring framework for capturing and analyzing runtime metrics occurring on various facets of the coordinated CPSS. Furthermore, we present the implementation of our monitoring framework, and showcase monitoring features in a simulated system using real world infrastructure maintenance scenarios.

This article introduces a dynamic cloud-based marketplace of near-realtime human sensing data (MARSA) for different stakeholders to sell and buy near-realtime data. MARSA is designed for environments where information technology (IT) infrastructures are not well developed but the need to gather and sell near-realtime data is great. To this end, we present techniques for selecting data types and managing data contracts based on different cost models, quality of data, and data rights. We design our MARSA platform by leveraging different data transferring solutions to enable an open and scalable communication mechanism between sellers (data providers) and buyers (data consumers). To evaluate MARSA, we carry out several experiments with the near-realtime transportation data provided by people in Ho Chi Minh City, Vietnam, and simulated scenarios in multicloud environments.

Cloud applications can benefit from the on-demand capacity of cloud infrastructures, which offer computing and data resources with diverse capabilities, pricing, and quality models. However, state-of-the-art tools mainly enable the user to specify “if-then-else” policies concerning resource usage and size, resulting in a cumbersome specification process that lacks expressiveness for enabling the control of complex multilevel elasticity requirements. In this article, first we propose SYBL, a novel language for specifying elasticity requirements at multiple levels of abstraction. Second, we design and develop the rSYBL framework for controlling cloud services at multiple levels of abstractions. To enforce user-specified requirements, we develop a multilevel elasticity control mechanism enhanced with conflict resolution. rSYBL supports different cloud providers and is highly extensible, allowing service providers or developers to define their own connectors to the desired infrastructures or tools. We validate it through experiments with two distinct services, evaluating rSYBL over two distinct cloud infrastructures, and showing the importance of multilevel elasticity control.

Internet of Things (IoT) devices are usually considered external application dependencies that only provide data or process and execute simple instructions. The recent emergence of IoT devices with embedded execution environments allows practitioners to deploy and execute custom application logic directly on the device. This approach fundamentally changes the overall process of designing, developing, deploying, and managing IoT systems. However, these devices exhibit significant differences in available execution environments, processing, and storage capabilities. To accommodate this diversity, a structured approach is needed to uniformly and transparently deploy application components onto a large number of heterogeneous devices. This is especially important in the context of large-scale IoT systems, such as in the smart city domain. In this article, we present LEONORE, an infrastructure toolset that provides elastic provisioning of application components on resource-constrained and heterogeneous edge devices in large-scale IoT deployments. LEONORE supports push-based as well as pull-based deployments. To improve scalability and reduce generated network traffic between cloud and edge infrastructure, we present a distributed provisioning approach that deploys LEONORE local nodes within the deployment infrastructure close to the actual edge devices. We show that our solution is able to elastically provision large numbers of devices using a testbed based on a real-world industry scenario.

Trust management is one of the most challenging issues for the adoption and growth of cloud computing. The highly dynamic, distributed, and non-transparent nature of cloud services introduces several challenging issues such as privacy, security, and availability. Preserving consumers' privacy is not an easy task due to the sensitive information involved in the interactions between consumers and the trust management service. Protecting cloud services against their malicious users (e.g., such users might give misleading feedback to disadvantage a particular cloud service) is a difficult problem. Guaranteeing the availability of the trust management service is another significant challenge because of the dynamic nature of cloud environments. In this article, we describe the design and implementation of CloudArmor, a reputation-based trust management framework that provides a set of functionalities to deliver trust as a service (TaaS), which includes i) a novel protocol to prove the credibility of trust feedbacks and preserve users' privacy, ii) an adaptive and robust credibility model for measuring the credibility of trust feedbacks to protect cloud services from malicious users and to compare the trustworthiness of cloud services, and iii) an availability model to manage the availability of the decentralized implementation of the trust management service. The feasibility and benefits of our approach have been validated by a prototype and experimental studies using a collection of real-world trust feedbacks on cloud services.

Business Process Management is a matter of great importance in different industries and application areas. In many cases, it involves the execution of resource-intensive tasks in terms of computing power such as CPU and RAM. Due to the emergence of Cloud computing, theoretically unlimited resources can be used for the enactment of business processes. These Cloud resources render several challenges for Business Process Management Systems to ensure a predefined Quality of Service level during Cloud-based process enactment. Therefore, new solutions for process scheduling and resource allocation are required to tackle these challenges. Within this paper, we present a novel approach to schedule business processes and optimize the used Cloud-based computational resources in a cost-efficient way, thus realizing so-called elastic processes. For that, we specify the Service Instance Placement Problem, i.e., an optimization model which defines the setting of how service instances are scheduled among resources. Through extensive evaluations we show the benefits of our contributions and compare the novel approach against a baseline which follows an ad hoc approach.

Cloud computing is becoming increasingly important for executing business processes. This development contributes to a novel class of Business Process Management Systems, called eBPMS, that inherit elasticity from cloud computing. The aim of eBPMS is to improve the efficiency of process enactment, in particular regarding scalability and cost-efficiency. However, there is hardly any research that investigates scheduling for eBPMS so far. Against this background, we design an elastic scheduling approach for eBPMS and a corresponding formal problem definition in order to evaluate its data transfer capabilities -- this is especially important for hybrid cloud environments. Through extensive evaluations, we are able to show that our approach reduces the total cost by a considerable share.

Emerging IoT cloud systems create unified IoT cloud infrastructures that offer large pools of elastic resources, which need to be governed through their entire lifecycle. However, numerous uncertainties are inherently present in such infrastructures, mainly due to the novel interactions of IoT elements, network elements, cloud resources and humans. They pose a plethora of challenges for the governance of such IoT cloud systems. In this paper we introduce U-GovOps -- a novel framework for dynamic, on-demand governance of elastic IoT cloud systems under uncertainty. We introduce a declarative policy language to simplifythe development of uncertainty-and elasticity-aware governance strategies. Based on that we develop runtime mechanisms, which enable mitigating the uncertainties by monitoring and governing the IoT cloud systems through specified strategies. We evaluate our approach using a real-life case study in the domain of predictive maintenance.

Presents a listing of the Symposium organizing committee.

There exists a huge amount of vertical applications that are developed for isolated computing environments. Due to increasing demand for additional resources there is a clear need to adapt these applications to the distributed environments. However, this is not an easy task and numerous variants are possible. Moreover, in this transition a new quality requirements become important, such as application elasticity. Application elasticity has to be built into a software system to enable smooth cost optimization at the run-time. In this paper, we provide a framework for evaluating different transformation variants of vertical Java EE multi-tiered applications into elastic cloud applications. With support of this framework the software developer is guided how to transform its application achieving optimal elasticity strategy. The framework is evaluated on slicing and evaluating elasticity of existing SaaS multi-tiered Java application used in Croatian market.

We propose a novel technique for improving the efficiency of cloud-based continuous integration development environments. Our technique identifies repetitive, expensive and time-consuming setup activities that are required to run integration and system tests in the cloud, and consolidates them into preconfigured testing virtual machines such that the overall costs of test execution are minimized. We create such testing machines by reconfiguring and opportunistically snapshotting the virtual machines already registered in the cloud.

The advent of the Smart City domain has led to the creation of massive amounts of diverse data. Stakeholders in this domain need to be able to analyze this data in order to make informed planning decisions. To address this complex task, Distributed Analytical Environments (DAEs) have emerged. These environments consist of different distributed analytical and data services, which are composed in a dynamic way to deliver insights that are crucial for stakeholders. Since these environments deal with business critical and sensitive information, strict compliance constraints apply. These constraints lead to situations where certain concrete services are not allowed to exchange data, even though their interaction is necessary to produce the desired results. Finding a valid solution in the space of possible instantiations is a non-trivial problem. In this paper we introduce Nomads, a framework that enables service mobility in such constrained dynamic composition environments to overcome aforementioned restrictions. The framework improves the overall satisfiability and therefore also the quality of constrained DAEs. We outline the requirements of a representative DAE scenario, provide a detailed problem formulation, and then discuss the service mobility framework along with our solution finding algorithm. The evaluation demonstrates that the Nomads framework considerably increases the number of successfully performed compositions even in highly constrained environments.

Developing and operating IoT cloud systems require novel features for deploying, controlling, monitoring and testing both IoT units and cloud services in an integrated environment spanning different infrastructures. In this paper, we demonstrate iCOMOT -- a novel toolset offering these features. Using iCOMOT we can perform various activities, such as dynamically reconfiguration of sensors, communication protocols, and cloud services in an elastic manner, suitable for testing and assuring quality of IoT cloud systems configurations. We will demonstrate our iCOMOT with a real-world predictive maintenance case study.

Contemporary cloud providers offer out-of-the-box auto-scaling solutions. However, defining a non-trivial scaling behavior that goes beyond the feature set provided by existing solutions is still challenging. In this paper we present SPEEDL, a declarative and extensible domain-specific language that simplifies the creation of elastic scaling behavior on top of IaaS clouds. SPEEDL simplifies the creation of event-driven policies for resource management (How many resources, and what resource types, are needed?), as well as task mapping (Which tasks should be handled by which resources?). Based on a dataset of real-life scaling policies, we demonstrate that SPEEDL can cover most scaling behaviors real-life developers want to express, and that the resulting SPEEDL policies are at the same time substantially more compact, easier to read, and less error-prone than the same behavior expressed via a general-purpose programming language.

To optimize the cost and performance of complex cloud services under dynamic requirements, workflows and diverse cloud offerings, we rely on different elasticity control processes. An elasticity control process, when being enforced, produces effects in different parts of the cloud service. These effects normally evolve in time and depend on workload characteristics, and on the actions within the elasticity control process enforced. Therefore, understanding the effects on the behavior of the cloud service is of utter importance for runtime decision-making process, when controlling cloud service elasticity. In this paper, we present a novel methodology and a framework for estimating and evaluating cloud service elasticity behaviors. To estimate the elasticity behavior, we collect information concerning service structure, deployment, service runtime, control processes, and cloud infrastructure. Based on this information, we utilize clustering techniques to identify cloud service elasticity behavior, in time, and for different parts of the service. Knowledge about such behavior is utilized within a cloud service elasticity controller to substantially improve the selection and execution of elasticity control processes. These elasticity behavior estimations are successfully being used by our elasticity controller, in order to improve runtime decision quality. We evaluate our framework with three real-world cloud services in different application domains. Experiments show that we are able to estimate the behavior in 89.5% of the cases. Moreover, we have observed improvements in our elasticity controller, which takes better control decisions, and does not exhibit control oscillations.

Building Infrastructure-as-a-Service (IaaS) applications today is a complex, repetitive, and error-prone endeavor, as IaaS does not provide abstractions on top of virtual machines. This article presents JC loud S cale , a Java-based middleware for moving elastic applications to IaaS clouds, with minimal adjustments to the application code. We discuss the architecture and technical features, as well as evaluate our system with regard to user acceptance and performance overhead. Our user study reveals that JC loud S cale allows many participants to build IaaS applications more efficiently, compared to industrial Platform-as-a-Service (PaaS) solutions. Additionally, unlike PaaS, JC loud S cale does not lead to a control loss and vendor lock-in.

Predictive business process monitoring aims at forecasting potential problems during process execution before they occur so that these problems can be handled proactively. Several predictive monitoring techniques have been proposed in the past. However, so far those prediction techniques have been assessed only independently from each other, making it hard to reliably compare their applicability and accuracy. We empirically analyze and compare three main classes of predictive monitoring techniques, which are based on machine learning, constraint satisfaction, and Quality-of-Service (QoS) aggregation. Based on empirical evidence from an industrial case study in the area of transport and logistics, we assess those techniques with respect to five accuracy indicators. We further determine the dependency of accuracy on the point in time during process execution when a prediction is made in order to determine lead-times for accurate predictions. Our evidence suggests that, given a lead-time of half of the process duration, all predictive monitoring techniques consistently provide an accuracy of at least 70%. Yet, it also becomes evident that the techniques differ in terms of how accurately they may predict violations and nonviolations. To improve the prediction process, we thus exploit the characteristics of the individual techniques and propose their combination. Based on our case study data, evidence indicates that certain combinations of techniques may outperform individual techniques with respect to specific accuracy indicators. Combining constraint satisfaction with QoS aggregation, for instance, improves precision by 14%; combining machine learning with constraint satisfaction shows an improvement in recall by 23%.

Cloud manufacturing refers to “as a Service” production model that exploits an on-demand access to a distributed pool of diversified manufacturing services and resources. It forms elastic and reconfigurable production lines, which enhance efficiency, by allowing optimal resource allocation in response to demand changes and market dynamics. This paper studies these challenges and proposes a portable cloud manufacturing platform, entitled “CloudMan”, aiming at achieving a portable deployment of cloud manufacturing services to any compliant distributed production line in the cloud. The stakeholders of CloudMan are detailed together with their API requirements, where each stakeholder has an interest in. Having this rigorous analysis in mind, we present a holistic architecture for CloudMan, as it considers the manufacturing data, material and event flow from sensors and shop floors, through services to end products. In architecting such platform, there is a lack of agreed standard for the portability and orchestration of manufacturing services, as well as their definition. The proposed platform incorporates OASIS Topology and Orchestration Specification for cloud Applications (TOSCA) policies, plans and templates as a mechanism for dynamic configuration, portability and deployment of manufacturing services across multiple collaborating manufacturers. Thereby, the architecture provides a set of abstraction levels for various types of manufacturing services in which encapsulates and addresses specific requirements to satisfy the needs of stakeholders.

Providers are incorporating novel techniques to cope with prospective aspects of trading like resource allocation over future demands and its pricing elasticity that was not foreseen before. To leverage the pricing elasticity of upcoming demand and supply, we employ financial option theory (future contracts) as a mechanism to alleviate the risk in resource allocation over future demands. This study introduces a novel Cloud Asset Pricing Tree (CAPT) model that finds the optimal premium price of the Cloud federation options efficiently. Providers will benefit by this model to make decisions when to buy options in advance and when to exercise them to achieve more economies of scale. The CAPT model adapts its structure to address the price elasticity concerns and makes the demand and provisioning, price inelastic. Our empirical evidences suggest that using the CAPT model, exploits the Cloud market potential as an opportunity for more resource utilization and future capacity planning.

Contemporary cloud services are constructed from different types of software and deployed on multiple cloud infrastructures, which offer various configuration options, and can change dynamically at runtime. Due to this complexity, such cloud services require substantial configuration efforts. Currently we lack techniques for automating the complex tasks and providing fine-grained configuration features for multi-cloud services. In this paper, we present a novel multi-level configuration approach for complex cloud services on multi-cloud environments. We develop techniques for automating configuration orchestration activities. Our solution enables the fine-grained configuration at different application abstraction levels and supports the dynamic change of cloud services at runtime. We provide the SALSA framework to implement our approach and demonstrate its usefulness with several real-world services.

With the increasing cloud popularity, substantial effort has been paid for the development of emerging elastic cloud services, consisting of different units distributed among virtual machines/containers in different clouds. Due to the software stack and deployment complexity in single and multi-cloud scenarios, developing and managing such services is impeded by a lack of tools and techniques for understanding the elasticity relationships among individual service units, which influence the service's overall elasticity. In this paper we characterize the elasticity relationships, and develop mechanisms for analyzing them, based on service monitoring information and elasticity requirements. From collected monitoring information we abstract the elasticity behavior of the whole cloud service and individual units, over which we design a customizable algorithm for relationships analysis. We illustrate our approach via several experiments with an elastic data service for M2M platforms, highlighting the importance of determining elasticity relationships for the development and operation of elastic services.

Today's crowdsourcing systems are predominantly used for processing independent tasks with simplistic coordination. As such, they offer limited support for handling complex, intellectually and organizationally challenging labour types, such as software development. In order to support crowd

Due to increasing competitive pressure, manufacturing companies need to support flexible and scalable business processes - both on the shop floor and in their enterprise software systems. Cloud manufacturing is a recent approach to realize real-world manufacturing processes by applying well-known basic concepts from the field of Cloud computing to this domain. To implement Cloud manufacturing, it is necessary to model, enact and monitor according manufacturing processes and virtualize the single process steps. So far, Business Process Management Systems do not explicitly support Cloud manufacturing. This paper analyzes requirements regarding process enactment for Cloud manufacturing and provides a concept for an according software framework.

Techniques for big data analytics should support principles of elasticity that are inherent in types of data and data resources being analyzed, computational models and computing units used for analyzing data, and the quality of results expected from the consumer. In this paper, we analyze and present these principles and their consequences for software-defined environments to support data analytics. We will conceptualize software-defined elastic systems for data analytics and present a case study in smart city management, urban mobility and energy systems with our elasticity supports.

Platform-as-a-Service (PaaS) should support the design, deployment, execution, test and monitoring of native elastic systems constructed from elastic service units based on multi-dimensional elasticity requirements. In this paper, we discuss fundamental building blocks for enabling multi-dimensional elasticity programming of software-defined elastic systems. We describe CoMoT, a novel PaaS for elasticity in the cloud that is developed based on these fundamental building blocks.

Modeling collaboration processes is a challenging task. Existing modeling approaches are not capable of expressing the unpredictable, non-routine nature of human collaboration, which is influenced by the social context of involved collaborators. We propose a modeling approach which considers collaboration processes as the evolution of a network of collaborative documents along with a social network of collaborators. Our modeling approach, accompanied by a graphical notation and formalization, allows to capture the influence of complex social structures formed by collaborators, and therefore facilitates such activities as the discovery of socially coherent teams, social hubs, or unbiased experts. We demonstrate the applicability and expressiveness of our approach and notation, and discuss their strengths and weaknesses.

SUMMARY The Cloud computing paradigm provides the basis for a class of platforms and applications that face novel challenges related to multi‐tenancy, adaptivity, and elasticity. To account for service delivery guarantees in the face of ever increasing levels of heterogeneity, scale, and dynamism, service provisioning in the Cloud has raised the demand for systematic and flexible approaches to monitoring and adaptation of applications. In this paper, we tackle this issue and present a framework for efficient runtime management of Cloud environments and distributed heterogeneous systems in general. A novel domain‐specific language termed MONINA is introduced that allows to define integrated monitoring and adaptation functionality for controlling such systems. We propose a mechanism for optimal deployment of the defined control operators onto available computing resources. Deployment is based on solving a quadratic programming problem, which aims at achieving minimized reaction times, low overhead, and scalable monitoring and adaptation. The monitoring infrastructure is based on a distributed messaging middleware, providing high level of decoupling and allowing new monitoring nodes to join the system dynamically. We provide a detailed formalization of the problem domain, discuss architectural details, highlight the implementation of the developed prototype, and put our work into perspective with existing work in the field. Copyright © 2014 John Wiley & Sons, Ltd.

Especially in large companies, business process landscapes may be made up from thousands of different process definitions and instances. As a result, a Business Process Management System (BPMS) needs to be able to handle the concurrent execution of a very large number of workflow steps. Many of these workflow steps may be resource-intensive, leading to ever-changing requirements regarding the needed computing resources to execute them. Using Cloud technologies, it is possible to allocate workflow steps to resources obtained on demand from Cloud platform providers. However, current BPMS do not feature the means to make use of Cloud resources in order to execute workflows. This work presents an approach to automatically lease and release Cloud resources for workflow executions based on knowledge about the current and future process landscape. This approach to self-adaptive resource allocation for elastic process execution is implemented as part of ViePEP, a research BPMS able to handle workflow executions in the Cloud.

Nowadays, IoT services are typically delivered as physically isolated vertical solutions, in which all system components ranging from sensory devices to applications are customized and tightly coupled for the requirements of each specific project. The efficiency and scalability of such service delivery model are intrinsically limited, posing significant challenges to IoT solution providers. Therefore, we propose a novel PaaS framework that provides essential platform services for IoT solution providers to efficiently deliver and continuously extend their services. This paper first introduces the IoT PaaS architecture, on which IoT solutions can be delivered as virtual verticals by leveraging computing resources and middleware services on cloud. Then we present the detailed mechanism and implementation of domain mediation, which helps solution providers to efficiently provide domain-specific control applications. The proposed approaches are demonstrated through the implementation of a domain mediator for building management and two use cases using the mediator.

Elasticity in cloud computing is a complex problem, regarding not only resource elasticity but also quality and cost elasticity, and most importantly, the relations among the three. Therefore, existing support for controlling elasticity in complex applications, focusing solely on resource scaling, is not adequate. In this paper we present SYBL - a novel language for controlling elasticity in cloud applications - and its runtime system. SYBL allows specifying in detail elasticity monitoring, constraints, and strategies at different levels of cloud applications, including the whole application, application component, and within application component code. Based on simple SYBL elasticity directives, our runtime system will perform complex elasticity controls for the client, by leveraging underlying cloud monitoring and resource management APIs. We also present a prototype implementation and experiments illustrating how SYBL can be used in real-world scenarios.

Cloud computing has enabled a wide array of applications to be exposed as elastic cloud services. While the number of such services has rapidly increased, there is a lack of techniques for supporting cross-layered multi-level monitoring and analysis of elastic service behavior. In this paper we introduce novel concepts, namely elasticity space and elasticity pathway, for understanding elasticity of cloud services, and techniques for monitoring and evaluating them. We present MELA, a customizable framework, which enables service providers and developers to analyze cross-layered, multi-level elasticity of cloud services, from the whole cloud service to service units, based on service structure dependencies. Besides support for real-time elasticity analysis of cloud service behavior, MELA provides several customizable features for extracting functions and patterns that characterize that behavior. To illustrate the usefulness of MELA, we conduct several experiments with a realistic data-as-a-service in an M2M cloud platform.

The process of engineering and provisioning service-based systems (SBS) follows a complex and dynamic lifecycle with different phases and levels of abstraction. We tackle the problem of making this lifecycle explicit, providing development time and runtime support for evolutionary changes in such systems. SBSs are modeled as integrated ecosystems consisting of four conceptual layers (or phases): design, implementation, deployment, and runtime. Our work is driven by the notion that identifying the right changes (monitoring) and effecting of these changes (adaptation) usually takes place individually on each layer. While considering changes on a single layer (e.g., runtime adaptation) is often sufficient, some cases require systematic escalation to adjacent layers. We present a generic lifecycle model that provides an abstracted view of the problem domain and can be mapped to concrete artifacts on each individual layer. We introduce a real-life scenario taken from the telecommunications domain, which serves as the basis for discussion of the challenges and our solution. Based on the scenario and our experience from a research project on Virtual Service Platforms, we evaluate three concrete use cases which illustrate the diversity of evolutionary changes supported by the approach.

Cloud-based elastic computing systems dynamically change their resources allocation to provide consistent quality of service and minimal usage of resources in the face of workload fluctuations. As elastic systems are increasingly adopted to implement business critical functions in a cost-efficient way, their reliability is becoming a key concern for developers. Without proper testing, cloud-based systems might fail to provide the required functionalities with the expected service level and costs. Using system testing techniques, developers can expose problems that escaped the previous quality assurance activities and have a last chance to fix bugs before releasing the system in production. System testing of cloud-based systems accounts for a series of complex and time demanding activities, from the deployment and configuration of the elastic system, to the execution of synthetic clients, and the collection and persistence of execution data. Furthermore, clouds enable parallel executions of the same elastic system that can reduce the overall test execution time. However, manually managing the concurrent testing of multiple system instances might quickly overwhelm developers' capabilities, and automatic support for test generation, system test execution, and management of execution data is needed. In this demo we showcase AUToCLES, our tool for automatic testing of cloud-based elastic systems. Given specifications of the test suite and the system under test, AUToCLES implements testing as a service (TaaS): It automatically instantiates the SUT, configures the testing scaffoldings, and automatically executes test suites. If required, AUToCLES can generate new test inputs. Designers can inspect executions both during and after the tests.

In this paper we propose a provider-managed, model-based adaptation approach for cloud computing applications, allowing customers to easily specify application behavior goals or adaptation rules. Delegating control over corrective actions to the cloud provider will pose advantages for both, customers and providers. Customers are relieved of effort and expertise requirements necessary to build sophisticated adaptation solutions, while providers can incorporate and analyze data from a multitude of customers to improve adaptation decisions. The envisioned approach will enable increased application performance, as well as cost savings for customers, whereas providers can manage their infrastructure more efficiently.

Elastic computing systems can dynamically scale to continuously and cost-effectively provide their required Quality of Service in face of time-varying workloads, and they are usually implemented in the cloud. Despite their wide-spread adoption by industry, a formal definition of elasticity and suitable procedures for its assessment and verification are still missing. Both academia and industry are trying to adapt established testing procedures for functional and non-functional properties, with limited effectiveness with respect to elasticity. In this paper we propose a new methodology to automatically generate test-suites for testing the elastic properties of systems. Elasticity, plasticity, and oscillations are first formalized through a convenient behavioral abstraction of the elastic system and then used to drive an iterative test suite refinement process. The outcomes of our approach are a test suite tailored to the violation of elasticity properties and a human-readable abstraction of the system behavior to further support diagnosis and fix.

Many query languages for graph-structured data are based on regular path expressions, which describe relations among pairs of nodes. We propose an extension that allows to retrieve groups of nodes based on group structural characteristics and relations to other nodes or groups. It allows to express group selection queries in a concise and natural style, and can be integrated into any query language based on regular path queries. We present an efficient algorithm for evaluating group queries in polynomial time from an input data graph. Evaluations using real-world social networks demonstrate the practical feasibility of our approach.

SUMMARY Cloud computing represents a novel on‐demand computing technology where resources are provisioned in compliance to a set of predefined non‐functional properties specified and negotiated by means of service level agreements (SLAs). Currently, cloud providers strive to achieve efficient SLA enforcement strategies to avoid costly SLA violations during application provisioning and to timely react to failures and environmental changes. These strategies include advanced application deployment mechanisms and appropriate resource monitoring concepts. In terms of cloud resource monitoring, providers tend to adopt existing monitoring tools, such as those from grid environments. However, those tools are usually restricted to locality and homogeneity of monitored objects, are not scalable, and do not support mapping of low‐level resource metrics (e.g., system uptime and downtime) to high‐level application‐specific SLA parameters (e.g., system availability). In this paper, we present a novel low‐level metrics to high‐level SLA (LoM2HiS) framework for managing the monitoring of low‐level resource metrics and mapping them to high‐level SLAs and an application deployment mechanism for scheduling and provisioning applications in clouds. The LoM2HiS framework provides the application deployment mechanism with monitored information and SLA violation prevention techniques, thereby ensuring the performance of the applications and thus increasing the revenue of the cloud provider by avoiding SLA violation penalty cost. This framework is the building block of the Foundations of Self‐governing ICT Infrastructures project, which intends to facilitate autonomic SLA management and enforcement. Thus, the LoM2HiS framework detects future SLA violation threats and can notify the knowledge component to act so as to avert the threats. We discuss in detail the conceptual design of the LoM2HiS framework and the application deployment mechanism including their implementations. Finally, we present our evaluation results based on a use‐case scenario demonstrating the usage of the LoM2HiS framework in a real cloud environment. Copyright © 2012 John Wiley & Sons, Ltd.

Service process orchestration using workflow technologies has led to significant improvements in generating predicable outcomes by automating tedious manual tasks but suffer from challenges related to the flexibility required in work especially when humans are involved. Recently emerging trends in enterprises to explore social computing concepts have realized value in more agile work process orchestrations but tend to be less predictable with respect to outcomes. In this paper, we use IT services management, specifically, incident management for large scale systems, to investigate the interplay of workflow systems and social computing. We apply a recently introduced concept of social compute units (SCU), and flexible teams sourced based on various parameters such as skills, availability, incident urgency, etc. in the context of resolution of incidents in an IT service provider organization. Results from simulation-based experiments indicate that the combination of SCUs and workflow based processes can lead to significant improvement in key service delivery outcomes, with average resolution time per incident and number of SLO violations being at times as low as 53.7% and 38.1%, respectively of the corresponding values for pure workflow based incident management. Moreover, significant benefits may also be obtained through cross-skilling of practitioners via exposure to new skills in the context of collaborative work.

For solving complex problems, in many cases, software alone might not be sufficient and we need hybrid systems of software and humans in which humans not only direct the software performance but also perform computing and vice versa. Therefore, we advocate constructing "social computers" which combine software and human services. However, to date, human capabilities cannot be easily programmed into complex applications in a similar way like software capabilities. There is a lack of techniques to conceptualize and program human and software capabilities in a unified way. In this paper, we explore a new way to virtualize, provision and program human capabilities using cloud computing concepts and service delivery models. We propose novel methods for conceptualizing and modeling clouds of human-based services (HBS) and combine HBS with software-based services (SBS) to establish clouds of hybrid services. In our model, we present common APIs, similar to well-developed APIs for software services, to access individual and team-based compute units in clouds of HBS. Based on that, we propose a framework for utilizing SBS and HBS to solve complex problems. We present several programming primitives for hybrid services, also covering forming hybrid solutions consisting of software and humans. We illustrate our concepts via some examples of using our cloud APIs and existing cloud APIs for software.

CONTEXT: A distributed business process is executed in a distributed computing environment. The service-oriented architecture (SOA) paradigm is a popular option for the integration of software services and execution of distributed business processes. Entailment constraints, such as mutual exclusion and binding constraints, are important means to control process execution. Mutually exclusive tasks result from the division of powerful rights and responsibilities to prevent fraud and abuse. In contrast, binding constraints define that a subject who performed one task must also perform the corresponding bound task(s). OBJECTIVE: We aim to provide a model-driven approach for the specification and enforcement of task-based entailment constraints in distributed service-based business processes. METHOD: Based on a generic metamodel, we define a domain-specific language (DSL) that maps the different modeling-level artifacts to the implementation-level. The DSL integrates elements from role-based access control (RBAC) with the tasks that are performed in a business process. Process definitions are annotated using the DSL, and our software platform uses automated model transformations to produce executable WS-BPEL specifications which enforce the entailment constraints. We evaluate the impact of constraint enforcement on runtime performance for five selected service-based processes from existing literature. RESULTS: Our evaluation demonstrates that the approach correctly enforces task-based entailment constraints at runtime. The performance experiments illustrate that the runtime enforcement operates with an overhead that scales well up to the order of several ten thousand logged invocations. Using our DSL annotations, the user-defined process definition remains declarative and clean of security enforcement code. CONCLUSION: Our approach decouples the concerns of (non-technical) domain experts from technical details of entailment constraint enforcement. The developed framework integrates seamlessly with WS-BPEL and the Web services technology stack. Our prototype implementation shows the feasibility of the approach, and the evaluation points to future work and further performance optimizations.

SUMMARY This paper addresses integration testing of data‐centric and event‐based dynamic service compositions. The compositions under test define abstract services that are replaced by concrete candidate services at runtime. Testing all possible instantiations of a composition leads to combinatorial explosion and is often infeasible. We consider data dependencies between services as potential points of failure and introduce the k‐node data flow test coverage metric, which helps to significantly reduce the number of test combinations. We formulate a combinatorial optimization problem for generating minimal sets of test cases. On the basis of this formalization, we present a mapping to the model of FoCuS, a coverage analysis tool. FoCuS efficiently computes near‐optimal solutions, which are used to automatically generate test instances. The proposed approach is applicable to various composition paradigms. We illustrate the end‐to‐end practicability based on an integrated scenario, which uses two diverse composition techniques: on the one hand, the Web Services Business Process Execution Language and on the other hand, WS‐Aggregation, a platform for event‐based service composition. Copyright © 2013 John Wiley & Sons, Ltd.

For providers of composite services, preventing cases of SLA violations is crucial. Previous work has established runtime adaptation of compositions as a promising tool to achieve SLA conformance. However, to get a realistic and complete view of the decision process of service providers, the costs of adaptation need to be taken into account. In this paper, we formalize the problem of finding the optimal set of adaptations, which minimizes the total costs arising from SLA violations and the adaptations to prevent them. We present possible algorithms to solve this complex optimization problem, and detail an end-to-end system based on our earlier work on the PREvent (prediction and prevention based on event monitoring) framework, which clearly indicates the usefulness of our model. We discuss experimental results that show how the application of our approach leads to reduced costs for the service provider, and explain the circumstances in which different algorithms lead to more or less satisfactory results.

Providers of applications deployed in an Infrastructure-as-a-Service cloud permanently face the decision of whether it is more cost-efficient to scale up(i.e., rent more resources from the cloud) or to delay incoming requests, even though doing so may lead to dissatisfied customers and broken service level agreements. This decision is further complicated by the fact that not all customers have the same agreements, and not all requests require the same amount of resources devoted to them. In this paper, we present an approach for optimally scheduling incoming requests to virtual computing resources in the cloud, so that the sum of payments for resources and loss incurred by service level agreement violations is minimized. We discuss our approach based on an illustrative use case. Furthermore, we present a numerical evaluation based on real-life request data, which shows that our agreement-aware algorithm improves upon earlier work, which does not take service level agreements into account.

Cloud computing based Data-as-a-Service (DaaS) has become popular. Several data assets have been released in DaaSes across different cloud platforms. Nevertheless, there are no well-defined ways to describe DaaSes and their associated data assets. On the one hand, existing DaaS providers simply use HTML documents to describe their service. This simple way of service description requires user to manually perform service lookup by reading the HTML documents to understand DaaSes as well as their provided data assets. On the other hand, existing service description techniques are not suitable for describing DaaSes because they consider only service information. The lack of well-structured/linked model to describe DaaSes hinders the automatic service lookup for DaaSes and the integration of DaaSes into data composition and analytic tools. In this paper, we propose DEMODS, a Description Model for DaaS, which introduces a general linked model to cover all basic information of a DaaS. Besides the basic DaaS description model, we also introduce an extended model that integrates existing work in describing quality of data, data and service contract, data dependency, and Quality of Service (QoS). We present a mechanism to incorporate DEMODS into both new and existing DaaSes. Finally, a prototype of DEMODS has been developed to evaluate the effectiveness of the proposed model.

Crowdsourcing in enterprises is a promising approach for organizing a flexible workforce. Recent developments show that the idea gains additional momentum. However, an obstacle for widespread adoption is the lack of an integrated way to execute business processes based on a crowdsourcing platform. T

In a model-driven service-oriented architecture (SOA), the services are in large parts generated from models. To facilitate monitoring, governance, and self-adaptation the information in these models can be used by services that monitor, manage, or adapt the SOA at runtime. If a service for monitoring, management, or adaptation in an SOA is dependent on models, and the metamodel changes, usually the service needs to be manually adapted to work with the new version, recompiled, and redeployed. This manual effort impedes the use of models at runtime. To address this problem, this paper introduces model-aware services that work with models at runtime. These services are supported using a service environment, called Morse. Hiding the complexity of implicit versioning of models from users while respecting the principle of Universally Unique Identifiers (UUIDs), it realizes a novel transparent UUID-based model versioning technique. It uses the model-driven approach to automatically generate and deploy Morse services that are used by the model-aware services to access models in the correct version. In this way, monitoring and adaptation in SOAs can be better supported, and the manual effort to evolve services for monitoring, management, or adaptation, which are based on models at runtime, can be minimized.

Cloud Computing infrastructures have been developed as individual islands, and mostly proprietary solutions so far. However, as more and more infrastructure providers apply the technology, users face the inevitable question of using multiple infrastructures in parallel. Federated cloud management systems offer a simplified use of these infrastructures by hiding their proprietary solutions. As the infrastructure becomes more complex underneath these systems, the situations (like system failures, handling of load peaks and slopes) that users cannot easily handle, occur more and more frequently. Therefore, federations need to manage these situations autonomously without user interactions. This paper introduces a methodology to autonomously operate cloud federations by controlling their behavior with the help of knowledge management systems. Such systems do not only suggest reactive actions to comply with established Service Level Agreements (SLA) between provider and consumer, but they also find a balance between the fulfillment of established SLAs and resource consumption. The paper adopts rule-based techniques as its knowledge management solution and provides an extensible rule set for federated clouds built on top of multiple infrastructures.

In this paper we study fault localization techniques for identification of incompatible configurations and implementations in service-based applications. We propose an approach using pooled decision trees for localization of faulty service parameter and binding configurations, explicitly addressing temporary and changing fault conditions.

In recent years, new business and research opportunities have been increasingly emerging in the field of large-scale context-aware pervasive systems (e.g. pervasive health-care, city traffic monitoring, environmental monitoring, smart grids). These large-scale pervasive systems are characterized by the need to employ large number of context sources, process massive amounts of real-time context data, provide services to numerous context-aware applications, and cope with higher volatility of the environment. This paper proposes the Origins Model — a programming model for context-aware applications in large-scale pervasive systems. In the Origins Model, an origin is an abstraction of any source of context information. Origins are universal, discoverable, composable, migratable, and replicable components that are associated with type and meta-information. They create an adequate foundation for the development of context-aware applications. Based on them, four processing operations are defined in the Origins Model: filter, infer, aggregate, and compose. As such, these operations provide a powerful mechanism to express a rich set of processing schemes in context-aware applications. Based on the Origins Model, we present the Origins Toolkit — a proof-of-concept implementation developed using the Scala programming language and the Akka toolkit to provide a distributed, scalable, and fault-tolerant solution.

Crowdsourcing is an increasingly used model to outsource certain tasks to be carried out by external experts on the Web. Especially when lacking experience or expertise with certain task types, crowdsourcing offers a convenient way to receive instant support. In this paper, we introduce an in-house enterprise crowdsourcing model, which leverages the crowdsourcing concept and transfers it to traditional organizations. Here, a company's staff is considered a crowd that — besides its regularly assigned tasks — can also receive tasks from colleagues from other departments and across hierarchical structures. The aim is to offer instant support and utilize free capacities throughout a large organization more efficiently. In our work, we describe this concept and supporting mechanisms in context of an agile software development use case. However, in contrast to usually crowdsourced microtasks, complex software architectures usually consist of tens and hundreds of connected modules that can be potentially crowdsourced. These technical dependencies between modules require active coordination and interactions between crowd members that process the single artifacts. Hence, technical dependencies of artifacts result in social dependencies of collaborating crowd members that create them. In order to efficiently discover member compositions based on artifact dependencies, we introduce an indexing and discovery approach based on subgraph matching. Typically, assigning tasks to well-rehearsed teams results in more reliable task processing, faster results, and higher quality of work. We evaluate our approach in terms of system scalability and overall applicability by mining and analyzing the popular SourceForge community. We show that our approach of member composition discovery is feasibly in terms of scalability and quality of discovery results. Our findings deliver important input for the design and implementation of supporting information systems for future large-scale collaboration platforms.

No abstract available.

Web-based collaborations and processes have become essential in today's business environments. Such processes typically span interactions between people and services across globally distributed companies. Web services and SOA are the defacto technology to implement compositions of humans and services. The increasing complexity of compositions and the distribution of people and services require adaptive and context-aware interaction models. To support complex interaction scenarios, we introduce a mixed service-oriented system composed of both human-provided and Software-Based Services (SBSs) interacting to perform joint activities or to solve emerging problems. However, competencies of people evolve over time, thereby requiring approaches for the automated management of actor skills, reputation, and trust. Discovering the right actor in mixed service-oriented systems is challenging due to scale and temporary nature of collaborations. We present a novel approach addressing the need for flexible involvement of experts and knowledge workers in distributed collaborations. We argue that the automated inference of trust between members is a key factor for successful collaborations. Instead of following a security perspective on trust, we focus on dynamic trust in collaborative networks. We discuss Human-Provided Services (HPSs) and an approach for managing user preferences and network structures. HPS allows experts to offer their skills and capabilities as services that can be requested on demand. Our main contributions center around a context-sensitive trust-based algorithm called ExpertHITS inspired by the concept of hubs and authorities in web-based environments. ExpertHITS takes trust-relations and link properties in social networks into account to estimate the reputation of users.

We propose a domain-specific service selection mechanism and system implementation to address the issue of runtime adaptation of composite services that implement mission-critical business processes. To this end, we leverage quality of service (QoS) as a means to specify rigid dependability requirements. QoS does not include only common attributes such as availability or response time but also attributes specific to certain business domains and processes. Therefore, we combine both domain-agnostic and domain-specific QoS attributes in an adaptive QoS model. For specifying the service selection strategy, we propose a domain-specific language called VieDASSL to specify so-called selectors. This language can be used to specify selector implementations based on the available QoS attributes. Both the QoS model implementation and the selectors can be adapted at runtime to deal with changing business and QoS requirements. Our approach is implemented on top of an existing WS-BPEL engine. We demonstrate its feasibility by implementing a case study from the telecommunication domain.

Today, most tools for processing big data are batch-oriented. However, many scenarios require continuous, online processing of data streams and events. We present ESC, a new stream computing engine. It is designed for computations with real-time demands, such as online data mining. It offers a simple programming model in which programs are specified by directed acyclic graphs (DAGs). The DAG defines the data flow of a program, vertices represent operations applied to the data. The data which are streaming through the graph are expressed as key/value pairs. ESC allows programmers to focus on the problem at hand and deals with distribution and fault tolerance. Furthermore, it is able to adapt to changing computational demands. In the cloud, ESC can dynamically attach and release machines to adjust the computational capacities to the current needs. This is crucial for stream computing since the amount of data fed into the system is not under the platform's control. We substantiate the concepts we propose in this paper with an evaluation based on a high-frequency trading scenario.

With the advancement in distributed computing and collaborative software technologies, information sharing and privacy related issues are gaining interest of researchers related to digital information creation, management, and distribution. Collaborative information sharing environment requires enhanced information sharing among users while privacy laws demand for the protection of user's information from unauthorized access and usage. Keeping this trade-off in view, there is a need for a flexible and enhanced information sharing model that preserves the privacy of user's information. We extend the Role-Based Access Control (RBAC) model to incorporate sharing and privacy related requirements and present a Dynamic Sharing and Privacy-aware Role-Based Access Control (DySP-RBAC) model. It is a family of models including core, hierarchical, and constrained RBAC models. The RBAC model is extended using team and task data elements as well as new data elements related to sharing and privacy of information. Sharing and privacy-based permission assignments and their conflict-handling strategies are described for a distributed and dynamic information sharing scenario.

With diverse and rich offerings from cloud computing providers in the open cloud market, scientists have great opportunities to design and conduct complex applications by utilizing and combining computational resources, software components and data sources in elastic manners. While existing techniques focus mainly on resource elasticity in single cloud infrastructure, scientists expect to design their applications being elastic in multiple dimensions to ensure that they applications can operate on multiple clouds with minimum software engineering effort. In this paper we will focus on providing techniques for scientists to compose elastic high performance applications by utilizing traditional software components, user-provided components and cloud services. We characterize elastic compositions via their resource, quality, cost, available time and usage right elasticity, thus enabling scientists to evaluate and decide how to develop, deploy and control the compositions to match their elastic needs. To illustrate our approach, we will present several real-world application compositions for multi-cloud environments.

Rich types of data offered by data as a service(DaaS) in the cloud are typically associated with different and complex data concerns that DaaS service providers, data providers and data consumers must carefully examine and agree with before passing and utilizing data. Unlike service agreements, data agreements, reflecting conditions established on the basis of data concerns, between relevant stakeholders have got little attention. However, as data concerns are complex and contextual, given the trend of mixing data sources by automated techniques, such as data mash up, data agreements must be associated with data discovery, retrieval and utilization. Unfortunately, exchanging data agreements so far has not been automated and incorporated into service and data discovery and composition. In this paper, we analyze possible steps and propose interactions among data consumers, DaaS service providers and data providers in exchanging data agreements. Based on that, we present a novel service for composing, managing, analyzing data agreements for DaaS in cloud environments and data marketplaces.

Currently, rich and diverse data types have been increasingly provided using the Data-as-a-Service (DaaS) model, a form of cloud computing services. However, data offered by DaaS are constrained by several data concerns that, if not automatically being reasoned properly, will lead to a wrong way of using them. In this paper, we support the assumption that data concerns should be explicitly modeled and specified in data contracts to support concern-aware data selection and utilization. Instead of relying on a specific definition of data contracts, we analyze contemporary data contracts and we present an abstract model for data contracts. Based on the abstract model, we propose several techniques for evaluating data contracts that can be integrated into data service selection and composition frameworks. We also illustrate our approach with some real world scenarios.

In recent years scientific workflows have been used for conducting data-intensive and long running simulations. Such simulation workflows have processed and produced different types of data whose quality has a strong influence on the final outcome of simulations. Therefore being able to monitor and analyze quality of this data during workflow execution is of paramount importance, as detection of quality problems will enable us to control the execution of simulations efficiently. Unfortunately, existing scientific workflow execution systems do not support the monitoring and analysis of quality of data for multi-scale or multi-domain simulations. In this paper, we examine how quality of data can be comprehensively measured within workflows and how the measured quality can be used to control and adapt running workflows. We present a quality of data measurement process and describe a quality of data monitoring and analysis framework that integrates this measurement process into a workflow management system.

Open Web-based and social platforms dramatically influence models of work. Today, there is an increasing interest in outsourcing tasks to crowd sourcing environments that guarantee professional processing. The challenge is to gain the customer's confidence by organizing the crowd's mixture of capabilities and structure to become reliable. This work outlines the requirements for a reliable management in crowd computing environments. For that purpose, distinguished crowd members act as responsible points of reference. These members mediate the crowd's workforce, settle agreements, organize activities, schedule tasks, and monitor behavior. At the center of this work we provide a hard/soft constraints scheduling algorithm that integrates existing agreement models for service-oriented systems with crowd computing environments. We outline an architecture that monitors the capabilities of crowd members, triggers agreement violations, and deploys counteractions to compensate service quality degradation.

Flexible interactions in complex social and service-oriented collaboration systems increasingly demand for automated adaptation techniques to optimize partner discovery and selection. Today, applications of complex service-oriented systems can be found in crowd sourcing environments. In such environments, collaborations are typically short-lived and strongly influenced by incentives and actor behavior. As actors prove their reliable and dependable behavior in jointly performed activities, they become increasingly considered as invaluable partners. A social network builds a strong basis to enable successful collaborations between crowd members. In order to keep track of the dynamics in such systems, it is inevitable to apply an autonomous approach to manage social network structures automatically using captured interaction data. Thus, we introduce an adaptation concept that accounts for emerging social relations based on varying interaction behavior of collaboration partners. We describe the foundational concepts for dynamic social link management in Web-based collaborations. We highlight major concerns of computational models in highly dynamic networks and deal with temporal aspects such as supporting the emergence of relations, efficient update mechanisms, and aging of relations.

This paper addresses the problem of integration testing of data-centric dynamic compositions in service-based systems. These compositions define abstract services, which are replaced by invocations to concrete candidate services at runtime. Testing all possible runtime instances of a composition is often unfeasible. We regard data dependencies between services as potential points of failure, and introduce the k-node data flow test coverage metric. Limiting the level of desired coverage helps to significantly reduce the search space of service combinations. We formulate the problem of generating a minimum set of test cases as a combinatorial optimization problem. Based on the formalization we present a mapping of the problem to the data model of FoCuS, a coverage analysis tool developed at IBM. FoCuS can efficiently compute near-optimal solutions, which we then use to automatically generate and execute test instances of the composition. We evaluate our prototype implementation using an illustrative scenario to show the end-to-end practicability of the approach.

Achieving sustainability has become an important goal in the construction, refurbishment, operation and management of buildings. To this end, we need to achieve greater information exchange, especially, about practices and solutions for Energy Efficiency (EE) and the use of Renewable Energy Sources (RES) in buildings. However, in the building life-cycle, complex and disparate information sources are used by various stakeholders, thus understanding, integrating, managing and providing means for sharing such information is a challenging task. In this paper, we analyze the possibilities to capture, distill and disseminate expert know-how related to sustainable buildings, addressing the needs of the various stakeholders. A Sustainable Building Profile (SBP) is presented, which is a novel conceptual model designed to integrate information on EE and RES aspects of buildings. The SBP makes it possible to analyse the transformation of a particular building over time. Different stakeholders can use it to study various engineering, operation and maintenance problems in buildings related to energy efficiency.

In this paper, we present an approach for identity and access management (IAM) in the context of (cross-organizational) service-oriented architectures (SOA). In particular, we defined a domain-specific language (DSL) for role-based access control (RBAC) that allows for the definition of IAM policies for SOAs. For the application in a SOA context, our DSL environment automatically produces WS-BPEL (Business Process Execution Language for Web services) specifications from the RBAC models defined in our DSL. We use the WS-BPEL extension mechanism to annotate parts of the process definition with directives concerning the IAM policies. At deployment time, the WS-BPEL process is instrumented with special activities which are executed at runtime to ensure its compliance to the IAM policies. The algorithm that produces extended WS-BPEL specifications from DSL models is described in detail. Thereby, policies defined via our DSL are automatically mapped to the implementation level of a SOA-based business process. This way, the DSL decouples domain experts' concerns from the technical details of IAM policy specification and enforcement. Our approach thus enables (non-technical) domain experts, such as physicians or hospital clerks, to participate in defining and maintaining IAM policies in a SOA context. Based on a prototype implementation we also discuss several performance aspects of our approach.

Social networks have emerged from niche existence to a mass phenomenon. Nowadays, their fundamental concepts, such as managing personal contacts and sharing profile information, are increasingly harnessed for businesses in professional environments. Similar to service-oriented networks, they allow flexible discovery on demand and loose coupling of participants. Establishing social links facilitates cooperation and enables selective sharing of information. Intuitively, one shares more information with his connected neighbors and less or even none with unrelated individuals. Today, information is one of the most important and valuable goods in business networks. Being informed about ongoing collaborations and upcoming trends is a key success factor. Thus, in professional networks, participants aim at strategically establishing connections to enable reliable information flows. In this paper, we especially highlight an opportunistic model that let mediators connect actually unrelated actors in order to benefit from information mediation. We further discuss a framework that implements this model for service-oriented professional virtual communities.

Web-based collaboration and virtual environments supported by various Web 2.0 concepts enable the application of numerous monitoring, mining and analysis tools to study human interactions and team formation processes. The composition of an effective team requires a balance between adequate skill fulfillment and sufficient team connectivity. The underlying interaction structure reflects social behavior and relations of individuals and determines to a large degree how well people can be expected to collaborate. In this paper we address an extended team formation problem that does not only require direct interactions to determine team connectivity but additionally uses implicit recommendations of collaboration partners to support even sparsely connected networks. We provide two heuristics based on Genetic Algorithms and Simulated Annealing for discovering efficient team configurations that yield the best trade-off between skill coverage and team connectivity. Our self-adjusting mechanism aims to discover the best combination of direct interactions and recommendations when deriving connectivity. We evaluate our approach based on multiple configurations of a simulated collaboration network that features close resemblance to real world expert networks. We demonstrate that our algorithm successfully identifies efficient team configurations even when removing up to 40% of experts from various social network configurations.

In this paper we describe a method and corresponding tool chain that allows moving an application to the cloud. In particular, we support to split an application such that various parts of it are moved to different clouds. This split can be done manually or by support of optimization algorithms. The split application is then automatically provisioned in the different target clouds. A metamodel for such applications supporting the proposed method is introduced. The architecture of a supporting tool is described. Experiences from the usage of the proposed method are reported.

Cloud computing represents a promising computing paradigm, where computational power is provided similar to utilities like water, electricity or gas. While most of the Cloud providers can guarantee some measurable non-functional performance metrics e.g., service availability or throughput, there is lack of adequate mechanisms for guaranteeing certifiable and auditable security, trust, and privacy of the applications and the data they process. This lack represents an obstacle for moving most business relevant applications into the Cloud. In this paper we devise a novel approach for compliance management in Clouds, which we termed Compliant Cloud Computing (C3). On one hand, we propose novel languages for specifying compliance requirements concerning security, privacy, and trust by leveraging domain specific languages and compliance level agreements. On the other hand, we propose the C3 middleware responsible for the deployment of certifiable and auditable applications, for provider selection in compliance with the user requirements, and for enactment and enforcement of compliance level agreements. We underpin our approach with a use case discussing various techniques necessary for achieving security, privacy, and trust in Clouds as for example data fragmentation among different protection domains or among different geographical regions.

The inadequate quality of context forces the context consumers in pervasive environments to reason about the quality and relevance of context to be confident of its worth to perform their functionality. The additional task of analyzing large volumes of context drastically affects the performance of the context consumers to adjust to dynamically changing situations. A single value that presents the quality and relevance of context information tailored to the needs of a particular context consumer may release them from spending resources on context quality analysis and let them concentrate on their main task. In this paper we present a novel technique to combine different Quality of Context (QoC) metrics to infer the value of confidence on context. Our technique also considers the requirements of a particular context consumer regarding QoC metrics while confidence inference. Confidence on context is further provided to the context consumers to select high quality context and use the confidence in their functionality. We have successfully evaluated our approach using two context consumer services and user context collected from a smart home pervasive environment.

The proliferation of Data as a Service (DaaS) available on the Internet and offered by cloud service providers indicates an increasing trend in providing data under Web services in e-science and business domains. While data usage and selection are dependent on different constraints established on the basis of several data concerns, for example, quality of data and data privacy, existing data service engineering approaches lack techniques to allow the evaluation, association and publishing of such concerns with data provided via DaaS. Furthermore, data sources behind DaaSs are not static but dynamically changing, thus requiring the evaluation and publishing of data concerns to be dynamic and on-the-fly as well. In this paper, we present a novel data concern-aware service engineering process for evaluating and publishing data concerns inside DaaS that covers different evaluation and publishing scopes, modes, and integration models. Based on our process, we present a framework and its implementation for the evaluation and publishing of quality of data metrics associated with data provided by DaaSs. In this paper, we also perform several experiments to demonstrate our framework.

Web services and service-oriented architecture (SOA) have become the de facto standard for designing distributed and loosely coupled applications. Many service-based applications demand for a mix of interactions between humans and Software-Based Services (SBS). An example is a process model comprising SBS and services provided by human actors. Such applications are difficult to manage due to changing interaction patterns, behavior, and faults resulting from varying conditions in the environment. To address these complexities, we introduce a self-healing approach enabling recovery mechanisms to avoid degraded or stalled systems. The presented work extends the notion of self-healing by considering a mixture of human and service interactions observing their behavior patterns. We present the design and architecture of the VieCure framework supporting fundamental principles for autonomic self-healing strategies. We validate our self-healing approach through simulations.

Complex networks consisting of humans and software services, such as Web-based social and collaborative environments, typically require flexible and context-based interaction models. Due to the dynamics in such systems, networks are in a state of constant flux and change. Several fundamental concepts, including discovery, interactions, task delegations and executions are no longer based on static policies, but need periodic readjustments. Sophisticated adaptation techniques for improving collaborations are within the key research areas in service-oriented systems. In this paper, we introduce an adaptation approach that accounts for emerging trust relations based on varying interaction behavior of network members. We describe a science collaboration scenario that applies adaptive information sharing techniques. In our model, trust evolves from cooperative behavior of collaboration partners. This behavioral trust provides an intuitive grounding for adaptations and optimizations of member compositions and sharing policies. As people prove their reliable and dependable behavior in jointly performed activities, they become increasingly considered as invaluable partners. We describe the foundational concepts, including support for ad-hoc and self-managed collaboration scenarios, and dynamic trust determination supported by SOA concepts. Furthermore, we present a reference architecture, and evaluate its applicability for large-scale collaboration networks.

This paper addresses one of the major problems of SOA software development: the lack of support for testing complex service-oriented systems. The research community has developed various means for checking individual Web services but has not come up with satisfactory solutions for testing systems that operate in service-based environments and, therefore, need realistic testbeds for evaluating their quality. We regard this as an unnecessary burden for SOA engineers. As a proposed solution for this issue, we present the Genesis2 testbed generator framework. Genesis2 supports engineers in modeling testbeds and programming their behavior. Out of these models it generates running instances of Web services, clients, registries, and other entities in order to emulate realistic SOA environments. By generating real testbeds, our approach assists engineers in performing runtime tests of their systems and particular focus has been put on the framework's extensibility to allow the emulation of arbitrarily complex environments. Furthermore, by exploiting the advantages of the Groovy language, Genesis2 provides an intuitive yet powerful scripting interface for testbed control.

We propose the PREvent framework, which is a system that integrates event-based monitoring, prediction of SLA violations using machine learning techniques, and automated runtime prevention of those violations by triggering adaptation actions in service compositions. PREvent improves on related work in that it can be used to prevent violations ex ante, before they have negatively impacted the provider's SLAs. We explain PREvent in detail and show the impact on SLA violations based on a case study.

The evolution towards cross-organizational collaboration and interaction patterns has led to the emergence of scalable, Web services-based composition infrastructures. The success of service-oriented architecture (SOA) was mainly influenced by the standardization of composition languages such as BPEL. However, compositions require humans to be in the loop and ways to interface with people in a service-oriented manner. In this paper, we discuss Human-Provided Services (HPS) enabling the seamless integration of human capabilities in SOA. In complex and large-scale environments, processes might span interactions among partially unknown participants residing in different organizational units. To address the problem of trusted selection of participants, we introduce a mining approach for the automatic inference of trust relations. Unlike a security-based view on trust, our approach relates to the emergence of trust across humans and services from a social perspective.

In service-oriented systems, clients can access services via a network. Service level agreements (SLA) can exist, which specify --- among other things --- performance-related Quality of Service (QoS) properties between the client and the server, such as round-trip time, processing time, or availability. For a service provider serious financial consequences or other penalties can follow in case of not fulfilling the SLAs. The service consumer wants to evaluate that the provider complies with the guaranteed SLAs. Designing and developing a QoS-aware service-oriented system means facing many design challenges, such as where and how to measure the performance-related QoS properties. This paper presents design practices and patterns for measuring such QoS properties by extending and utilizing existing patterns. The focus of the patterns lies on the QoS measuring impact on the client's or service's performance, the extend of separation of concerns, the property of reusability, and the preciseness of the measured QoS properties. The patterns help to build efficient solutions to measure performance-related QoS properties in a service-oriented system.

Today's ubiquitous internet access has opened new opportunities for mobile workers. By using portable devices, the workers are not only able to access their company's data and/or services from everywhere, but are also offering their own services for being accessible on-demand. The result is on the one hand a higher flexibility, in terms of coordination, but on the other hand poses various challenges to the company's internal workflows due to the dynamic nature of mobility. Consequently, the workflows must be tested at runtime in realistic scenarios in order to get evidence about their correct execution. In this paper we present an approach for emulating mobile workers in order to test the effects of unreliable dependability on workflows. By using the Genesis2 framework we generate testbeds consisting of real Web services and simulate their QoS as well as mobility issues such as packet loss, delay, and an unreliable availability. By generating a running testbed environment, our approach allows to investigate a workflow's execution at to detect runtime faults.

Cloud computing represents a promising computing paradigm where resources have to be dynamically allocated to software that needs to be executed. Self-manageable Cloud infrastructures are required to achieve that level of flexibility on the one hand, and to comply to users' requirements specified by means of Service Level Agreements (SLAs) on the other. Such infrastructures should automatically respond to changing component, workload, and environmental conditions minimizing user interactions with the system and preventing violations of SLAs. However, identification of system states where reactive actions are necessary for the prevention of SLA violations is far from trivial. In this paper we investigate how current knowledge management systems can be used for the prevention of SLA violations in Clouds. First, we define a typical SLA use case and formulate the expected behavior of the knowledge management system in order to prevent possible SLA violations. Second, we investigate different methods for knowledge management, e.g., situation calculus and case based reasoning (CBR). We discuss how these methods match the expected behavior for SLA violation prevention. In particular we examine the CBR method and devise several approaches for knowledge management in Clouds based on CBR. Finally, we evaluate our approach based on the presented use case.

Context-aware services need to acquire context information from heterogeneous context sources. The diversity of service requirements posts challenges on context provisioning systems as well as their programming models. This paper proposes COPAL (COntext Provisioning for ALl) - an adaptive approach to context provisioning. COPAL is at first a runtime middleware, which provides loose-coupling between context and its processing. The component architecture of COPAL ensures that new context processing functions can be added dynamically. A set of context processing patterns are proposed to customize context attributes and compose context provisioning schemes. The COPAL components and models are reflected in a Domain Specific Language (DSL), which can further reduce the development efforts of context provisioning using automatic code generation. A motivating scenario is used throughout the paper to illustrate COPAL approach.

Service-Oriented Computing has recently received a lot of attention from both academia and industry. However, current service-oriented solutions are often not as dynamic and adaptable as intended because the publish-find-bind-execute cycle of the Service-Oriented Architecture triangle is not entirely realized. In this paper, we highlight some issues of current web service technologies, with a special emphasis on service metadata, Quality of Service, service querying, dynamic binding, and service mediation. Then, we present the Vienna Runtime Environment for Service-Oriented Computing (VRESCo) that addresses these issues. We give a detailed description of the different aspects by focusing on service querying and service mediation. Finally, we present a performance evaluation of the different components, together with an end-to-end evaluation to show the applicability and usefulness of our system.

Governing business compliance with regulations, laws, best practices, contracts, and the like is not an easy task, and so far there are only limited software products available that help a company to express compliance rules and to analyze its compliance state. We argue that todaypsilas SOA-based way of implementing and conducting business (e.g., using Web services and business process engines) lends itself very well to the development of a comprehensive compliance government solution that effectively aids companies in being compliant. In this paper, we contextualize the compliance problem in SOA-based businesses, we highlight which are the most salient research challenges that need to be addressed, and we describe our approach to compliance governance, spanning design, execution, and evaluation concerns.

Mashup tools are becoming increasingly important enabling users to compose services and processes on the Web. Most existing tools focus on Web-based interfaces, usability, and visual languages for creating mashups. A major challenge that has received limited attention is context-awareness and adaptivity of service mashups. In this paper we focus on two main aspects: First, a service capability model describing service characteristics that can be tracked and matched against the requirements associated with service mashups and second an algorithm to recommend refinements such as replacing services within mashups. We implemented a set of adaptation algorithms to validate our approach in real service-oriented systems.

In a number of scenarios, services generated using a model-driven development (MDD) approach could benefit from “reflective” access to the information in the models from which they have been generated. Examples are monitoring, auditing, reporting, and business intelligence scenarios. Some of the information contained in the models of a service can statically be generated into its source code. In a distributed and changing environment this approach is limited, however, due to the fact that models and their relations evolve after the generation and deployment of a service. For example, the current model of a service might be different than the deployed version of the service. Our approach to solve this issue is a Model-Aware Service Environment (MORSE). It consists of a model repository that manages MDD projects and artifacts, and model-aware services that interact with the repository for performing reflective queries on the models stored in the repository. Thus, MORSE supports the dynamic, reflective lookup of models in service-oriented systems.

Since the Internet of Services (IoS) is becoming reality, there is an inherent need for novel service selection mechanisms, which work in spite of large numbers of alternative services and take the user-centric nature of services in the IoS into account. One way to do this is to incorporate feedback from previous service users. However, practical issues such as trust aspects, interaction contexts or synonymous feedbacks have to be taken into account. In this paper we discuss a service selection mechanism which makes use of structured and unstructured feedback to capture the Quality of Experience that services have provided in the past. We have implemented our approach within the SOA runtime VRESCO, where we use freeform tags for unstructured and numerical ratings for structured user feedback. We discuss the general process of feedback-based service selection, and explain how the problems described above can be tackled. We conclude the paper with an illustrative case study and discussion of the presented ideas.

Providing data as a service has not only fostered the access to data from anywhere at anytime but also reduced the cost of investment. However, data is often associated with various concerns that must be explicitly described and modeled in order to ensure that the data consumer can find and select relevant data services as well as utilize the data in the right way. In particular, the use of data is bound to various rules imposed by data owners and regulators. Although, technically Web services and database technologies allow us to quickly expose data sources as Web services, until now, research has not been focused on the description of data service concerns, thus hindering the discovery, selection and utilization of data services. In this paper, we analyze major concerns for data as a service, model these concerns, and discuss how they can be used to improve the search and utilization of data services.

A simple and effective composition of software services into higher-level composite services is still a very challenging task. Especially in enterprise environments, quality of service (QoS) concerns play a major role when building software systems following the service-oriented architecture (SOA) paradigm. Inthis paper we present a composition approach based on a domain-specific language(DSL) for specifying functional requirements of services and the expected QoS inform of constraint hierarchies by leveraging hard and soft constraints. Acomposition runtime will resolve the user's constraints to find an optimize dcomposition semi-automatically. To this end we leverage data flow analysis to generate a structured composition model and use two different techniques for the optimization, a constraint programming and an integer programming approach.

Business activity monitoring enables continuous observation of key performance indicators (KPIs). However, if things go wrong, a deeper analysis of process performance becomes necessary. Business analysts want to learn about the factors that influence the performance of business processes and most often contribute to the violation of KPI target values, and how they relate to each other. We provide a framework for performance monitoring and analysis of WS-BPEL processes, which consolidates process events and Quality of Service measurements. The framework uses machine learning techniques in order to construct tree structures, which represent the dependencies of a KPI on process and QoS metrics. These dependency trees allow business analysts to analyze how the process KPIs depend on lower-level process metrics and QoS characterisitics of the IT infrastructure. Deeper knowledge about the structure of dependencies can be gained by drill-down analysis of single factors of influence.

Software as a Service (SaaS) and the possibility to compose Web services provisioned over the Internet are important assets for a service-oriented architecture (SOA). However, the complexity and time for developing and provisioning a composite service is very high and it is generally an error-prone task. In this paper we address these issues by describing a semi-automated "Composition as a Service'' (CaaS) approach combined with a domain-specific language called VCL (Vienna composition language). The proposed approach facilitates rapid development and provisioning of composite services by specifying what to compose in a constraint-hierarchy based way using VCL. Invoking the composition service triggers the composition process and upon success the newly composed service is immediately deployed and available. This solution requires no client-side composition infrastructure because it is transparently encapsulated in the CaaS infrastructure.

Many service-oriented business systems have to comply to various contracts and agreements. Multiple technical and non-technical stakeholders with different background and knowledge are involved in modeling such business concerns. In many cases, these concerns are only encoded in the technical models and implementations of the systems, making it hard for non-technical stakeholders to get involved in the modeling process. In this paper we propose to tackle this problem by providing model-driven Domain-specific Languages (DSL) for specifying the contracts and agreements, as well as an approach to separate these DSLs into sub-languages at different abstraction levels, where each sub-language is tailored for the appropriate stakeholders. We exemplify our approach by describing a Quality-of-Service (QoS) DSL which can be used to describe Service Level Agreements (SLA). This work provides insights into how DSLs can be utilized to model and enrich service-oriented business systems with concerns defined in contracts and agreements.

In general, provenance of electronic data represents an important issue in information systems. So far, service-oriented computing research has mainly focused on provenance of data. However, service provenance also plays a central role since service providers and consumers want to be aware of the service's origin and history. In this paper, we present an approach for service provenance that builds on service metadata and various service runtime events. In addition, access control mechanisms are implemented to restrict access to this information. Besides being able to query and subscribe to provenance information, provenance graphs can be used to illustrate the history of services. We give some usage examples of service provenance and show how our approach was integrated into the VRESCo Web service runtime environment.

Context-awareness has become a desired key feature of today's mobile systems, yet, its realization still remains a challenge. On the one hand, mobile computing provides great potential for adaptations based on sensed contextual information. On the other hand, the lack of dependability in mobile networks hampers an efficient provision of this information to requesting clients. In this paper we present COSINE, a context management framework for mobile environments. COSINE has been developed on the principles of peer-to-peer computing and establishes context sharing infrastructures consisting of loosely coupled Web services. The services represent modular entities of the applied context model and manage the retrieval, aggregation, query, and provision of context data. Clients access the distributed information transparently via proxies and a self-adaptive routing of queries provides increased fault-tolerance, which is essential in mobile environments.

Increasingly, application developers seek the ability to search for existing Web services within large Internet-based repositories. The goal is to retrieve services that match the user's requirements. With the growing number of services in the repositories and the challenges of quickly finding the right ones, the need for clustering related services becomes evident to enhance search engine results with a list of similar services for each hit. In this article, a statistical clustering approach is presented that enhances an existing distributed vector space search engine for Web services with the possibility of dynamically calculating clusters of similar services for each hit in the list found by the search engine. The focus is laid on a very efficient and scalable clustering implementation that can handle very large service repositories. The evaluation with a large service repository demonstrates the feasibility and performance of the approach.

Nowadays, the importance of Web services is steadily increasing in domains where interoperability is of paramount importance. This trend is especially observable in complex computer systems which consist of a large number of interacting distributed components, often implemented using Web services. Large-scale systems and high complexity usually result in higher error-proneness in the development process. This should be addressed as early as possible during the development of complex service-oriented systems, ideally before they are actually deployed on a distributed infrastructure. In this paper we present GENESIS - a software framework for solving this problem. Our framework allows automatic generation and steering of testbeds of complex Web services, thereby empowering developers to specify functional and non-functional properties of Web services, to generate and deploy Web service instances on remote hosting environments, to enhance the functionality of the framework with plug-ins, and to control the behavior of the testbed during runtime.

The need for integration of human interaction scenarios into BPEL processes lead to the formalisation of tasks and human roles. The specifications BPEL4People and WSHumanTask introduce, among other definitions, a dedicated people activity that uses a task performed by a human as well as human roles that describe the relationship of people, processes and tasks. This work presents the architecture of Vienna BPEL for People (VieBOP), a new BPEL4People system that can be coupled with an arbitrary BPEL engine. We will evaluate the standards for BPEL4People and WSHumanTask against goals as derived from the BPEL4People white paper and compare it to our work.

Web service processes currently lack monitoring and dynamic (runtime) adaptation mechanisms. In highly dynamic processes, services frequently need to be exchanged due to a variety of reasons. In this paper we present VieDAME, a system which allows monitoring of BPEL processes according to Quality of Service (QoS) attributes and replacement of existing partner services based on various (pluggable) replacement strategies. The chosen replacement services can be syntactically or semantically equivalent to the BPEL interface. Services can be automatically replaced during runtime without any downtime of the overall system. We implemented our solution with an aspect-oriented approach by intercepting SOAP messages and allow services to be exchanged during runtime with little performance penalty costs, as shown in our experiments, thereby making our approach suitable for high-availability BPEL environments.

Service-Oriented Computing (SOC) is a new computing paradigm that utilizes services as the basic constructs to support the development of rapid, low-cost and easy composition of distributed applications even in heterogeneous environments. The promise of Service-Oriented Computing is a world of cooperating services where application components are assembled with little effort into a network of services that can be loosely coupled to create flexible dynamic business processes and agile applications that may span organizations and computing platforms. The subject of Service-Oriented Computing is vast and enormously complex, spanning many concepts and technologies that find their origins in diverse disciplines that are woven together in an intricate manner. In addition, there is a need to merge technology with an understanding of business processes and organizational structures, a combination of recognizing an enterprise's pain points and the potential solutions that can be applied to correct them. The material in research spans an immense and diverse spectrum of literature, in origin and in character. As a result research activities are very fragmented. This necessitates that a broader vision and perspective be established — one that permeates and transforms the fundamental requirements of complex applications that require the use of the Service-Oriented Computing paradigm. This paper provides a Service Oriented Computing Roadmap and places on-going research activities and projects in the broader context of this roadmap. This research roadmap launches four pivotal, inherently related, research themes to Service-Oriented Computing: service foundations, service composition, service management and monitoring and service-oriented engineering.

No abstract available.

Developing cross-organizational business processes is a tedious task. The partners have to agree on a common data format and meaning as well as on the Quality of Service (QoS) requirements each partner has to fulfill. The QoS requirements are typically described using Service Level Agreements (SLAs) among the partners. In this paper, we propose a top-down modeling approach for Web service based business processes to capture the functional and non-functional aspects using a choreography language (WS-CDL) which describes the message interactions among the participants. The choreography is annotated with SLAs for the different partners. For each partner in the process, an orchestration (in WS-BPEL) and the necessary Web service templates are automatically generated. Additionally, the Service Level Objectives (SLOs) from the partner SLAs are automatically translated into policies which can then be enforced by a BPEL engine during execution.

In this paper we present the concept of activity-centric collaboration using service-oriented architectures (ACCUSO), which addresses the requirements arising from ad-hoc collaboration in mobile teams. In ACCUSO, activities are used to map human actions to Web services exploiting the potential benefits of SOA, such as service discovery and binding at run time. The possibility to compose activities hierarchically from sub-activities and to redesign running activities provides the process-flexibility required in ad-hoc collaboration. We expand the notion of service orientation by introducing human-provided services (HpS) which provide functionality not realizable through software services. HpS are "implemented" by human actors (possibly being mobile), which remains transparent to the system, thereby allowing for the provisioning of HpS based on conventional WS-infrastructure. The feasibility and applicability of ACCUSO is demonstrated through a proof-of-concept implementation.

Web services composition is a keystone in the development of interoperable systems. However, despite the widespread adoption of Web services, several obstacles still hinder their smooth automatic semantic reconciliation when being composed. Consistent understanding of data exchanged between composed Web services is hampered by various implicit modeling assumptions and representations. Our contribution in this article revolves around context and how it enriches data exchange between Web services. In particular, a context-based mediation approach to solve semantic heterogeneities between composed Web services is presented.

Service-oriented architectures are increasingly used in the context of business processes. However, the proven practices for process-oriented integration of services are not well documented yet. In addition, modeling approaches for the integration of processes and services are neither mature nor do they exactly reflect the proven practices. In this article, we propose a pattern language for process-oriented integration of services to describe the proven practices. Our main contribution is a modeling concept based on pattern primitives for these patterns. A pattern primitive is a fundamental, precisely specified modeling element that represents a pattern. We present a catalog of pattern primitives that are precisely modeled using OCL constraints and map these primitives to the patterns in the pattern language of process-oriented integration of services. We also present a model validation tool that we have developed to support modeling the process-oriented integration of services, and an industrial case study in which we have applied our results.

Mobile ad-hoc networks with their arbitrary topologies are a difficult domain for providing highly available Web services. Since hosts can move unpredictably, finding services and featuring their constant and reliable functionality poses a challenge. In this paper we present a flexible system which is not only bound to ad-hoc networks, but can be used in any other environment. Our solution offers a discovery technique which keeps UDDI information in distributed registries up-to-date and includes a replication and synchronization mechanism which provides backup services for a highly increased service dependability.

In this paper, we present a recently funded European research project, namely WORKPAD, that aims at designing and developing an innovative software infrastructure (software, models, services, etc.) for supporting collaborative work of human operators in emergency/disaster scenarios. In such scenarios, different teams, belonging to different organizations, need to collaborate with one other to reach a common goal; each team member is equipped with handheld devices (PDAs) and communication technologies, and should carry on specific tasks. In such a case we can consider the whole team as carrying on a process, and the different teams (of the different organizations) collaborate through the "interleaving" of all the different processes (macro-process). Each team is supported by some back-end centre, and the different centres need to cooperate at an inter-organizational level to reach an effective coordination among teams. The project investigates a 2-level framework for such scenarios: a back-end peer-to-peer community, providing advanced services requiring high computational power, data & knowledge & content integration, and a set of front-end peer-to-peer communities, that provide services to human workers, mainly by adaptively enacting processes on mobile ad-hoc networks

In collaborative working environments (CWE), human interaction patterns represent reoccurring situations describing the sequence and type of interactions between individuals. We believe that such patterns provide information that may be used to improve human collaboration. In this paper we introduce interaction patterns to an existing context sharing platform used by distributed teams. We use these patterns to formulate rules that help determining the relevance of context information between users and that raise team awareness between interacting entities. These rules are integrated in an existing platform for context sharing between mobile users which allows us to demonstrate the practical applicability of our approach

The publish/subscribe style is an emerging paradigm for the construction of loosely coupled systems. Yet, the verification of such systems remains difficult. We have constructed a framework called RAY for testing publish/subscribe applications. This framework is implemented as an Eclipse plug-in. In this paper, we present the detailed architecture of RAY, as well as the interaction with its supporting components. In addition to the presented architecture for a testing framework for publish/subscribe applications, the contribution of this paper includes the experience gained during the development of this framework

Business rules provide an elegant solution to manage dynamic business logic by separating business knowledge from its implementation logic. The drawback of most existing business rule approaches is the lack of standardization and interoperability. The lack of service-orientation and remote accessibility of business rule engines makes it hard to use business rules in distributed environments. This paper contributes the design and implementation of ViDRE (Vienna distributed rules engine), a service-oriented business rule engine based on RuleML. ViDRE enables enterprise applications to access business rules as easy as accessing a database, by exposing rules as Web services. ViDRE uses RuleML as an interlingua to represent facts, rules, and queries. One of the main contributions of the ViDRE approach is the ability to distribute rules and facts across various rule engines, therefore, enabling powerful ways of separating and executing business rules within intra- and interorganizational boundaries

Web services gain momentum for developing flexible service-oriented architectures. Quality of service (QoS) issues are not part of the Web service standard stack, although non-functional attributes like performance, dependability or cost and payment play an important role for service discovery, selection, and composition. A lot of research is dedicated to different QoS models, at the same time omitting a way to specify how QoS parameters (esp. the performance related aspects) are assessed, evaluated and constantly monitored. Our contribution in this paper comprises: a) an evaluation approach for QoS attributes of Web services, which works completely service-and provider independent, b) a method to analyze Web service interactions by using our evaluation tool and extract important QoS information without any knowledge about the service implementation. Furthermore, our implementation allows assessing performance specific values (such as latency or service processing time) that usually require access to the server which hosts the service. The result of the evaluation process can be used to enrich existing Web service descriptions with a set of up-to-date QoS attributes, therefore, making it a valuable instrument for Web service selection

The different approaches of emerging workflow modeling languages are manifold. Today, there exist many notations for workflow modeling with various specializations on different domains. In this paper, we analyze three well known business process (workflow) modeling notations for their support for elaborated key aspects in workflow modeling. The aim of this paper is to discuss their differences and commonalities concerning these aspects.

To understand the performance of grid workflows, performance analysis tools have to select, measure and analyze various performance metrics of the workflows. However there is a lack of a comprehensive study of performance metrics which can be used to evaluate the performance of a workflow executed in the grid. This paper presents performance metrics that performance monitoring and analysis tools should provide during the evaluation of the performance of grid workflows. Performance metrics are associated with many levels of abstraction. We introduce an ontology for describing performance data of grid workflows. We describe how the ontology can he utilized for monitoring and analyzing the performance of grid workflows.

The paper presents and discusses models of generic mobile services. The primary goal is to gain understanding of the challenges in designing, developing and deploying advanced mobile data services. Two types of models are introduced. First, a composition model is given, describing the components of a generic mobile service and the component relationships. Second, distribution models are presented, describing the distributions of the components in the first model across hosts, networks and domains. A brief mobility analysis is carried out, followed by a discussion of the dependency of mobility and service continuity on the service distribution. The functions necessary to provide service continuity are identified and incorporated in a service continuity layer.

This paper presents an analysis of the requirements for service personalization and proposes a generic service architecture that supports personalization. It starts with a study of relevant personalization works and a discussion on the importance of personalization on services. An information space for service personalization is elaborated. A definition of personalization is given, and based on this definition the components of a generic mobile service are identified. A summary of the requirements for each of these components is given. Two models for realizing personalization of mobile services are presented. Last, two case studies on personalized Web browsing are presented to further highlight the complexity of personalization of generic mobile services, and to motivate for future work.