Vision Transformers (ViTs) have gained significant attention and widespread adoption due to their impressive performance in various computer vision tasks. However, in practice, their substantial computational overhead often leads to high inference latency and increased overheads when deployed on resource-constrained edge devices like smartphones, autonomous vehicles, and robots. To address these challenges, Early Exit (EE) has emerged as a promising approach for lightweight inference on edge devices. It accelerates inference and reduces computational overhead by adaptively producing predictions through early exits based on sample complexity. Existing EE methods typically suffer from substantial accuracy decreases in late exits while providing only marginal accuracy improvements to early exits. This paper presents EnViT, an exit-aware structured dropout-enabled self-distillation approach that enhances the performance of early exits without compromising late exits. EnViT leverages structured dropout to enable self-distillation, where the full model serves as the teacher and its own virtual sub-models generated by structured dropout as students. This mechanism effectively distills knowledge from the full model to early exits and avoids performance degradation in late exits by mitigating parameter conflicts across exits during training. Evaluation on five datasets shows that our EnViT achieves accuracy improvements ranging from 0.36% to 7.92% while maintaining competitive speed-up ratios of 1.72x to 2.23x.

Federated Learning (FL) enables knowledge sharing across client models with privacy preservation. A key challenge in FL is to aggregate general knowledge across clients while preserving personalized knowledge for each client. We discover that this challenge primarily stems from the coupling of general and personalized knowledge across different model layers. Existing methods either fail to fully decouple these two types of knowledge or do not leverage them effectively for inference, leading to suboptimal performance. This paper presents Thorki, a new FL system that decouples general and personalized knowledge throughout all model layers. In Thorki, instead of one model, each client stores its personalized knowledge in a small model and general knowledge in a large model. When making an inference, it employs a fusion network to combine the outputs of the two models, leveraging both types of knowledge adaptively. This new model collaboration paradigm allows clients to share their general knowledge in a federated manner without compromising their personalized inference capabilities. Extensive experiments with three models on four datasets demonstrate that Thorki outperforms state-of-the-art FL systems by 0.16%-19.14% in model accuracy and improves time-to-accuracy performance by 2.00x-10.00x.

Pre-trained visual models have been deployed on various edge devices to facilitate a broad range of downstream tasks through few-shot learning (FSL), in particular when downstream data or on-device resources are limited. However, FSL often suffers from poor performance due to its inability to adapt to the characteristics of the deployment environments, while backbone fine-tuning prior to model deployment is typically infeasible because of the unavailability of environment-specific samples. To tackle this challenge, this paper presents Glasses, a lightweight fine-tuning scheme that can adapt ViT-based model backbones to deployment environments rapidly through device-cloud collaboration, helping the model achieve better FSL performance on the device. Glasses leverages the computational power and sample resources in the cloud to produce model updates for rapid model adaptation based on only one environment image without needing a label. Experiments with five models on two datasets demonstrate that Glasses can adapt a model rapidly and outperform the original backbone by 5.54%–22.56% in the 1-shot setting and by 2.50%–10.28% in the 5-shot setting. The source code is available at https://github.com/CGCL-codes/Glasses.

Collaborative learning between edge servers (e.g., base stations) and end devices (e.g., drones) enables simultaneous model training in web applications through knowledge sharing. The resulting models effectively reduce service latency. However, existing approaches either assume isomorphic models on edge servers and end devices or incur substantial transmission overhead when training. Moreover, edge servers are often unable to access data from end devices on time due to long-distance constraints or strict data privacy regulations. This paper proposes a Prototype Augmentation-based Edge-end Collaborative Learning method (PAECL). It simultaneously trains heterogeneous edge and end models in the absence of data on edge servers by transmitting only augmented class-wise feature vectors (prototypes), significantly reducing communication overhead compared to sharing models, data, or logits. Specifically, on end devices, prototype-implied latent knowledge is augmented via local prototype contrast and global prototype alignment. On edge servers, prototypes are further augmented to produce bounded virtual vectors by mixing them with random noise, and the augmented prototypes are then delivered to generative models to provide data during edge model training. Through simulations and field experiments, PAECL achieves the highest accuracy for edge and end models under limited training resources and reduces the transmission burden by at least 297 times compared to existing edge-end heterogeneous learning methods.

Deep neural network (DNN) model partitioning and pruning have proven to be effective methods for enhancing resource efficiency and reducing inference delay by strategically allocating DNN workloads across heterogeneous edge and cloud infrastructures. Nevertheless, the heterogeneous nature of resources complicates the deployment of DNN in mobile edge-cloud computing (MEC) networks. In this paper, we present an innovative framework for collaborative DNN inference in MEC networks by integrating fine-grained model partitioning and magnitude-based pruning. However, the joint model partitioning and pruning policy presents significant challenges due to the inherently coupled and mutually influential nature. To address it, we adopt Long Short-Term Memory (LSTM) networks as action generation controllers to generate discrete actions for model partitioning and pruning alternately. After that, we adopt the policy gradient algorithms to optimize the LSTM-generated actions with a moving average according to the Monte Carlo estimate. By directly optimizing the policy function, the proposed framework enhances the efficiency and stability of action space exploration, yielding faster convergence and improved inference performance. Experimental results on standard datasets indicate that the proposed framework outperforms state-of-the-art approaches, achieving an 8.247% increase in system reward and an average reduction of 27.313% in total delay within the considered MEC networks.

Multi-access edge computing (MEC) has garnered increasing attention from users due to low web service latency. Edge servers deployed near base stations have limited resources and can only serve users within their coverage areas. Therefore, efficiently allocating users to the appropriate edge servers is crucial for significantly enhancing system performance. Traditional edge user allocation (EUA) strategies often rely on precise user location, leading to significant privacy leakage risks and weakening users' trust. To tackle this challenge, this paper presents Disguiser, a novel privacy-preserving scheme designed to achieve efficient edge user allocation while safeguarding user location privacy. Disguiser employs a Laplace noise-based location obfuscation mechanism to ensure users' privacy. To resolve the user allocation problem after location obfuscation, which involves balancing real-time performance and accuracy, we design a two-stage user allocation algorithm, TEUA, consisting of the LR-EUA initial allocation algorithm and the MR-EUA reallocation algorithm. First, a novel EUA algorithm, LR-EUA, is integrated into Disguiser, innovatively combining linear relaxation with a greedy approach. Additionally, Disguiser further minimizes resource wastage in MEC systems caused by location obfuscation by employing the MR-EUA algorithm at base stations. Experimental results show that Disguiser effectively balances privacy protection with system performance, substantially outperforming existing state-of-the-art methods.

As a promising distributed computing paradigm, edge computing (EC) enhances service quality and reduces service latency by deploying computational and storage resources at the network edge. However, due to edge servers' geographic distribution and resource constraints, EC is challenged by edge denial-of-service (EDDoS) attacks. Although various approaches have been proposed to mitigate EDDoS attacks, the impact of capacity uncertainties in edge server processing capacity and transmission latency has been largely overlooked. This limits the adaptability and robustness of mitigation strategies in edge computing environments, which could result in overload and paralysis of edge servers. To address this issue, this paper introduces uEDDoS-D, an uncertainty-aware EDDoS mitigation approach based on an improved deep deterministic policy gradient algorithm. uEDDoS-D models the uncertainties of computing capacities and transmission latency of edge servers in EC environments and leverages the collective computational resources of edge servers to mitigate EDDoS attacks without relying on traditional attack detection mechanisms. uEDDoS-D aims to minimize the impact of uncertainties in edge server processing capacity and transmission latency on mitigation decisions while effectively reducing service latency. Experimental results demonstrate that uEDDoS-D significantly reduces service latency, outperforming the state-of-the-art approaches by an average of 16.75%, and enhances system robustness in EDDoS attack scenarios.

Edge computing facilitates the development and implementation of new mechanisms for data storage on edge servers located near users, thereby enabling low-latency edge data retrieval services. However, individual edge servers have limited storage capacity, which restricts their ability to meet edge users' diverse data demands. They often need to cooperate with neighbors. Edge data indexing systems play a crucial role in helping edge servers locate the requested data. Existing edge data indexing systems typically rely on static indexing structures, which may lead to significantly reduced query accuracy or excessive system memory overhead in dynamic edge data query (EDQ) scenarios, making it difficult to meet the high scalability requirements of edge computing systems. In this paper, we address these challenges and propose a novel adaptive edge data indexing system called Balloon, which dynamically scales memory usage to balance query accuracy and memory overhead as data volume varies at runtime. Specifically, each edge server maintains a resizable Adaptive Bloom Filter (ABF) for its local data, and builds a Balloon tree that aggregates the ABFs of neighboring servers within a latency constraint. The size of the Balloon tree dynamically scales with data volume to balance query accuracy and memory overhead. To validate Balloon, we implement and conduct comprehensive experiments using an edge storage system with 50 edge servers. The results indicate that, compared to the state-of-the-art edge indexing system, Balloon improves query accuracy by 26.74% and reduces query time by 18.16%. In the meantime, it reduces up to 35.66% memory overhead.

Multi-access Edge Computing (MEC) deploys computation and storage resources at the network edge, enabling devices to process data and requests on nearby edge services. This reduces data transmission latency and network congestion. However, due to edge servers' volatile running status and limited resources, the reliability of edge services deployed on them fluctuates over time. This may lead to concept drifts in edge services' real-time reliability streaming data. A severe negative drift may indicate a runtime reliability anomaly in an edge service, which often impacts users' Quality of Experience (QoE). To ensure edge services' reliability, this paper proposes CS-Detection, a hierarchical approach for detecting runtime reliability anomalies based on concept drift. CS-Detection employs the compressed sensing technique to sample complex and large-scale reliability streaming data. It employs a new technique that combines Variational AutoEncoder and Energy-Based Generative Adversarial Network (E2BGAN) to estimate the anomaly level of edge services by calculating the reconstruction error and discriminant error of compressed real-time reliability streaming data. To demonstrate the usefulness of CS-Detection in ensuring the QoE of MEC systems, we present CPRest, a coordinated checkpoint-based effective rejuvenation approach for restoring the normal operation of edge services affected by runtime reliability anomalies. CPRest classifies detection results into four levels and adjusts the edge services' restart trigger time accordingly. Comprehensive experiments conducted on real-world datasets demonstrate the effectiveness and efficiency of CS-Detection compared to state-of-the-art approaches.

As an emerging distributed computing paradigm, edge computing (EC) brings computing and storage capabilities to the network edge to support low-latency services. Service providers can cache popular data on edge servers to greatly reduce service latency and improve service quality. However, data popularity in EC environments is often dynamic and stochastic. Given the limited storage resources of edge servers, when data popularity changes, data may need to be migrated from edge servers with lower data popularity to those with higher data popularity. Data migration between edge servers can effectively reduce the high transmission costs associated with transferring data from the cloud to the edge. Most existing data migration approaches are designed for static EC environments, assuming that user access patterns and data demands remain constant over time. Even in dynamic EC environments, many approaches oversimplify data popularity by treating it as static, failing to model its fluctuations over time. In realistic EC scenarios, unexpected situations, such as sudden shifts in user preferences for specific data at certain times, can cause significant fluctuations in data popularity, leading to increased data retrieval latency and inefficient storage management. To address this uncertainty, this paper proposes a popularity uncertainty-aware online data migration approach that combines distributed robust optimization and Lyapunov optimization to minimize the data retrieval latency and migration cost for service providers. Specifically, it handles data popularity uncertainty in a data-driven manner through robust optimization. Then, it employs Lyapunov optimization to decompose the continuous optimization problem into multiple single-slot online optimization problems. Extensive experiments on a widely used real-world dataset confirm the effectiveness of this approach and its significant advantages over state-of-the-art approaches.

Data deduplication, originally designed for cloud storage systems, is increasingly popular in edge storage systems due to the costly and limited resources and prevalent data redundancy in edge computing environments. The geographical distribution of edge servers poses a challenge in aggregating all data storage information for global decision-making. Existing edge data deduplication (EDD) methods rely on centralized cloud control, which faces issues of timeliness and system scalability. Additionally, these methods overlook data popularity, leading to significantly increased data retrieval latency. A promising approach to this challenge is to implement distributed EDD without cloud control, performing regional deduplication with the edge server requiring deduplication as the center. However, our investigation reveals that existing distributed EDD approaches either fail to account for the impact of collaborative caching on data availability or generate excessive information exchange between edge servers, leading to high communication overhead. To tackle this challenge, this paper presents EdgeDup, which attempts to implement effective EDD in a distributed manner. Additionally, to ensure data availability, EdgeDup aims to maintain low data retrieval latency. EdgeDup achieves its goals by: 1) identifying data redundancies across different edge servers in the system; 2) deduplicating data based on their popularity; and 3) reducing communication overheads using a novel data dependency index. Extensive experimental results show that EdgeDup significantly enhances performance, i.e., reducing data retrieval latency by an average of 47.78% compared to state-of-the-art EDD approaches while maintaining a comparable deduplication ratio.

Mobile edge computing (MEC) is an emerging distributed paradigm where edge servers are deployed near base stations or access points to support low-latency services. The edge user allocation (EUA) problem, which aims to minimize system cost while meeting constraints like data transmission latency, has become a critical challenge for service providers. Existing studies typically focus on static MEC scenarios, assuming predictable latency between users and edge servers. However, network congestion introduces latency uncertainty, which, if not addressed, increases the risk of allocation failures or excessive service latency. This paper addresses the latency uncertainty-aware edge user allocation (uEUA) problem. We model uEUA as an integer programming problem and apply chance-constrained programming to convert uncertain latency into probabilistic constraints, which we then transform into deterministic conditions using Chebyshev's inequality. We propose two methods to solve the problem: BD-uEUA, an exact approach based on Benders decomposition, and LR-uEUA, an approximate method based on linear relaxation. Extensive experiments on a real-world dataset show that BD-uEUA reduces system costs by 19.70% compared to the state-of-the-art method, while LR-uEUA achieves a 3.05% reduction with just 0.09% of the system overhead.

With the rapid deployment of large language model (LLM) technology across various fields, numerous LLMs have been deployed on edge servers (ESs) to provide low-latency generative services for edge devices. However, as factual knowledge evolves, the massive number of parameters in LLMs poses significant challenges for updating LLMs on ESs. Existing studies employ federated fine-tuning to update LLMs, but these unconstrained updating approaches can lead to overfitting and knowledge forgetting, while also resulting in substantial overhead. Knowledge editing (KE), as a promising technology, ensures the injection of new knowledge while preserving existing knowledge by editing specific parameters. In this paper, we propose multi-ES collaborative KE for the first time and design the CoKE and pCoKE frameworks in edge-cloud scenarios. These frameworks enhance editing efficiency by extracting identical expressions of the same knowledge across different LLM parameters for collaborative editing across multiple ESs. Additionally, we incorporate a personalized selection module in pCoKE to provide domain-specific generative services on ESs. To further reduce editing latency, we design a binary search-based resource allocation algorithm to balance editing latency across ESs. Extensive experiments demonstrate that CoKE and pCoKE reduce editing latency by 73% while maintaining high editing quality. Moreover, pCoKE achieves an approximately 6% improvement in editing quality.

In large-scale urban areas, edge computing, with flexible and low-latency services, enriches various pioneering applications such as the internet of vehicles (IoVs) and smart cities. These location-sensitive applications raise a critical concern, i.e., the mismatch between the spatial distributions of computing requirements and computing capacities. And this mismatch gives a huge challenge for load balancing among edge servers. However, existing approaches do not account for this spatial unevenness, which undermines the high-quality implementations of urban edge computing systems. Regarding this load balancing problem, we propose a novel Power Diagram based Edge Balancing (PDEB) approach, pursuing computing capacities self-adapted with computing requirements via the power diagram. This paper makes three key contributions. First, we propose a distribution modeling framework that formulates spatial mismatch as an optimization problem. Second, we introduce a decentralized power diagram constructed from modeled distributions, serving as the mathematical foundation for decentralized edge coordination. Third, we develop a structured pairing and scheduling strategy based on the power diagram to proactively redistribute load across edge servers. Experimentally, PDEB achieves$42.78\%$better load balance and$16.69\%$lower queuing delay than leading baselines, validating its theoretical and practical advantages.

We are delighted to welcome all participants to the 2025 IEEE World Congress on Services (IEEE SERVICES 2025), which is taking place in the beautiful city of Helsinki, Finland. To support the services community of researchers and practitioners around the world, IEEE SERVICES 2025 is held in hybrid mode.

Hourglass: Enabling Efficient Split Federated Learning with Data Parallelism

We are delighted to welcome all participants to the 2025 IEEE World Congress on Services (IEEE SERVICES 2025), which is taking place in the beautiful city of Helsinki, Finland. To support the services community of researchers and practitioners around the world, IEEE SERVICES 2025 is held in hybrid mode.

KV cache technology, by storing key-value pairs, helps reduce the computational overhead incurred by *large language models* (LLMs). It facilitates their deployment on resource-constrained edge computing nodes like edge servers. However, as the complexity and size of tasks increase, KV cache usage leads to substantial GPU memory consumption. Existing research has focused on mitigating KV cache memory usage through sequence length reduction, task-specific compression, and dynamic eviction policies. However, these methods are computationally expensive for resource-constrained edge computing nodes. To tackle this challenge, this paper presents Sim-LLM, a novel inference optimization mechanism that leverages task similarity to reduce KV cache memory consumption for LLMs. By caching KVs from processed tasks and reusing them for subsequent similar tasks during inference, Sim-LLM significantly reduces memory consumption while boosting system throughput and increasing maximum batch size, all with minimal accuracy degradation. Evaluated on both A40 and A100 GPUs, Sim-LLM achieves a system throughput improvement of up to 39.40\% and a memory reduction of up to 34.65%, compared to state-of-the-art approaches. Our source code is available at https://github.com/CGCL-codes/SimLLM.

In an edge-assisted federated learning (FL) system, edge servers aggregate the local models from the clients within their coverage areas to produce intermediate models for the production of the global model. This significantly reduces the communication overhead incurred during the FL process. To accelerate model convergence, FedEdge, the state-of-the-art edge-assisted FL system, trains clients' models in local federations when they wait for the global model in each training round. However, our investigation reveals that it drives the global model towards clients with excessive local training, causing model drifts that undermine model performance for other clients. To tackle this problem, this paper presents Maverick, a new edge-assisted FL system that mitigates model drifts by training personalized local models for clients through contrastive local training. It introduces a model-contrastive loss to facilitate personalized local federated training by driving clients' local models away from the global model and close to their corresponding intermediate models. In addition, Maverick includes anomalous models in contrastive local training as negative samples to accelerate the convergence of clients' local models. Extensive experiments are conducted on three widely-used models trained on three datasets to comprehensively evaluate the performance of Maverick. Compared to state-of-the-art edge-assisted FL systems, Maverick accelerates model convergence by up to 16.2x and improves model accuracy by up to 12.7%.

Machine learning (ML) models are widely deployed on edge nodes, such as mobile phones and edge servers, to power a wide range of AI applications over the web. Ensuring the integrity of these edge models is paramount, as they are subject to corruption caused by software/hardware exceptions and malicious tampering, which may undermine model performance, incur economic losses, and pose health risks. Existing data integrity mechanisms designed for files stored on disks cannot properly verify the integrity of models running in GPUs or mitigate the new integrity threats against edge models. This paper proposes EdgeThemis, a novel mechanism for verifying the integrity of edge models through sentinel verification. To enable verifiability for a model M, EdgeThemis embeds a sentinel backdoor and a verification module into M. Then, a challenger can send verification requests to the edge node hosting M to verify its integrity. Next, the sentinel activates the verification module to generate a unique integrity proof tied to the identity of the edge node for verification. Finally, the challenger can verify the integrity proof to detect model corruption. Theoretical analysis proves that EdgeThemis can properly mitigate potential integrity threats against edge models. Experiments demonstrate that EdgeThemis achieves a verification accuracy of 100.00% across various models and different types of model corruption with robustness against replay attacks, theft attacks, and replacement attacks.

Various Web API Recommendation (AR) techniques have assisted developers in efficiently identifying suitable APIs for mashup creation. With the emergence of large language models (LLMs), there has been increasing interest in leveraging LLMs for recommender systems. Although several approaches have attempted to utilize LLMs by framing recommendations as prompts, this approach is not ideally suited for AR due to fundamental differences in the training processes of LLMs and AR models. Consequently, it's crucial to conduct further research to identify effective applications of LLMs in AR. To this end, we propose a novelLLM-based generative solution forAPIRecommendation (LLMAR) that combines instruction learning of multitask and multistage Low-Rank Adaptation fine-tuning based on LLaMA models. Experimental results on the ProgrammableWeb dataset show that LLMAR significantly outperforms representative methods in regular and data-limited scenarios.

Vehicular edge computing (VEC) tackles the escalating computational demands of intelligent transportation systems by offloading tasks to nearby roadside units (RSUs) for processing. However, in the dynamic vehicular network environment, where vehicles are constantly moving, effective VEC demands a sophisticated approach to managing computing, caching, and communication resources. This involves coordinating resource allocation and data caching across multiple vehicles and RSUs while making complex decisions about task placement. In this paper, we present VECO, a Vehicular Edge Caching and Offloading framework powered by digital twins (DTs). VECO leverages DTs for real-time monitoring of network conditions and resource states, enabling predictive analysis and intelligent decision-making. The framework incorporates a Dynamic Task Caching and Computation Offloading (DT2C) mechanism to optimize data caching and adapt task offloading based on task characteristics and dynamic resource availability. Specifically, we develop a utility-based caching algorithm for RSUs and a novel task offloading strategy using a Proximal Policy Optimization-based deep reinforcement learning algorithm. Extensive experiments demonstrate that VECO, augmented by the DT2C mechanism, significantly outperforms baseline approaches, achieving faster learning convergence and a 21% reduction in total costs, including system latency and energy consumption.

Edge learning (EL) is an end-to-edge collaborative learning paradigm enabling devices to participate in model training and data analysis, opening countless opportunities for edge intelligence. As a promising EL framework, federated synergy learning (FSyL) mitigates the computation and communication overhead on resource-constrained devices by offloading partial model layers to the edge server for synergistic training. Nevertheless, due to the system and statistical heterogeneity, naively using existing FSyL methods is significantly time-consuming and causes accuracy degradation. Motivated by this issue, this paper introduces a novel FSyL framework that integrates multi-dimensional training optimization and formulates the edge learning cost minimization (ELCM) problem. To tackle the ELCM efficiently, we designOL-MG, anOnLineModel Splitting and Resource ProvisioningGame. Specifically, we first reformulate and decompose the original ELCM based on data quality evaluation. Then, given a model splitting decision, we determine the optimal resource provisioning in Sub-problem1, based on which optimal model splitting in Sub-problem2 is modeled as a potential game. Subsequently, we introduce a decentralized algorithm to find a Nash equilibrium (NE) solution. Furthermore, we further extendOL-MGto support a budget-aware multi-edge scenario. Extensive experiments demonstrate that the proposed mechanism significantly outperforms state-of-the-art methods in cost-saving and accuracy improvement.

In the mobile edge computing environment, caching data in edge storage systems can significantly reduce data retrieval latency for users while saving the costs incurred by cloud-edge data transmissions for app vendors. Existingedge data caching(EDC) methods prioritize popular data and aim to minimize users’ data retrieval latency and system storage costs jointly. However, these EDC methods often rely on the assumption that data popularity always follows certain distributions. As a result, they cannot properly adapt to the fluctuations in data popularity due to user mobility or unexpected increases in user demands. Meanwhile, unlike cloud data centers, complex and fragile edge servers are more likely to experience physical failures or network outages, presenting new challenges for EDC strategies. Specifically, when an edge server fails or experiences an outage, cached data may become temporarily unavailable, leading to increased latency as requests are redirected to alternative servers or the cloud. In this paper, to enableuncertainty-aware edge data caching(uEDC), we first model the problem as a robust optimization problem and propose an optimal algorithm named uEDC-B to find the optimal uEDC solution. To address the high computational complexity of uEDC-B, we introduce an approximate algorithm named uEDC-L based on linear decision rules. Theoretical analysis and extensive experiments on a real-world dataset demonstrate that the proposed methods outperform two state-of-the-art approaches in handling the uncertainties in data popularity and edge server failure with a significant performance improvement of 59.27% in data retrieval latency and 55.07% in data caching cost.

In the edge computing environment, app vendors can distribute popular data from the cloud to edge servers to provide low-latency data retrieval. A key problem is how to distribute these data from the cloud to edge servers cost-effectively. Under current schemes, a file is divided into some data blocks for parallel transmissions from the cloud to target edge servers. Edge servers can then combine received data blocks to reconstruct the file. While this method expedites the data distribution process, it presents potential drawbacks. It is sensitive to transmission delays and transmission failures caused by runtime exceptions like network fluctuations and server failures. This paper presents EdgeHydra, the first edge data distribution scheme that tackles this challenge through fault tolerance based on erasure coding. Under EdgeHydra, a file is encoded into data blocks and parity blocks for parallel transmission from the cloud to target edge servers. An edge server can reconstruct the file upon the receipt of a sufficient number of these blocks without having to wait for all the blocks in transmission. It also innovatively employs a leaderless block supplement mechanism to ensure the receipt of sufficient blocks for individual target edge servers. These improve the robustness of the data distribution process significantly. Extensive experiments show that EdgeHydra can tolerate delays and failures in individual transmission links effectively, outperforming the state-of-the-art scheme by up to 50.54% in distribution time.

With its advantages in ensuring low data retrieval latency and reducing backhaul network traffic, edge computing is becoming a backbone solution for many latency-sensitive applications. An increasingly large number of data is being generated at the edge, stretching the limited capacity of edge storage systems. Improving resource utilization for edge storage systems has become a significant challenge in recent years. Existing solutions attempt to achieve this goal through data placement optimization, data partitioning, data sharing, etc. These approaches overlook the data redundancy in edge storage systems, which produces substantial storage resource wastage. This motivates the need for an approach for data deduplication at the edge. However, existing data deduplication methods rely on centralized control, which is not always feasible in practical edge computing environments. This article presents Ripple, the first approach that enables edge servers to deduplicate their data in a decentralized manner. At its core, it builds a data index for each edge server, enabling them to deduplicate data without central control. With Ripple, edge servers can 1) identify data duplicates; 2) remove redundant data without violating data retrieval latency constraints; and 3) ensure data availability after deduplication. The results of trace-driven experiments conducted in a testbed system demonstrate the usefulness of Ripple in practice. Compared with the state-of-the-art approach, Ripple improves the deduplication ratio by up to 16.79% and reduces data retrieval latency by an average of 60.42%.

The emergence ofmobile edge computing(MEC) in distributed systems has sparked increased attention toward edge data management. A conflict arises from the disparity between limited edge resources and the continuously expanding data requests for data storage, making the reduction of data storage costs a critical objective. Despite the extensive studies of edge data deduplication as a data reduction technique, existing deduplication methods encounter numerous challenges in MEC environments. These challenges stem from disparities between edge servers and cloud data center edge servers, as well as uncertainties such as user mobility, leading to insufficient robustness in deduplication decision-making. Consequently, this paper presents a robust optimization-based approach for the edge data deduplication problem. By accounting for uncertainties including the number of data requirements and edge server failures, we propose two distinct solving algorithms: uEDDE-C, a two-stage algorithm based on column-and-constraint generation, and uEDDE-A, an approximation algorithm to address the high computation overhead of uEDDE-C. Our method facilitates efficient data deduplication in volatile edge network environments and maintains robustness across various uncertain scenarios. We validate the performance and robustness of uEDDE-C and uEDDE-A through theoretical analysis and experimental evaluations. The extensive experimental results demonstrate that our approach significantly reduces data storage cost and data retrieval latency while ensuring reliability in real-world MEC environments.

Edge computing allows app vendors to store popular data on edge servers, enabling users to retrieve data with low latency. However, edge servers may become unavailable at runtime due to expected exceptions. Data requests are routed to cloud servers, resulting in increased data retrieval latency. To address this issue,erasure coding(EC) has been employed to improve data availability, aiming to ensure full data access for all the users in anedge storage system(ESS). However, in real-world scenarios, data popularity differs and varies. Existing approaches for edge data placement place coded blocks across the entire system without considering data popularity. As a result, they often suffer from high data retrieval latency. In addition, they are designed to process data items individually. Data placed earlier will limit the placement options for subsequent files because edge servers with the most neighbors in the system can be easily exhausted. Some files cannot be placed properly to accommodate user demands. This increases users' data retrieval latency further. This paper tries to study the placement of multiple files in an edge storage system, considering their popularity. We first model theedge data placement(EDP) problem as a mixed-integer programming problem and prove its$\mathcal {NP}$-hardness. Then, we present an optimal algorithm named EDP-O, decoupling the EDP problem into three convex optimization subproblems for solving with an iterative algorithm. In addition, we propose an approximation algorithm named EDP-A that quickly solves the EDP problem in large-scale scenarios with a guaranteed approximation ratio of$\ln N$. The results of experiments conducted on a real-world dataset show that EDP-O and EDP-A reduce the average data retrieval latency against four representative approaches by an average of 18.4% and 15.6% in small-scale scenarios. EDP-A reduces the average data retrieval latency against four representative approaches by an average of 54.7% and reduces the data discard rate by an average of 34.9% in large-scale scenarios.

Third-party libraries (TPLs) play a crucial role in software development. Utilizing TPL recommender systems can aid software developers in promptly finding useful TPLs. A number of TPL recommendation approaches have been proposed and among them graph neural network (GNN)-based recommendation is attracting the most attention. However, GNN-based approaches generate node representations through multiple convolutional aggregations, which is prone to introducing noise, resulting in the over-smoothing issue. In addition, due to the high sparsity of labelled data, node representations may be biased in real-world scenarios. To address these issues, this paper presents a TPL recommendation method named Implicit Supervision-assisted Graph Collaborative Filtering (ISGCF). Specifically, it takes the App-TPL interaction relationships as input and employs a popularity-debiased method to generate denoised App and TPL graphs. This reduces the noise introduced during graph convolution and alleviates the over-smoothing issue. It also employs a novel implicitly-supervised loss function to exploit the labelled data to learn enhanced node representations. Extensive experiments on a large-scale real-world dataset demonstrate that ISGCF achieves a significant performance advantage over other state-of-the-art TPL recommendation methods in Recall, NDCG and MAP. The experiments also validate the superiority of ISGCF in mitigating the over-smoothing problem.

Edge computing, as an emerging paradigm in distributed computing, introduces a novel data access framework for latency-sensitive applications, enabling data retrieval from edge servers situated closer to users, rather than the remote cloud. This significantly reduces data retrieval latency, thereby enhancing the quality of experience. However, the resources of edge servers are highly constrained. Recent studies in erasure coding-based edge data storage have effectively sealed the gap between storage cost and data retrieval latency. Despite these advantages, the highly dynamic edge computing environments and frequent data updates introduce significant challenges in cache replacement for erasure coding-based edge storage systems. Specifically, direct data replacement, similar to traditional replica-based storage methods, may result in insufficient encoded blocks for reconstructing the original data during retrieval, which can increase data retrieval latency and even compromise data availability. In this paper, we identify and address, for the first time, the cache replacement problem in erasure coding-based edge storage systems. We propose a novel cache replacement algorithm, named ECCR, based on Lyapunov optimization, which effectively solves the cache replacement problem in dynamic edge computing environments. Theoretical analysis and extensive experiments on real-world datasets demonstrate the effectiveness and efficiency of the proposed method, which outperforms two state-of-the-art approaches and achieves an average system cost reduction of 49.78%.

Edge servers cache applications as microservices to provide low-latency services to end-device users. Due to heterogeneous resource constraints, microservices supporting the same application may be distributed across multiple edge servers. When an end-device makes a request, it generates a task execution path comprising interconnected dependent microservices across the edge network. However, task execution time can vary significantly across these servers due to resource heterogeneity, unpredictable workloads, cyberattacks, and software exceptions. The interdependent nature of microservice execution makes single-path models highly vulnerable, as any failure or delay compromises the entire task chain. This paper formulates the Dependent Task Execution Path Planning (DTEP) problem-a novel NP-hard optimization that transforms single-path dependency models into robust multi-path redundancy frameworks. With a redundancy budget, it plans multiple redundant, shortest node-disjoint execution paths to enhance reliability and latency performance. To solve this complex problem efficiently, we propose KPath, a novel two-phase approximation algorithm consisting of base path generation followed by target path acquisition, offering a guaranteed approximation ratio of$O(m)$. Experimental results demonstrate that KPath efficiently generates path sets with average path lengths up to 17.87% shorter than modified state-of-the-art methods across various problem scales. Furthermore, when node issues occur, KPath achieves up to 26% reduction in communication latency through redundancy.

The emergence of the mobile edge computing paradigm enables the deployment of microservices on edge servers, which greatly improves the quality of services and reduces network transmission costs. However, due to limited computing and storage resources, an individual edge server can host only a limited number of microservice instances. Moreover, user mobility often results in uneven distribution of service requests in mobile edge computing systems. To this end, it is a key problem to dispatch microservice requests to appropriate edge servers to minimize the average service response time. Current solutions to this problem rely on centralized methods and suffer from serious problems of single point of failure, error-proneness, difficult expansion, low robustness, etc. To resolve these problems, this paper proposes a decentralized game-theoretic approach for dispatching microservice requests effectively and efficiently in mobile edge computing systems. Specifically, we formulate the request dispatching problem as a decentralized non-cooperative game and propose a decentralized request dispatching algorithm that can find the Nash equilibrium through finite iterations. We conduct a series of experiments to demonstrate that our approach beats benchmarking approaches with close-to-optimal performance and high efficiency measured by convergence time.

In an edge computing environment, data can be cached on edge servers to enable fast data services for users. These edge data are subject to corruption and must be verified to ensure their integrity. Meanwhile, they are also subject to partial content changes over time. Existing edge data integrity (EDI) schemes are designed to verify edge data as a whole. They fail to accommodate partially identical edge data and consequently suffer from low verification accuracy in many real-world applications. In the meantime, their reliability is subject to compromises caused by edge servers' Byzantine behaviors. This paper presents DynaEDI+, a novel decentralized EDI scheme capable of verifying the integrity of partially identical edge data. It introduces pairing trees, a new tree-based data digest structure, to enable subtree-based content comparison, allowing precise identification of version-matched data blocks. To reduce communication overhead, DynaEDI+ transmits only root nodes instead of entire trees for verification. It also implements a series of security mechanisms to safeguard the verification process against evasion attacks, replay attacks, and theft attacks from Byzantine edge servers. Theoretical analysis proves that DynaEDI+ can effectively defend against potential threats from Byzantine edge servers. Experimental results demonstrate that DynaEDI+ achieves high accuracy in edge environments with partially identical data and Byzantine edge servers, while reducing communication overhead by an order of magnitude compared to benchmark schemes.

This paper presents ChestBox, a novel approach that utilizesstate functionsto facilitate low-latency state sharing for stateful serverless computing. When anapplication functionneeds to share a state, the state function creates a memory space with Linux's shared memory object to store the state. Other application functions can then read the state directly from the shared memory. ChestBox enables fast state sharing that avoids excessive memory overhead without compromising on-demand resource allocation compared to existing solutions. This effectively reduces the energy consumption of serverless computing and promotes sustainable computing. The implementation of ChestBox on Apache OpenWhisk unearths two major implementation challenges, which we address with respective optimization techniques, i.e., state function channel and state swapping. The evaluation of ChestBox with four real-world applications shows that compared with the state-of-the-art approach, it can reduce state-sharing latency by up to 99.71%, while reducing execution costs by 24.59% and storage costs by 99.76%.

This paper quantitatively analyses the potential of vertical scaling MicroVMs in serverless computing. Our analysis shows that under real-world serverless workloads, vertical scaling can significantly improve execution performance and resource utilization. However, we also find that the memory scaling of MicroVMs is the bottleneck that hinders vertical scaling from reaching the performance ceiling. We propose Faascale, a novel mechanism that efficiently scales the memory of MicroVMs for serverless applications. Faascale employs a series of techniques to tackle this bottleneck: 1) it sizes up/down the memory for a MicroVM by blocks that bind with a function instance instead of general pages; and 2) it pre-populates physical memory for function instances to reduce the delays introduced by the lazy-population. Compared with existing memory scaling mechanisms, Faascale improves the memory scaling efficiency by 2 to 3 orders of magnitude. We implement Faascale on Amazon Firecracker to evaluate its gains for the serverless platform. The results of experiments conducted on eight serverless benchmark functions demonstrate that compared with horizontal scaling strategies based the state-of-the-art snapshots technique, Faascale reduces time for cold-starting MicroVMs by 89.01% and functions execution time by 23.93% on average.

Abstract Edge computing is regarded as an extension of cloud computing that brings computing and storage resources to the network edge. For some Industrial Internet of Things (IIoT) applications such as supply‐chain supervision and collaboration, Internet of Vehicles, real‐time video analysis and so forth, users should be authenticated before visiting the geographically distributed edge servers. Limited by the considerable latency between the cloud and edge servers, and the limited capacity of edge servers, it is infeasible to copy the authentication method from cloud servers when users are authenticated in edge servers. In view of this challenge, this paper proposes a novel token‐based authentication scheme, named EdgeAuth, that enables fast edge user authentication through collaboration among cloud servers and edge servers. Under the EdgeAuth scheme, edge servers can rapidly verify the credentials of users who have been authenticated by the cloud server. EdgeAuth can also protect users from a series of authentication attacks, for example, the replay attack, DoS attack and man‐in‐the‐middle attack. The results of experiments conducted on a simulated edge computing environment validate the usefulness of EdgeAuth through a comparison in latency and throughput against two baseline schemes.

Healthcare uses state-of-the-art technologies (such as wearable devices, blood glucose meters, electrocardiographs), which results in the generation of large amounts of data. Healthcare data is essential in patient management and plays a critical role in transforming healthcare services, medical scheme design, and scientific research. Missing data is a challenging problem in healthcare due to system failure and untimely filing, resulting in inaccurate diagnosis treatment anomalies. Therefore, there is a need to accurately predict and impute missing data as only complete data could provide a scientific and comprehensive basis for patients, doctors, and researchers. However, traditional approaches in this paradigm often neglect the effect of the time factor on forecasting results. This paper proposes a time-aware missing healthcare data prediction approach based on the autoregressive integrated moving average (ARIMA) model. We combine a truncated singular value decomposition (SVD) with the ARIMA model to improve the prediction efficiency of the ARIMA model and remove data redundancy and noise. Through the improved ARIMA model, our proposed approach (namedMHDP$_{SVD\_{A}RIMA}$) can capture underlying pattern of healthcare data changes with time and accurately predict missing data. The experiments conducted on the WISDM dataset show thatMHDP$_{SVD\_{A}RIMA}$approach is effective and efficient in predicting missing healthcare data.

Collaborative filtering (CF) is dedicated to learning the representations of users and items based on interactive data. Regrettably, the lack of fine-grained modeling of interactive motivation makes the model less interpretable. A feasible solution is to combine the disentangling idea with the graph neural network (GNN) and capture different types of interaction relationships by using a message propagation mechanism on the graph of user–item interaction. However, this process typically relies on the disentangling of users’ hidden intents, ignoring the significance of item features to user engagement. This fact leads to the inadequate interpretability of existing models. To make up for the deficiency, this article proposes a new feature-aware disentangled GNN (FDGNN) for the recommendation. By learning the relationship between user behavior and important features of items, the model aims to achieve better recommendation performance and model interpretability. In the end, we first realize the feature partition based on mutual information and then design an attention-based graph disentangling model to realize the fine-grained disentangling of user intents. In addition, to further ensure the independence of the disentangled intents, we augment the model with disagreement regularization. Through multilayer embedding propagation, FDGNN can display a capture CF effect in feature semantics. The interpretability and efficiency of our proposed approach are demonstrated by numerous pertinent experiments.

Model partitioning is a promising technique for improving the efficiency of distributed inference by executing partial deep neural network (DNN) models on edge servers (ESs) or Internet-of-Things (IoT) devices. However, due to heterogeneous resources of ESs and IoT devices in mobile edge computing (MEC) networks, it is non-trivial to guarantee the DNN inference speed to satisfy specific delay constraints. Meanwhile, many existing DNN models have a deep and complex architecture with numerous DNN blocks, which leads to a huge search space for fine-grained model partitioning. To address these challenges, we investigate distributed DNN inference with fine-grained model partitioning, with collaborations between ESs and IoT devices. We formulate the problem and propose a multi-task learning based asynchronous advantage actor-critic approach to find a competitive model partitioning policy that reduces DNN inference delay. Specifically, we combine the shared layers of actor-network and critic-network via soft parameter sharing, and expand the output layer into multiple branches to determine the model partitioning policy for each DNN block individually. Experiment results demonstrate that the proposed approach outperforms state-of-the-art approaches by reducing total inference delay, edge inference delay and local inference delay by an average of 4.76%, 10.04% and 8.03% in the considered MEC networks.

As the primary driver of intelligent mobile applications, deep neural networks (DNNs) have gradually deployed to millions of mobile devices, producing massive latency-sensitive and computation-intensive tasks daily. Mobile edge computing facilitates the deployment of computing resources at the edge, which enables fine-grained offloading of DNN inference tasks from mobile devices to edge nodes. However, most existing studies have not systematically considered three crucial performance aspects: scheduling multiple streams of DNN inference tasks, leveraging multi-exit models to hasten task processing, and partitioning inference models for partial offloading. To this end, this paper proposes an adaptive inference framework in mobile edge computing, which can dynamically select the exit point and partition point for multiple inference task streams. We design a dynamic programming algorithm to obtain an efficient solution under the ideal condition that task arrival information is known. Further, we design a learning-based algorithm for online scheduling, whose training efficiency is improved based on historical experience initialization and priority experience replay. Experimental results show that compared with the Greedy algorithm, the online algorithm improves the performance on two environmental parameters by an average of 5.9% and 32%, respectively.

By pushing computing resources from the cloud to the network edge close to mobile users, mobile edge computing (MEC) enables low latency for a wide variety of applications. Nevertheless, in dynamic MEC systems, MEC services are challenged by the risks of runtime reliability anomalies. Detecting runtime reliability anomalies for MEC services is challenging yet critical to ensuring the stability of MEC systems. The effectiveness of existing anomaly detection methods suffers from poor performance when handling MEC services’ large-volume, continuous, and volatile reliability streaming data. The key is to identify significant changes in the distribution of MEC services’ current reliability streaming data compared with their historical performance. Inspired by concept drift, this paper proposes B-Detection, a boosting Long Short-Term Memory (LSTM) Autoencoder for detecting MEC services’ runtime reliability anomalies based on distribution dissimilarity evaluation. B-Detection employs a deep learning method named LSTM Autoencoder to characterize the MEC services’ historical reliability data distribution. To cope with the challenge of modeling complex distribution characteristics of MEC services’ historical reliability streaming data and guarantee the real-time performance of B-Detection, we enhance LSTM Autoencoder with a weight-based reservoir sampling technique and an LSTM boosting algorithm. The reconstruction loss of the trained LSTM Autoencoder model is estimated for the up-to-date reliability streaming data, and the result is used to infer MEC services’ runtime reliability anomalies. The performance of B-Detection is verified through a series of experiments conducted on a real-world dataset.

Edge computing (EC) enables low-latency services by pushing computing resources to the network edge. Due to the geographic distribution and limited capacities of edge servers, EC systems face the challenge of edge distributed denial-of-service (DDoS) attacks. Existing systems designed to fight cloud DDoS attacks cannot mitigate edge DDoS attacks effectively due to new attack characteristics. In addition, those systems are typically activated upon detected attacks, which is not always realistic in EC systems. DDoS mitigation needs to be cohesively integrated with workload migration at the edge to ensure timely responses to edge DDoS attacks. In this paper, we present EdgeShield, a novel DDoS mitigation system that leverages edge servers’ computing resources collectively to defend against edge DDoS attacks without the need for attack detection. Aiming to maximize system throughput over time without causing significant service delays, EdgeShield monitors service delays and migrates workloads across an EC system with adaptive mitigation strategies. The experimental results show that EdgeShield significantly outperforms state-of-the-art solutions in both system throughput and service delays.

Mobile edge computing (MEC) offers a new computing paradigm that turns computing and storage resources to the network edge to provide minimal service latency compared to cloud computing. Many research works have attempted to help app vendors allocate users to appropriate edge servers for high-performance service provisioning. However, existing edge user allocation (EUA) approaches have ignored fairness in users' data rates caused by interference, which is crucial in service provisioning in the MEC environment. To pursue fairness in EUA, edge users need to be assigned to edge servers so their quality of experience can be ensured at minimum costs without significant service performance differences among them. In this paper, we make the first attempt to address this fair edge user allocation (FEUA) problem. Specifically, we formulate the FEUA problem, prove its N P-hardness, and propose an optimal approach to solve small-scale FEUA problems. To accommodate large-scale FEUA scenarios, we propose a game-theoretic approach called FEUAGame that transforms the FEUA problem into a potential game that admits a Nash equilibrium. FEUA employs a decentralized algorithm to find the Nash equilibrium in the potential game as the solution to the FEUA problem. A widely-used real-world data set is utilised to experimentally compare the performance of FEUAGame to four representative approaches. The numerical outcomes show the effectiveness and efficiency of the proposed approaches in solving the FEUA problem

Mobile edge computing (MEC) enables web data caching in close geographic proximity to end users. Popular data can be cached on edge servers located less than hundreds of meters away from end users. This ensures bounded latency guarantees for various latency-sensitive web applications. However, transmitting a large volume of data out of the cloud onto many geographically-distributed web servers individually can be expensive. In addition, web content dissemination may be interrupted by various intentional and accidental events in the volatile MEC environment, which undermines dissemination efficiency and subsequently incurs extra transmission costs. To tackle the above challenges, we present a novel scheme named EdgeDis that coordinates data dissemination by distributed consensus among those servers. We analyze EdgeDis's validity theoretically and evaluate its performance experimentally. Results demonstrate that compared with baseline and state-of-the-art schemes, EdgeDis: 1) is 5.97x - 7.52x faster; 2) reduces dissemination costs by 48.21% to 91.87%; and 3) reduces performance loss caused by dissemination failures by up to 97.30% in time and 96.35% in costs.

Service recommendations are crucial for developers to create mashups such as mobile applications, workflows, e-business solutions, etc. Existing methods based on collaborative filtering or content analysis are manual and cannot automatically acquire services that align with the requirements of mashup creation. A possible solution to automatically acquiring necessary services for mashups is the seq2seq (sequence to sequence) generation model, which has demonstrated promising performance in automatic text and program code generation. However, two main challenges must be tackled in service acquisition based on the seq2seq model. First, the seq2seq model can only acquire a set of services without inter-service dependencies, but such dependencies are crucial in the generation of sequences for services. Second, external knowledge must be leveraged to recommend services more accurately that fulfill developers' requirements, such as similar historical user requirements and combining mashup category information, due to the incomplete description of user requirements. To tackle these challenges, this paper proposes GSR (Generation ofServiceRecommendations), an approach that can automatically acquire services based on user requirements. Specifically, GSR employs reinforcement learning to learn the inter-dependencies among services and integrate dependencies into service recommendations. To further improve the quality of the acquired services, GSR retrieves relevant user requirements based on BERT (Bidirectional Encoder Representation from Transformers) to help identify potential services. Experiment results conducted on real-world datasets show the superior performance of GSR. Compared with the existing recommendation approaches, the precision metric is increased by up to 1.99x, and the recall metric is increased by up to 12%.

Federated edge learning (FEEL) is a promising collaborative paradigm, which employs edge devices (EDs) to train machine learning models for a federation. It opens countless opportunities to enable edge intelligence. The increasingly diversified demands for intelligent services are driving the deployment of various federations at the edge. Existing works on FEEL focus on a single federation and ignore inter-federation device competition and intra-device resource allocation, which hinders the applications of FEEL. To address this issue, this article first investigates the bottlenecks of executing multiple federations and builds a joint optimization model as a two-stage Stackelberg game involving device selection and resource allocation. To tackle the problem efficiently, we present a game-theoretical approach namedDeviceSelection andResourceAllocation forMultipleFederationsGame (DSRAMF-G). First, following the arbitrary device selection of leaders (i.e., federations), the time cost minimization of followers (i.e., EDs) is modeled as a convex problem to obtain the optimal resource allocation. Then, based on followers’ optimal responses, device selection is modeled as a congestion game. We prove the existence of the Nash equilibrium and propose a decentralized mechanism. Finally, extensive experiments show that DSRAMF-G significantly outperforms the state-of-the-art methods, achieving up to 5.9x training speedup and 2.8x resource-savings.

Dynamic quality of service (QoS) measurement is crucial for discovering services and developing online service systems. Collaborative filtering-based approaches perform dynamic QoS prediction by incorporating temporal information only but never consider the dynamic network environment and suffer from poor performance. Considering different service invocation routes directly reflect the dynamic environment and further lead to QoS fluctuations, we coin the problem of Dynamic QoS Prediction (DQP) with Intelligent Route Estimation (IRE) and propose a novel framework named IRE4DQP. Under the IRE4DQP framework, the dynamic environment is captured by Network Status Representation, and the IRE is modeled as a Markov decision process and implemented by a deep learning agent. After that, the DQP is achieved by a specific neural model with the estimated route as input. Through collaborative training with reinforcement and inverse reinforcement learning, eventually, based on the updated representations of the network status, IRE learns an optimal route policy that matches well with observed QoS values, and DQP achieves accurate predictions. Experimental results demonstrate that IRE4DQP outperforms SOTA methods on the accuracy of response-time prediction by 5.79–31.34% in MAE, by 1.29–20.18% in RMSE, and by 4.43–27.73% in NMAE and with a success rate of nearly 45% on finding routes.

Mobile edge-cloud computing networks can provide distributed, hierarchical, and fine-grained resources, and have become a major goal for future high-performance computing networks. The key is how to jointly optimize service caching and computation offloading. However, the joint service caching and computation offloading problem faces three significant challenges of dynamic tasks, heterogeneous resources, and coupled decisions. In this paper, we investigate the issue of joint service caching and computation offloading in mobile edge-cloud computing networks. Specifically, we formulate the optimization problem as minimizing the long-term average service latency, which is NP-hard. To solve the problem, we conduct in-depth theoretical analyses and decompose it into two sub-problems: service caching processing and computation offloading processing. We are the first to propose a novel hierarchical deep reinforcement learning algorithm to solve the formulated problem, where multiple edge agents and a cloud agent collaboratively determine the caching-action and offloading-action, respectively. The results obtained through trace-driven simulations reveal that the proposed framework outperforms several prevailing algorithms concerning the average service latency across diverse scenarios. In a complex real scenario, our framework achieves an approximately 33% convergence improvement and a remarkable 39% reduction in the average service latency when compared to reinforcement learning-based algorithms.

Edge computing has emerged as a powerful paradigm for efficient scheduling at the network edge to fulfill users' requirements for low latency. Edge servers' and clouds' ability to interact allows them to leverage edge servers' limited computing and storage resources collectively to handle all requests collaboratively. This has unlocked the potential of the edge-cloud system. However, its sophistication (e.g., heterogeneous resources, intertwined dependencies, etc.) also raised unforeseen challenges in request dispatch and service orchestration phases within the scheduling process, includingcomplex hierarchy,limited cooperationandunfocused information. Traditional resource optimization methods for edge-cloud systems cannot address these intricate situations properly, raising concerns over system efficiency, stability, and costs. This paper proposes learning-based methods to accommodate time-varying requests and dynamic service prevalence. Specifically, we employ the multi-agent advantage actor-critic (MAA2C) and the graph convolutional networks-based MAA2C (GCN-MAA2C) in two phases, respectively, to enhance individual intelligence and facilitate optimal dispatch and orchestration decisions. Inspired by the regulations in human society, we proposeSocialEdge, a socialized learning-based scheduling framework for the edge-cloud system. To leverage the hierarchical correlation, we develop an inter-layer socialized refining mechanism. The inference results from one layer guide the training process of the next layer, enabling the abstraction of optimization-critical knowledge. Moreover, we apply intra-layer socialized cooperating, where federated averaging with MAA2C is implemented to ensure the cooperation of individuals over private data for achieving optimal global solutions. Furthermore, we propose a socialized resonance mechanism across the layers to extract high-value information and induce resonance toward the system objective, aiming to improve the efficiency of scheduling. Experimental evaluations on a proof-of-concept testbed and two real traces demonstrate thatSocialEdgereduces scheduling cost by 11.2% while enhancing time efficiency by 77.4%, and system throughput by 17.9%, compared to baseline methods.

Graph convolutional networks (GCNs)GCN have been widely used to learn high-quality representations (a.k.a. embeddings) from multiorder neighbors in recommendation tasks. However, many existing graph convolutional network (GCN)-based methods learn user and item embeddings in the user–item interaction bipartite graph indistinguishably, ignoring the inherent heterogeneity of the bipartite graph, i.e., users and items are two distinct types of entities. This article explores in depth the high-order connections for user and his (her) neighbor items. We propose an innovative model, neighbor item embedding-aware graph convolutional network (NIE-GCN). As opposed to previous GCN-based approaches, NIE-GCN employs a novel dual user aggregationdual user aggregation (DUA) scheme and a neighbor-aware attention mechanism to construct user embeddings and distinguish the contribution of different neighbor nodes. In addition, we propagate information in an alternating manner to eliminate the effects of heterogeneity of user–item interaction bipartite graph. According to detailed experiments on three large-scale datasets, the proposed NIE-GCN significantly outperforms state-of-the-art approaches on the Top-$N$recommendation task while reducing model parameters by about half. Further analyses show the effectiveness and rationality of dual user aggregationDUA and neighbor-aware attention mechanism.

The ability for cloud data centres and edge data centres to collaborate unleashes the potential of the edge-cloud system. However, its sophistication causes unexpected issues in request scheduling, such as Insufficient intelligence, complicated hierarchy and limited cooperation. Traditional resource optimization methods for the edge-cloud system struggle to accommodate such intricate situations. In this paper, inspired by the behaviors and regulations in human society, we propose a socialized learning-based scheduling approach for the edge-cloud system, namely SocialEdge. In order to adapt to time-varying requests and dynamic service prevalence, we propose a learning-based approach, multi-agent advantage actor-critic (MAA2C) and graph convolutional networks-based MAA2C for two phases, respectively, which can improve individual intelligence to achieve optimal dispatch and orchestration decisions. Then, to make use of hierarchy correlation, we develop the socialized refining inter the layers, where inference results from one layer guide the training process of the other layer so that optimization-critical knowledge can be abstracted. Besides, we apply socialized cooperation to each layer, where federated averaging with MAA2C is implemented to ensure cooperation over private data for achieving optimal global solutions. Experimental evaluations on a proof-of-concept testbed along with two real traces demonstrate that baselines have 105% more delay than SocialEdge while being 76.6% less time efficiency and 21.6% less throughput.

Edge learning (EL) is an end-edge collaborative learning paradigm that enables numerous edge devices to participate in model training and data analysis, opening countless opportunities to enable edge intelligence. As is a promising EL approach, split edge learning (SPEL) alleviates the computation and communication overhead on resource-constrained devices via offloading part of the machine learning (ML) model to the edge server for cooperative training. Nevertheless, due to the system and statistical heterogeneity of the edge environment, naively using existing SPEL methods brings significantly time-consuming and accuracy degradation. Specifically, system heterogeneity causes intolerable time costs in each training round, while statistical heterogeneity further results in weight divergence and more training rounds to achieve global convergence. Motivated by this issue, this paper designs an efficient SPEL scheme to minimize the total time cost of participating devices. Specifically, we propose a novel SPEL framework and formulate the edge learning cost minimization (ELCM) problem that involves jointly optimizing model splitting and resource provisioning. We design OL-MG, i.e., OnLine Model Splitting and Resource Provisioning Game scheme, to solve the ELCM problem. In OL-MG, we first transform and decompose the original ELCM into two subproblems based on data quality evaluation. Second, we determine the optimal resource provisioning of Sub-problem1 with a given model splitting decision, based on which optimal model splitting of Sub-problem2 is modeled as a potential game. Then, we propose a decentralized algorithm to find a Nash equilibrium (NE) solution for the ELCM problem. Experimental results from both hardware prototype and simulation demonstrate that OL-MG outperforms the state-of-the-art methods, achieving up to 3.1x training cost savings and 40% accuracy improvement.

In an edge storage system, popular data can be stored on edge servers to enable low-latency data retrieval for nearby users. Suffering from constrained storage capacities, edge servers must process users' data requests collaboratively. For sourcing data, it is essential to find out which edge servers in the system have the requested data. In this paper, we make the first attempt to study this edge data query (EDQ) problem and present EDIndex, a distributed Edge Data Indexing system to enable fast data queries at the edge. First, we introduce a new index structure named Counting Bloom Filter (CBF) tree for facilitating edge data queries. Then, to improve query performance, we enhance EDIndex with a novel index structure named hierarchical Counting Bloom Filter (HCBF) tree. In EDIndex, each edge server maintains an HCBF tree that indexes the data stored on nearby edge servers to facilitate data sourcing between edge servers at the edge. The results of extensive experiments conducted on an edge storage system comprised of 90 edge servers demonstrate that EDIndex 1) takes up to 8.8x less time to answer edge data queries compared with state-of-the-art edge indexing systems; and 2) can be implemented in practice with a high query accuracy at low initialization and maintenance overheads.

Training machine learning (ML) models on mobile and Web-of-Things (WoT) has been widely acknowledged and employed as a promising solution to privacy-preserving ML. However, these end-devices often suffer from constrained resources and fail to accommodate increasingly large ML models that crave great computation power. Offloading ML models partially to the cloud for training strikes a trade-off between privacy preservation and resource requirements. However, device-cloud training creates communication overheads that delay model training tremendously. This paper presents EdgeMove, the first device-edge training scheme that enables fast pipelined model training across edge devices and edge servers. It employs probing-based mechanisms to tackle the new challenges raised by device-edge training. Before training begins, it probes nearby edge servers’ training performance and bootstraps model training by constructing a training pipeline with an approximate model partitioning. During the training process, EdgeMove accommodates user mobility and system dynamics by probing nearby edge servers’ training performance adaptively and adapting the training pipeline proactively. Extensive experiments are conducted with two popular DNN models trained on four datasets for three ML tasks. The results demonstrate that EdgeMove achieves a 1.3 × -2.1 × speedup over the state-of-the-art scheme.

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world’s web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed; and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×.

Federated learning (FL) has been widely acknowledged as a promising solution to training machine learning (ML) model training with privacy preservation. To reduce the traffic overheads incurred by FL systems, edge servers have been included between clients and the parameter server to aggregate clients’ local models. Recent studies on this edge-assisted hierarchical FL scheme have focused on ensuring or accelerating model convergence by coping with various factors, e.g., uncertain network conditions, unreliable clients, heterogeneous compute resources, etc. This paper presents our three new discoveries of the edge-assisted hierarchical FL scheme: 1) it wastes significant time during its two-phase training rounds; 2) it does not recognize or utilize model diversity when producing a global model; and 3) it is vulnerable to model poisoning attacks. To overcome these drawbacks, we propose FedEdge, a novel edge-assisted hierarchical FL scheme that accelerates model training with asynchronous local federated training and adaptive model aggregation. Extensive experiments are conducted on two widely-used public datasets. The results demonstrate that, compared with state-of-the-art FL schemes, FedEdge accelerates model convergence by 1.14 × −3.20 ×, and improves model accuracy by 2.14% - 6.63%.

Powered by the massive data generated by the blossom of mobile and Web-of-Things (WoT) devices, Deep Neural Networks (DNNs) have developed both in accuracy and size in recent years. Conventional cloud-based DNN training incurs rapidly-increasing data and model transmission overheads as well as privacy issues. Mobile edge computing (MEC) provides a promising solution by facilitating DNN model training on edge servers at the network edge. However, edge servers often suffer from constrained resources and need to collaborate on DNN training. Unfortunately, managed by different telecoms, edge servers cannot properly collaborate with each other without incentives and trust. In this paper, we introduce PipeEdge, a scheme that promotes collaborative edge training between edge servers by introducing incentives and trust based on blockchain. Under the PipeEdge scheme, edge servers can hire trustworthy workers for pipelined DNN training tasks based on model parallelism. We implement PipeEdge and evaluate it comprehensively with four different DNN models. The results show that it outperforms state-of-the-art schemes by up to 173.98% with negligible overheads.

Recently, many data mining models based on heterogeneous graph (HG) have emerged. Among these, HG embedding is an important and indispensable process. However, the existing HG embedding models usually use graph neural network to learn embeddings separately on different meta-paths, ignoring the fact that different meta-paths contain not only their unique semantics but also related semantics. This may result in semantic overlap or irrelevance, which needs to be a feasible and effective tradeoff for high-quality HG embedding, yet studies thereof have rarely been reported. In this article, we propose semantic tradeoff HG embedding (STHGE) by first introducing the Hilbert–Schmidt independence criterion (HSIC) as restriction. The main idea of STHGE is to regard semantic tradeoff as independence tradeoff (or correlation) between different meta-path spaces. Specifically, we first transform the original features of nodes into different meta-path feature spaces with HSIC restriction between them. Then, we use graph attention network to learn the embeddings of nodes on different meta-paths with HSIC restrictions. Finally, we concatenate the embeddings on different meta-paths to perform prediction. Experimental results on three heterogeneous datasets not only demonstrate the effectiveness of STHGE but also demonstrate that STHGE can achieve a new semantic tradeoff between different meta-paths. Furthermore, we demonstrate the robustness of STHGE.

To meet the rapidly increasing need of computation-intensive and latency-sensitive applications, mobile edge computing (MEC) has attracted tremendous attention from both academia and industry. However, the runtime reliability of edge services fluctuates over time due to the dynamics in their internal states and the external environment. This causes the distribution of edge services’ reliability data streams to vary in the form of concept drift. Severe negative reliability drifts indicate that an edge service may be suffering from a performance anomaly or a runtime failure. To ensure the stable operation of edge services, we propose A-Detection, a concept drift-based runtime reliability anomaly detection approach for edge services adaptation. We integrate reservoir sampling and singular value decomposition (SVD) for large-scale streaming data sampling and feature extraction. Jensen Shannon (JS) divergence is utilized to develop a dissimilarity metric of data stream distribution, called FDC, for runtime edge service reliability anomaly detection. When an anomaly is detected in a running edge service, checkpoint-retry is combined with computation offloading to implement runtime reliability adaptation. Extensive experimental results verify and demonstrate the effectiveness and efficiency of A-Detection.

Mobile edge computing (MEC) raises a variety of new challenges for app vendors, including the Edge User Allocation (EUA) problem. EUA aims to allocate as many app users as possible in an MEC system to minimum edge servers in the system. In non-orthogonal multiple access (NOMA)-based MEC system, multiple app users can be allocated to the same subchannel on an edge server through transmit power allocation based on their intra-cell and inter-cell interference. However, allocating excessive app users to the same subchannel may result in severe interference and consequently impact app users’ data rates. In addition, in an MEC system, app users join and depart randomly, and thus need to be allocated in an online manner. Existing EUA approaches suffer from poor performance in dynamic real-world NOMA-based MEC systems because they allocate app users in an offline manner and do not consider the complication caused by NOMA. In this paper, we propose OL-EUA, an OnLine approach for solving dynamic EUA problems in NOMA-based MEC systems. Its performance is theoretically analyzed and experimentally evaluated on a public dataset.

Similar to cloud servers, edge servers running 24/7 in a mobile edge computing (MEC) system consume a large amount of energy, and thus require demand response management. Demand response has been widely employed to reduce energy consumption at data centers. However, existing demand response approaches for data centers are rendered obsolete by the new and unique characteristics of MEC systems: 1) proximity constraint - mobile users can be served by neighbor edge servers only; 2) latency constraint - mobile users' workloads should be processed by their neighbor edge servers to ensure low latency; and 3) capacity constraint - edge servers have limited computing and communication resources to serve mobile users. Demand response for MEC is further complicated by the non-orthogonal multiple access (NOMA) scheme - the emerging radio access scheme for 5G. Communication resources like channels and transmit power must be systematically considered with computing resources like CPU, memory and storage to fulfil mobile users' resource demands. This paper makes the first attempt to tackle this Edge Demand Response (EDR) problem. We first formulate this problem and prove its NP-hardness. Then, we propose a two-phase game-theoretical approach (EDRGame) to solve the EDR problem. Its performance is theoretically analyzed and experimentally evaluated against state-of-the-art approaches on a widely-used real-world dataset.

Edge intelligence, as a prospective paradigm for accelerating DNN inference, is mostly implemented by model partitioning which inevitably incurs the large transmission overhead of DNN's intermediate data. A popular solution introduces multi-exit DNNs to reduce latency by enabling early exits. However, existing work ignores the correlation between exit settings and synergistic inference, causing incoordination of device-to-edge. To address this issue, this paper first investigates the bottlenecks of executing multi-exit DNNs in edge computing and builds a novel model for inference acceleration with exit selection, model partition, and resource allocation. To tackle the intractable coupling subproblems, we propose a Multi-exit DNN inference Acceleration framework based on Multi-dimensional Optimization (MAMO). In MAMO, the exit selection subproblem is first extracted from the original problem. Then, bidirectional dynamic programming is employed to determine the optimal exit setting for an arbitrary multi-exit DNN. Finally, based on the optimal exit setting, a DRL-based policy is developed to learn joint decisions of model partition and resource allocation. We deploy MAMO on a real-world testbed and evaluate its performance in various scenarios. Extensive experiments show that it can adapt to heterogeneous tasks and dynamic networks, and accelerate DNN inference by up to 13.7x compared with the state-of-the-art.

This study tackles the online user allocation problem in mobile edge computing (MEC) systems powered by non-orthogonal multiple access. App vendors need to determine a proper wireless channel in a base station/edge server and sufficient transmit power for every user. We consider a stochastic MEC system where users arrive and depart over time. When an edge server runs out of computing resources, some users will have to wait until the resources become available again, which incurs an allocation delay cost. This cost is often not investigated in many studies, which also do not consider a multi-cell, multi-channel system as we do in this work, due to its complexity. We aim to minimize the allocation delay and transmit power costs, increasing the system’s energy efficiency. To achieve this objective while guaranteeing users’ data rate requirements over time, we adopt the Lyapunov framework to convert this long-term optimization problem into a series of subproblems to be solved in every time slot. To solve the aforementioned subproblems efficiently, we present a distributed game theory-based approach. The proposed algorithm is theoretically evaluated and experimentally demonstrated to outperform several baseline and state-of-the-art methods, highlighting the significance of systematic consideration for both computation and communication aspects of this problem.

Mobile Edge Computing (MEC) has emerged to overcome the inability of cloud computing to offer low latency services. It allows popular data to be cached on edge servers deployed within users' geographic proximity. However, the storage resources on edge servers are constrained due to their limited physical sizes. Existing studies of edge caching have predominantly focused on maximizing caching performance from the mobile network operator's perspective, e.g., maximizing data retrieval success rate, minimizing system energy consumption, balancing the overall caching workload, etc. App vendors, as key stakeholders in MEC systems, need to maximize the caching revenue, considering the cost incurred and the benefit produced. We investigate this novel Mobile Edge Data Caching (MEDC) problem from the app vendor's perspective, and prove its NP-hardness. We then propose Online MEDC (OL-MEDC), an approach that formulates MEDC strategies for app vendors, without requiring future information about data demands. Its performance is theoretically analyzed and experimentally evaluated. The experimental results demonstrate that OL-MEDC outperforms state-of-the-art approaches by at least 20.41\% on average.

The graph neural network (GNN) has demonstrated its superior power in various data mining tasks and has been widely applied in diversified fields. The core of GNN is the aggregation and combination functions, and mainstream GNN studies focus on the enhancement of these functions. However, GNNs face a common challenge, i.e., useless features contained in neighbor nodes may be integrated into the target node during the aggregation process. This leads to poor node embedding and undermines downstream tasks. To tackle this problem, this article proposes a novel GNN optimization framework GNN-MHSIC by introducing the nonparametric dependence method Hilbert-Schmidt independence criterion (HSIC) under the guidance of information bottleneck. HSIC is utilized to guide the information propagation among layers of a GNN from multiaspect views. GNN-MHSIC aims to achieve three main objectives: 1) minimizing the HSIC between the input features and the propagation layers; 2) maximizing the HSIC between the propagation layers and the ground truth; and 3) minimizing the HSIC between the propagation layers. With a multiaspect design, GNN-MHSIC can minimize the propagation of redundant information while preserving relevant information about the target node. We prove GNN-MHSIC's finite upper and lower bounds theoretically and evaluate it experimentally with four classic GNN models, including the graph convolutional network, the graph attention network (GAT), the heterogeneous GAT, and the heterogeneous graph (HG) propagation network on three widely used HGs. The results illustrate the usefulness and performance of GNN-MHSIC.

With the explosive growth of the Internet of Things (IoT) devices deployed in edge networks, the functionalities of IoT devices are typically encapsulated as IoT services, and user requests can be achieved through the composition of data and/or computation-intensive IoT services. Considering the prediction-uncertainty of forthcoming requests, certain IoT services may (i) not be hosted currently by appropriate IoT devices, or (ii) such an IoT service exists, but its non-functional properties may hardly be satisfied with respect to certain constraints prescribed by requests. To address this challenge, this paper proposes an efficiency-aware service Migration Scheduling (denoted eMS) mechanism in edge networks, in order to migrate IoT services on-demand, and thus, to optimally settle non-satisfiable constraints. Specifically, IoT services are re-scheduled, such that certain IoT services are migrated from their hosting IoT devices to neighboring ones, while minimizing the energy consumption and average delay caused by this service re-scheduling operation. We formulate this service re-scheduling as a multi-objective and multi-constraint optimization problem, which is solved through integrating the greedy algorithm into the fast non-dominated sorting and crowded-comparison operators as the hybrid genetic algorithm (G-NSGA-II). Based on real-life datasets provided by an oil pipeline monitoring project, extensive experiments are conducted, and evaluation results show that our eMS is promising in reducing the energy consumption and average delay of service re-scheduling in comparison with the state-of-art’s techniques.

With the rollout of the 5G network around the globe, a massive number of edge servers have been deployed to host online applications demanding low service latency for users. These edge servers constitute multi-access edge computing (MEC) systems. Running 24/7, edge servers consume tremendous energy and take up a great part of global carbon emissions. The edge energy-saving (EES) problem is needed to facilitate energy-efficient edge resource provisions. Unfortunately, existing energy-saving approaches designed for data centers are becoming impractical. First, edge servers are used to provide services to a specific geographical area. Its energy utilization is impacted by the temporal distribution of users within its coverage. Second, a user could be accommodated by any of its neighbor edge servers. Third, it is possible to activate and deactivate individual physical machines that facilitate an edge server as needed. Thus, EES is designed to save the system energy of physical machines in a long term by serving the users over time. EES problem has been formulated systematically and its problem hardness has been analyzed theoretically, then we propose EESaver (Edge Energy Saver) for formulating EES strategies dynamically over time to facilitate green MEC. EESaver's superior performance is tested comprehensively.

In themobile edge computing(MEC) environment, edge servers with storage and computing resources are deployed at base stations within users’ geographic proximity to extend the capabilities of cloud computing to the network edge.Edge storage system(ESS), is comprised by connected edge servers in a specific area, which ensures low-latency services for users. However, high data storage overheads incurred by edge servers’ limited storage capacities is a key challenge in ensuring the performance of applications deployed on an ESS. Data deduplication, as a classic data reduction technology, has been widely applied in cloud storage systems. It also offers a promising solution to reducing data redundancy in ESSs. However, the unique characteristics of MEC, such as edge servers’ geographic distribution and coverage, render cloud data deduplication mechanisms obsolete. In addition, data distribution must be balanced over edge storage systems to accommodate future data demands, which cannot be undermined by data deduplication. Thus,balanced edge data deduplication(BEDD) must consider deduplication ratio, data storage benefits, and resource balance systematically under the latency constraint. In this article, we model the novel BEDD problem formally and prove its$\mathcal {NP}$-hardness. Then, we propose an optimal approach for solving the BEDD problem exactly in small-scale scenarios and a sub-optimal approach to solve large-scale BEDD problems with a theoretical performance guarantee. Extensive and comprehensive experiments conducted on a real-world dataset demonstrate the significant performance improvements of our approaches against four representative approaches.

Multi-access Edge Computing (MEC) has emerged as a new distributed computing paradigm for its ability to offer low-latency services to users. Suffering from constrained computational resources because of their limited physical sizes, edge servers usually cannot handle all the incoming compute tasks on time when they operate independently. Thus, they need to cooperate by peer-offloading. Incentive and trust are the two major challenges towards to cooperative computing among edge servers operating in a distrusted environment. Another specific challenge in the MEC environment is to facilitate incentive and trust in a decentralized manner. This article proposes CoopEdge+, a novel blockchain-based decentralized platform, to drive and support cooperative multi-access edge computing to tackle these challenges in a systematic manner. On CoopEdge+, an edge server can publish a compute task for other edge servers to contend for. A winner is selected from candidate edge servers as the task executor based on their reputation to perform the compute task. After that, CoopEdge+ employs a random leader election scheme to elect a task recorder without revealing its leadership until its consensus epoch. The task recorder will coordinate a consensus among edge servers to record the task executor's performance on blockchain. We implement CoopEdge+ based on Hyperledger fabric and evaluate it experimentally against a baseline implementation and three state-of-the-art implementations in a simulated MEC environment. The results validate the usefulness of CoopEdge+ and demonstrate its performance.

Mobile edge computing (MEC) provides a new distributed computing paradigm that overcomes the inability of cloud computing to offer low end-to-end latency. In a MEC environment, app vendors can deliver lower-latency services to mobile app users by placing applications on edge servers in close proximity to app users. From an app vendor's perspective, an optimal edge application placement strategy under a budget ($k$) constraint aims to place application instances on$k$edge servers within a specific area to maximize user coverage. However, edge servers may be subject to failure due to multiple reasons, e.g., hardware faults, software exceptions, cyber-attacks, etc. App users served by failed edge servers need to access applications from remote cloud servers if they cannot access any other edge servers. This impacts app users’ quality of experience significantly. Thus, app vendors need to consider the reliability of the edge server network when choosing edge servers for placing their application instances. We make the first attempt in this paper to tackle this problem of joint optimization of coverage and reliability for edge application placement (EAP-CR). We formulate this problem as a constrained optimization problem and prove its$\mathcal {NP}$-hardness theoretically. Then, we propose an optimal approach to find the optimal solutions with the integer programming technique, an approximation approach is also proposed to find approximate solutions for large-scale EAP-CR problems. We evaluate EAP-$OPT$and EAP-$APX$against three relevant approaches through experiments conducted on a widely-used real-world data set and a synthetic data set. The results demonstrate that our proposed approaches can solve the EAP-CR problem effectively and efficiently.

In this paper, we propose LBS@E, a new architecture for location-based services (LBSs) facilitated by the mobile edge computing paradigm. LBS@E tackles the location privacy problem innovatively by delocalizing LBSs so that mobile users of LBSs implemented based on LBS@E do not have to reveal their locations. They retrieve local information from nearby edge servers around them instead of the cloud. In this way, we resolve the root cause of the conventional location privacy problem. However, LBS@E raises new challenges to location privacy. A mobile user can still be localized to a particular privacy area co-covered by the edge servers accessed by the mobile user. A small privacy area puts the mobile users location at the risk of being approximated. In the meantime, the size of the utility area, which determines the amount of local information retrievable for the mobile user, is positively correlated with the number of edge servers accessed by the mobile user. We model this problem as a constrained optimization problem and propose an optimal approach for solving it based on integer programming. Extensive experiments are conducted on a widely-used real-world dataset to demonstrate effectiveness and efficiency.

Edge computing, as a new computing paradigm, brings cloud computing’s computing and storage capacities to network edge for providing low latency services for users. The networked edge servers in a specific area constituteedge storage systems(ESSs), where popular data can be stored to serve the users in the area. The novel ESSs raise many new opportunities as well as unprecedented challenges. Most existing studies of ESSs focus on the storage of data replicas in the system to ensure low data retrieval latency for users. However, replica-based edge storage strategies can easily incur high storage costs. It is not cost-effective to store massive replicas of large-size data, especially those that do not require real-time access at the edge, e.g., system upgrade files, popular app installation files, videos in online games. It may not even be possible due to the constrained storage resources on edge servers. In this article, we make the first attempt to investigate the use of erasure codes in cost-effective data storage at the edge. The focus is to find the optimal strategy for placing coded data blocks on the edge servers in an ESS, aiming to minimize the storage cost while serving all the users in the system. We first model this novelErasure Coding based Edge Data Placement(EC-EDP) problem as an integer linear programming problem and prove its$\mathcal {NP}$-hardness. Then, we propose an optimal approach named EC-EDP-O based on integer programming. Another approximation algorithm named EC-EDP-V is proposed to address the high computation complexity of large-scale EC-EDP scenarios efficiently. The extensive experimental results demonstrate that EC-EDP-O and EC-EDP-V can save an average of 68.58% (and up to 81.16% in large-scale scenarios) storage cost compared with replica-based storage approaches.

Edge computing is a promising solution to enabling highly accessible resources and latency-sensitive services for nearby users. In public safety, it can provide critical support for urban crowd/hazard management services, such as real-time path planning, hazard warning, etc. In a crowd/hazard scenario, crowds can be allocated to nearby edge servers for obtaining real-time support, e.g., evacuation instructions for those who want to evacuate and crowd flow updates for those who want to rescue, etc. In such scenarios, the behaviors of different roles (like rescuers and evacuees) and the positive/negative interactions among them must be considered in user allocation for reducing injuries and fatalities. In this paper, these issues are defined as a novelRole-Based Criticality(RBC) model to describe the fatal risks of different roles in the crowd/hazard scenarios. Based on the model, theRole-Based User Allocation(RUA) problem is formulated. To tackle this problem, we devise an optimal solution named RUA-ILP based on Integer Linear Programming. To accommodate large-scale scenarios, we propose two representative approximate approach named RUA-A and RUA-GA to ensure efficient and effectiveness user allocation respectively. They can maximize the overall role-based criticality which can reduce injuries and fatalities in crowd/hazard scenarios by theoretical proofing and extensive experiments conducted on a real-world dataset.

Edge computing provides a novel computing paradigm by deploying services on edge servers to serve nearby end-users with low latency. In this regard, a suitable allocation strategy is crucial that maximizes the number of users served at the minimum overall cost, which is referred to as the Edge User Allocation (EUA) problem. However, when edge computing meets public safety, some critical issues have not been fully considered by existing EUA approaches. Among these issues, the levels of danger to individuals quantitatively indicate whether individuals are in danger in an emergency. Hence, the inclusion of these levels impacts the priority for allocating resources in the EUA problem. In this paper, these levels are defined as individual criticalities formally. Then, we take them into account to formulate the novel CRiticality-EUA (CR-EUA) problem, and prove its NP-hardness. To solve this problem, an optimal approach, named CR-EUA-O, is proposed by utilizing the Integer Programming technique. Furthermore, we propose an approach with a proven approximation ratio, named CR-EUA-H, as an effective and efficient solution. Experiments are conducted on a real-world dataset to evaluate our approaches against four representative approaches. The results show the superior performance of our approaches in the overall criticality and execution time.

As a nascent technique, Mobile Edge Computing (MEC) is mushrooming with a broad application prospect. By transferring abundant computing and storage resources from cloud to edge servers close to users, it allows services to be hosted on edge servers, which greatly reduces service latency. However, due to environmental dynamics, the edge services' reliability fluctuates in real-time. When the reliability of an edge service degrades severely, it may be suffering an anomaly, which can seriously impact its real-time performance and users' quality of experience. To ensure the real-time performance of edge services, this paper presents CDCrest, a concept drift-based checkpoint restart approach for edge service rejuvenation. CDCrest employs L-Detection, a concept drift-based approach to detect edge services' runtime reliability anomalies. L-Detection leverages a sliding window and Locality Sensitive Hashing (LSH)-based sampling to extract features from an edge service's real-time and historic reliability data streams. Then, it calculates the real-time Distribution Change Degree (DCD) based on Jensen-Shannon (JS) divergence to infer whether the edge service is suffering a reliability anomaly. Once an anomaly for an edge service is identified, CDCrest employs a checkpoint restart mechanism to ensure the rapid rejuvenation of the edge service. Extensive experiments conducted based on a popular real-world dataset demonstrate the effectiveness and efficiency of CDCrest against the state-of-the-art approaches.

Mobile edge computing (MEC) provides a new computing paradigm that can overcome the inability of the traditional cloud computing paradigm to ensure low service latency by pushing computing power and resources to the network edge. Many studies have attempted to formulate edge data caching strategies for app vendors to optimize caching performance by caching the right data on the right edge servers. However, existing edge data caching approaches have unfortunately ignored fairness, which is an important issue from the app vendor's perspective. In general, an app vendor needs to cache data on edge servers to serve its users with insignificant latency differences at a minimum caching cost. In this paper, we make the first attempt to tackle the fair edge data caching (FEDC) problem. Specifically, we formulate the FEDC problem as a constraint optimization problem (COP) and prove its$\mathcal {NP}$-hardness. An optimal approach named FEDC-OPT is proposed to find optimal solutions to small-scale FEDC problems with integer programming technique. In addition, an approximate algorithm named FEDC-APX is proposed to find approximate solutions in large-scale FEDC problems. The performance of the proposed approaches is analyzed theoretically, and evaluated experimentally on a widely-used real-world data set against four representative approaches. The experimental results show that the proposed approaches can solve the FEDC problem efficiently and effectively.

Recently, edge user allocation (EUA) problem has received much attentions. It aims to appropriately allocate edge users to their nearby edge servers. Existing EUA approaches suffer from a series of limitations. First, considering users' service requests only as a whole, they neglect the fact that a service request may be partitioned into multiple tasks to be performed by different servers. Second, the impact of the spatial distance between edge users and servers on users' quality of experience is not properly considered. Third, the temporal dynamics of users' service requests has not been fully investigated. To overcome these limitations systematically, this paper focuses on the problem of spatio-temporal edge user allocation with task decomposition (ST-EUA). We first formulate the ST-EUA problem. Then, we transform ST-EUA problem as a multi-objective optimization problem and prove its NP-hardness. To tackle the ST-EUA problem effectively and efficiently, we propose a novel genetic algorithm-based heuristic approach GA-ST, aiming to maximize usersoverall QoE while minimizing migration cost in different time slots. Extensive experiments are conducted on two widely-used real-world datasets to evaluate the performance of GA-ST. The results demonstrate that GA-ST significantly outperforms state-of-the-art approaches in finding approximate solutions in terms of the trade-off among multiple metrics.

Modern large-scale service systems are usually deployed with redundant components to ensure high dependability in distributed and volatile environments. Fault Injection Testing (FIT) is a popular technique for testing such systems, while the application of FIT to validating the correctness of redundant components remains a challenging task, especially when the system's structural information is unavailable when testing starts. In this study, we refer to a minimum set of faults that, when injected, will cut off all execution paths in a service system as afault-tolerance bottleneck, and we propose a novel Fault-tolerance Bottleneck driven Fault Injection (FBFI) approach to the exploration and validation of redundant components without prior knowledge of the system's business structure. The core idea of FBFI is to iteratively infer and inject bottlenecks of the business structure constructed so far. In this way, FBFI is able to discover and test redundant components by repeatedly triggering new system behaviors. The effectiveness and efficiency of FBFI is evaluated using two microservice benchmark systems with different deployment scales. The results reveal that FBFI is more practical and cost-effective than random and lineage-driven FIT approaches in testing service systems of high redundancy levels.

Android developers are often faced with the need to learn how to use different APIs suitable for their projects. Automated API recommendation approaches have been invented to help fill this gap, and these have been demonstrated to be useful to some extent. Unfortunately, most state-of-the-art works are not proposed for Android developers, and the ones dedicated to Android app development often suffer from high redundancy and poor run-time performance, or do not target the problem of recommending API usage patterns. To address this gap we propose to the community a new tool, namelyAPIMatchmaker, to recommend API usages by learning directly from similar real-world Android apps. Unlike existing recommendation approaches, which leverage a single context to find similar projects, we innovatively introduce a multi-dimensional, context-aware, collaborative filtering approach to better achieve the purpose. Specifically, in addition to code similarity, we also take app descriptions (or topics) into consideration to ensure that similar apps also provide similar functions. We evaluateAPIMatchmakeron a large number of real-world Android apps and observe thatAPIMatchmakeryields a high success rate in recommending APIs for Android apps under development, and it is also able to outperform the state-of-the-art.

Similar to cloud servers which are well-known energy consumers, edge servers running 24/7 jointly consume a tremendous amount of energy and thus require energy-saving management. However, the unique characteristics of edge computing make it a new and challenging problem to manage edge servers in an energy-efficient manner. First, an individual edge server is usually used to serve a specific region. The temporal distribution of end-users in the area impacts the edge server’s energy utilization. Second, multiple base stations may cover an end-user simultaneously and the end-user can be served by the physical machines attached to any of the base stations. Serving the end-users in an area with minimum physical machines can minimize the edge servers’ overall energy consumption. Third, physical machines facilitating an edge server can be powered off individually when not needed to minimize the edge server’s energy consumption. We formulate this Energy-efficient Edge Server Management (EESM) problem and analyze its problem hardness. Next, a game-theoretical approach, i.e., EESM-G, is proposed to address EESM problems efficiently. The superior performance of EESM-G is tested on a public real-world dataset.

Networked edge servers constitute an edge storage system in edge computing (EC). Upon users’ requests, data must be delivered from edge servers in the system or from the cloud to users. Existing studies of edge storage systems have unfortunately neglected the fact that an excessive number of users accessing the same edge server for data may impact users’ data rates seriously due to the wireless interference. Thus, users must first be allocated to edge servers properly for ensuring their data rates. After that, requested data can be delivered to users to minimize their average data delivery latency. In this paper, we formulate this Interference-aware Data Delivery at the network Edge (IDDE) problem, and demonstrate its NP-hardness. To tackle it effectively and efficiently, we propose IDDE-G, a novel approach that first finds a Nash equilibrium as the strategy for allocating users. Then, it finds an approximate strategy for delivering requested data to allocated users. We analyze the performance of IDDE-G theoretically and evaluate its performance experimentally to demonstrate the effectiveness and efficiency of IDDE-G on solving the IDDE problem.

Mobile edge computing (MEC) offers the infrastructure for improving data caching performance structurally by deploying edge servers at the network edge within users' close geographic proximity. Popular data like viral videos can be cached on edge servers to serve users with low latency. Investigating the integrity of these edge data is critical and challenging as edge servers often suffer from unreliability and constrained resources. Meanwhile, EDI (edge data integrity) investigation must be performed by edge servers collaboratively at the edge to avoid excessive backhaul network traffic. There are two main challenges in practice: 1) there is a lack of Byzantine-tolerant collaborative investigation method; and 2) edge servers may be reluctant to collaborate without proper incentives. To tackle these challenges systematically, this paper proposes a novel scheme named EdgeWatch to enable robust and collaborative EDI investigation in a decentralized manner based on blockchain. Under EdgeWatch, edge servers collaborate on EDI investigation following a novel integrity consensus. A blockchain system comprises of three main components is built as the infrastructure to facilitate integrity consensus: 1) an incentive mechanism that motivates edge servers to participate in EDI investigation; 2) a reputation system that elects reliable leaders for block consensus; and 3) a leader randomization technique that protects leaders from targeted attacks. We evaluate it against three representative schemes experimentally. The results demonstrate the high precision, efficiency, and robustness of EdgeWatch.

Mobile edge computing (MEC) is a new distributed computing paradigm where edge servers are deployed at, or near cellular base stations in close proximity to end-users. This offers computing resources at the edge of the network, facilitating a highly accessible platform for real-time, latency-sensitive services. A typical MEC environment is highly stochastic with random user arrivals and departures over time. Here, we address the user allocation problem from a service provider’s perspective, who needs to allocate its users to the cloud or edge servers in a specific area. A user, who has a multi-dimensional resource requirement, can be allocated to either the remote cloud, which incurs a high latency, or an edge server, which results in a low latency but might require the user to wait in a queue. This study aims to achieve a controllable trade-off between performance (throughput) and several associated costs such as queuing delay and latency costs. We model this problem as a stochastic optimization problem, propose SUAC (Stochastic User AlloCation) – an online Lyapunov optimization-based algorithm, and prove its performance bounds. The experimental results demonstrate that SUAC outperforms existing approaches, effectively allocating users with a desired trade-off while keeping the system strongly stable.

[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2022.3142265]

With the ever-increasing popularity of microservice architecture, a considerable number of enterprises or organizations have encapsulated their complex business services into various lightweight functions as published them accessible APIs (Application Programming Interfaces). Through keyword search, a software developer could select a set of APIs from a massive number of candidates to implement the functions of a complex mashup, which reduces the development cost significantly. However, traditional keyword search methods for APIs often suffer from several critical issues such as functional compatibility and limited diversity in search results, which may lead to mashup creation failures and lower development productivity. To deal with these challenges, this paper designs DAWAR, a diversity-aware Web APIs recommendation approach that finds diversified and compatible APIs for mashup creation. Specifically, the APIs recommendation problem for mashup creating is modelled as a graph search problem that aims to find the minimal group Steiner trees in a correlation graph of APIs. DAWAR innovatively employs the determinantal point processes to diversify the recommended results. Empirical evaluation is performed on commonly-used real-world datasets, and the statistic results show that DAWAR is able to achieve significant improvements in terms of recommendation diversity, accuracy, and compatibility.

Maximal clique, the most cohesive structure in a graph, has a broad range of applications, e.g., community detection, bioinformatics, anomaly detection, and graph visualization. However, the sheer number of maximal cliques brings the challenge to fully examine them all. In addition, the omnipresent overlaps between cliques imply that it may not be necessary to process every maximal clique, since many vertices are shared in multiple cliques. A real example is that, in commercial advertising, a small group of individuals who participate in different communities can help spread an advertisement across all the communities. Inspired by this observation, we study the problem of finding a τ-cover, which is a subset of vertices in a graph. This subset overlaps with each maximal clique by no less than τ, where τ is a threshold reflecting the user's requirement. We prove the NP-hardness and the non-submodularity of finding a minimum τ-cover. As a result, to find a small τ-cover as best effort, we propose three methods: MCCb, MCC, and EMCC. MCCb is a baseline that adds vertices into the cover while doing clique enumeration until the coverage requirement is satisfied. MCC decides whether to add a vertex with more caution by evaluating the increment of coverage lower bound with O(1) time complexity. EMCC is a randomized algorithm built on an elegant adaptive sampling, which further achieves cover conciseness by relaxing the coverage requirement in a statistical manner. Extensive experiments show that MCC (1.3 ∽ 2.5 × faster) produces a cover whose size is 1/2 of MCCb, and EMCC (2 ∽ 5 × faster) averagely produces a cover whose size is one order of magnitude smaller vs. MCCb.

Machine learning (ML) is powering a rapidly-increasing number of web applications. As a crucial part of 5G, edge computing facilitates edge artificial intelligence (AI) by ML model training and inference at the network edge on edge servers. Compared with centralized cloud AI, edge AI enables low-latency ML inference which is critical to many delay-sensitive web applications, e.g., web AR/VR, web gaming and Web-of-Things applications. Existing studies of edge AI focused on resource and performance optimization in training and inference, leveraging edge computing merely as a tool to accelerate training and inference processes. However, the unique ability of edge computing to process data with context awareness, a powerful feature for building the web-of-things for smart cities, has not been properly explored. In this paper, we propose a novel framework named Pyramid that unleashes the potential of edge AI by facilitating homogeneous and heterogeneous hierarchical ML inferences. We motivate and present Pyramid with traffic prediction as an illustrative example, and evaluate it through extensive experiments conducted on two real-world datasets. The results demonstrate the superior performance of Pyramid neural networks in hierarchical traffic prediction and weather analysis.

Mobile-edge computing (MEC), as an emerging computing paradigm, allows app vendors to deploy their mobile and/or IoT applications on edge servers to deliver low-latency services to their app users. However, when an edge server needs to serve excessive app users concurrently, severe interference is incurred, which immediately reduces app users’ achievable data rates and, consequently, impacts their perceived service quality. This is a major challenge to the app vendor’s attempt to minimize the edge resources required for serving its app users with a satisfactory service quality. To tackle this challenge, in this article, we present and formulate this multiple edge application deployment (MEAD) problem in the MEC environment, aiming to maximize app users’ overall service quality at minimum deployment cost, considering application shareability and communication interference. We prove that the MEAD problem is$\mathcal {NP}$-hard. Then, we propose a heuristic approach, namely, the deployment-priority greedy via the divide-and-conquer strategy (DPG-D&C), to solve the MEAD problem effectively and efficiently. We evaluate our approach extensively by using a widely used real-world data set. The experimental results show that DPG-D&C significantly outperforms state-of-the-art approaches.

There has been a very large growth in interest in big data analytics to discover patterns and insights. A major challenge in this domain is the need to combine domain knowledge – what the data means (semantics) and what it is used for – with advanced data analytics and visualization techniques to mine and communicate important information from the huge volumes of raw data. Many data analytics tools have been developed for both research and practice to assist in specifying, integrating and deploying data analytics applications. However, delivering such big data analytics applications requires a capable team with different skillsets including data scientists, software engineers and domain experts. Such teams and skillsets usually take a long time to build and have high running costs. An alternative is to provide domain experts and data scientists – the end users – with tools they can use to create and deploy complex data analytics application solutions directly with less technical skills required. In this paper we present a survey and analysis of several current research and practice approaches to supporting data analytics for end-users, identifying key strengths, weaknesses and opportunities for future research.

The ever-increasing popularity of web APIs allows app developers to leverage a set of existing web APIs to achieve their sophisticated objectives. The heavily fragmented distribution of web APIs makes it challenging for an app developer to find appropriate and compatible web APIs. Currently, app developers usually have to manually discover candidate web APIs, verify their compatibility and select appropriate and compatible ones. This process is cumbersome and requires detailed knowledge of web APIs which is often too demanding. It has become a major obstacle to further and broader applications of web APIs. To address this issue, we first propose a web API correlation graph built on extensive data about the compatibility between web APIs. Then, we propose WAR (WebAPIsRecommendation), the first data-driven approach for web APIs recommendation that integrates web API discovery, verification and selection operations based on keywords search over the web API correlation graph. WAR assists app developers without detailed knowledge of web APIs in searching for appropriate and compatible web APIs by typing a few keywords that represent the tasks required to achieve app developers’ objectives. WAR can significantly save app developers’ time and effort in searching for web APIs. We conducted large-scale experiments on 18,478 real-world web APIs and 6,146 real-world apps to demonstrate the usefulness and efficiency of WAR.

Latent factor analysis (LFA) via stochastic gradient descent (SGD) is highly efficient in discovering user and item patterns from high-dimensional and sparse (HiDS) matrices from recommender systems. However, most LFA-based recommender systems adopt a standard SGD algorithm, which suffers limited scalability when addressing big data. On the other hand, most existing parallel SGD solvers are either under the memory-sharing framework designed for a bare machine or suffering high communicational costs, which also greatly limits their applications in large-scale systems. To address the above issues, this article proposes a distributed alternative stochastic gradient descent (DASGD) solver for an LFA-based recommender. Its training-dependences among latent features are decoupled via alternatively fixing one-half of the features to learn the other half following the principle of SGD but in parallel. It's distribution mechanism consists of efficient data partition, allocation and task parallelization strategies, which greatly reduces its communicational cost for high scalability. Experimental results on three large-scale HiDS matrices generated by real-world applications demonstrate that the proposed DASGD algorithm outperforms state-of-the-art distributed SGD solvers for recommender systems in terms of prediction accuracy as well as scalability. Hence, it is highly useful for training LFA-based recommenders on large scale HiDS matrices with the help of cloud computing facilities.

How to extract useful knowledge from a high-dimensional and sparse (HiDS) matrix efficiently is critical for many big data-related applications. A latent factor (LF) model has been widely adopted to address this problem. It commonly relies on an iterative learning algorithm like stochastic gradient descent. However, an algorithm of this kind commonly consumes many iterations to converge, resulting in considerable time cost on large-scale datasets. How to accelerate an LF model's training process without accuracy loss becomes a vital issue. To address it, this study innovatively proposes amultilayered-and-randomizedlatentfactor (MLF) model. Its main idea is two-fold: a) adopting randomized-learning to train LFs for implementing a ‘one-iteration’ training process for saving time; and 2) adopting the principle of a generally multilayered structure as in a deep forest or multilayered extreme learning machine to structure its LFs, thereby enhancing its representative learning ability. Empirical studies on six HiDS matrices from real applications demonstrate that compared with state-of-the-art LF models, an MLF model achieves significantly higher computational efficiency with satisfactory prediction accuracy. It has the potential to handle LF analysis on a large scale HiDS matrix with real-time requirements.

Scalable data processing platforms built on cloud computing becomes increasingly attractive as infrastructure for supporting big data applications. But privacy concerns are one of the major obstacles to making use of public cloud platforms. Multidimensional anonymisation, a global-recoding generalisation scheme for privacy-preserving data publishing, has been a recent focus due to its capability of balancing data obfuscation and usability. Existing multidimensional anonymisation methods suffer from scalability problems when handling big data due to the impractical serial I/O cost. Given the recursive feature of multidimensional anonymisation, parallelisation is an ideal solution to scalability issues. However, it is still a challenge to use existing distributed and parallel paradigms directly for recursive computation. In this paper, we propose a scalable approach for big data multidimensional anonymisation based on MapReduce, a state-of-the-art data processing paradigm. Our basic idea is to partition a data set recursively into smaller partitions using MapReduce until all partitions can fit in the memory of a computing node. A tree indexing structure is proposed to achieve recursive computation. Moreover, we show the applicability of our approach to differential privacy. Experimental results on real-life data demonstrate that our approach can significantly improve the scalability of multidimensional anonymisation over existing methods.

Edge Cloud Computing (ECC) provides a new paradigm for app vendors to serve their users with low latency by deploying their services on edge servers attached to base stations or access points in close proximity to mobile users. From the edge infrastructure provider’s perspective, a cost-effective$k$edge server placement ($k$ESP) aims to place$k$edge servers within a particular geographic area to maximize the number of covered mobile users, i.e., to maximize theuser coverage. However, in the distributed and volatile ECC environment, edge servers are subject to failures due to various reasons, e.g., software exceptions, hardware faults, cyberattacks, etc. Mobile users connected to a failed edge server have to access services in the remote cloud if they are not covered by any other edge servers. This significantly impacts mobile users quality of experience. Thus, the robustness of the edge server network (referred to as network robustness hereafter) in a specific area must be considered in edge server placement. In this article, we formally model this joint user coverage and network robustness oriented$k$edge server placement ($k$ESP-CR) problem, and prove that finding the optimal solution to this problem is$\mathcal {NP}$-hard. To tackle this ESP-CR, we first propose an integer programming based optimal approach (namely ESP-O) for finding optimal solutions to small-scale$k$ESP-CR problems. Then, we propose an approximation approach, namely ESP-A, for solving large-scale$k$ESP-CR problems efficiently and theoretically prove its approximation ratio. Finally, the performance of these two approaches are experimentally evaluated against three representative approaches on a widely-used real-world dataset.

Edge Computing, extending cloud computing, has emerged as a prospective computing paradigm. It allows a SaaS (Software-as-a-Service) vendor to allocate its users to nearby edge servers to minimize network latency and energy consumption on their devices. From the SaaS vendor’s perspective, a cost-effective SaaS user allocation (SUA) aims to allocate maximum SaaS users on minimum edge servers. However, the allocation of excessive SaaS users to an edge server may result in severe interference and consequently impact SaaS users’ data rates. In this article, we formally model this problem and prove that finding the optimal solution to this problem is NP-hard. Thus, we propose ISUAGame, a game-theoretic approach that formulates the interference-aware SUA (ISUA) problem as a potential game. We analyze the game and show that it admits a Nash equilibrium. Then, we design a novel decentralized algorithm for finding a Nash equilibrium in the game as a solution to the ISUA problem. The performance of this algorithm is theoretically analyzed and experimentally evaluated. The results show that the ISUA problem can be solved effectively and efficiently.

Edge computing is a new distributed computing paradigm extending the cloud computing paradigm, offering much lower end-to-end latency, as real-time, latency-sensitive applications can now be deployed on edge servers that are much closer to end-users than distant cloud servers. In edge computing, edge user allocation (EUA) is a critical problem for any app vendors, who need to determine which edge servers will serve which users. This is to satisfy application-specific optimization objectives, e.g., maximizing users’ overall quality of experience, minimizing system costs, and so on. In this article, we focus on the cost-effectiveness of user allocation solutions with two optimization objectives. The primary one is to maximize the number of users allocated to edge servers. The secondary one is to minimize the number of required edge servers, which subsequently reduces the operating costs for app vendors. We first model this problem as a bin packing problem and introduce an approach for finding optimal solutions. However, finding optimal solutions to the$\mathcal {NP}$-hard EUA problem in large-scale scenarios is intractable. Thus, we propose a heuristic to efficiently find sub-optimal solutions to large-scale EUA problems. Extensive experiments conducted on real-world data demonstrate that our heuristic can solve the EUA problem effectively and efficiently, outperforming the state-of-the-art and baseline approaches.

In the edge computing environment, app vendors can cache their data on a large number of geographically distributed edge servers to serve their users. However, those cached data are particularly vulnerable to both intentional and accidental corruption, which makes data security a major concern in the EC environment. Given limited computing resources of edge servers, how to effectively and efficiently inspect those data over tremendous edge servers is a critical and open problem. To tackle this edge data integrity (EDI) problem, we first study the entities, threats, system objectives, and the inspection mechanism, then propose a novel approach named EDI-S for inspecting the integrity of edge data and localizing the corrupted ones. Based on the elliptic curve cryptography, EDI-S generates one digital signature as the integrity proof for each replica. Then, multiple integrity proofs can be inspected altogether via an aggregate verification. This allows the integrity of tremendous cache data on multiple edge servers can be inspected more efficiently. EDI-S also provides two methods for localizing the corrupted data on edge servers, one for small-scale scenarios and the other for large-scale scenarios. Both theoretical analysis and experimentally evaluation demonstrate that EDI-S can solve the EDI problem effectively and efficiently.

Clustering big data often requires tremendous computational resources where cloud computing is undoubtedly one of the promising solutions. However, the computation cost in the cloud can be unexpectedly high if it cannot be managed properly. The long tail phenomenon has been observed widely in the big data clustering area, which indicates that the majority of time is often consumed in the middle to late stages in the clustering process. In this research, we try to cut the unnecessary long tail in the clustering process to achieve a sufficiently satisfactory accuracy at the lowest possible computation cost. A novel approach is proposed to achieve cost-effective big data clustering in the cloud. By training the regression model with the sampling data, we can make widely used k-means and EM (Expectation-Maximization) algorithms stop automatically at an early point when the desired accuracy is obtained. Experiments are conducted on four popular data sets and the results demonstrate that both k-means and EM algorithms can achieve high cost-effectiveness in the cloud with our proposed approach. For example, in the case studies with the much more efficient k-means algorithm, we find that achieving a 99 percent accuracy needs only 47.71-71.14 percent of the computation cost required for achieving a 100 percent accuracy while the less efficient EM algorithm needs 16.69-32.04 percent of the computation cost. To put that into perspective, in the United States land use classification example, our approach can save up to $94,687.49 for the government in each use.

Edge computing (EC) is an emerging paradigm that extends cloud computing by pushing computing resources onto edge servers that are attached to base stations or access points at the edge of the cloud in close proximity with end-users. Due to edge servers’ geographic distribution, the EC paradigm is challenged by many new security threats, including the notorious distributed Denial-of-Service (DDoS) attack. In the EC environment, edge servers usually have constrained processing capacities due to their limited sizes. Thus, they are particularly vulnerable to DDoS attacks. DDoS attacks in the EC environment render existing DDoS mitigation approaches obsolete with its new characteristics. In this article, we make the first attempt to tackle the edge DDoS mitigation (EDM) problem. We model it as a constraint optimization problem and prove its$\mathcal {NP}$-hardness. To solve this problem, we propose an optimal approach named EDMOpti and a novel game-theoretical approach named EDMGame for mitigating edge DDoS attacks. EDMGame formulates the EDM problem as a potential EDM Game that admits a Nash equilibrium and employs a decentralized algorithm to find the Nash equilibrium as the solution to the EDM problem. Through theoretical analysis and experimental evaluation, we demonstrate that our approaches can solve the EDM problem effectively and efficiently.

Recent advances in localization techniques have fundamentally enhanced social networking services, allowing users to share their locations and location-related contents. This has further increased the popularity of location-based social networks (LBSNs) and produces a huge amount of trajectories composed of continuous and complex spatio-temporal points from people’s daily lives. How to accurately aggregate large-scale trajectories is an important and challenging task. Conventional clustering algorithms (e.g., k -means or k -mediods) cannot be directly employed to process trajectory data due to their serialization, triviality and redundancy. Aiming to overcome the drawbacks of traditional k -means algorithm and k -mediods, including their sensitivity to the selection of the initial k value, the cluster centers and easy convergence to a locally optimal solution, we first propose an optimized k -means algorithm (namely OKM ) to obtain k optimal initial clustering centers based on the density of trajectory points. Second, because k -means is sensitive to noisy points, we propose an improved k -mediods algorithm called IKMD based on an acceptable radius r by considering users’ geographic location in LBSNs. The value of k can be calculated based on r , and the optimal k points are selected as the initial clustering centers with high densities to reduce the cost of distance calculation. Thirdly, we thoroughly analyze the advantages of IKMD by comparing it with the commonly used clustering approaches through illustrative examples. Last, we conduct extensive experiments to evaluate the performance of IKMD against seven clustering approaches including the proposed optimized k -means algorithm, k -mediods algorithm, traditional density-based k -mediods algorithm and the state-of-the-arts trajectory clustering methods. The results demonstrate that IKMD significantly outperforms existing algorithms in the cost of distance calculation and the convergence speed. The methods proposed is proved to contribute to a larger effort targeted at advancing the study of intelligent trajectory data analytics.

Mobile Edge Computing (MEC), as an emerging and prospective mobile computing paradigm, allows a content provider to serve its users by allocating their mobile devices to nearby edge servers to lower the latency in the delivery of its content to those mobile services. From the content provider's perspective, a cost-effective mobile device allocation (MDA) aims to allocate maximum mobile devices to minimum edge servers. However, the allocation of excessive mobile devices to an edge server may result in severe communication interference and consequently, impact mobile devices data rates. Sometimes, not all users mobile devices can be allocated to edge servers. Unallocated mobile devices can still retrieve content from the remote cloud through base stations, however with high latency. The connection between these mobile devices and the base stations also incur communication interference. We formally model this Interference-aware Mobile Edge Device Allocation (I-MEDA) problem and propose a game-theoretic based approach named I-MEDAGame to formulate the I-MEDA problem as an I-MEDA game. Our theoretical analysis of I-MEDAGame shows that it admits a Nash equilibrium. I-MEDAGame employs a novel decentralized algorithm to find the Nash equilibrium of the IMEDA game. The performance of I-MEDAGame is theoretically analyzed and experimentally evaluated.

Mobile edge computing (MEC) allows edge servers to be placed at cellular base stations. App vendors like Uber and YouTube can rent computing resources and deploy latency-sensitive applications on edge servers for their users to access. Non-orthogonal multiple access (NOMA) is an emerging technique that facilitates the massive connectivity of 5G networks, further enhancing the capability of MEC. The edge user allocation (EUA) problem faces new challenges in 5G NOMA-based MEC systems. In this study, we investigate the EUA problem in a multi-cell multi-channel downlink power-domain NOMA-based MEC system. The main objective is to help mobile app vendors maximize their benefit by allocating maximum users to edge servers in a specific area at the lowest computing resource and transmit power costs. To this end, we introduce a decentralized game-theoretic approach to effectively select a channel and edge server for each user while fulfilling their resource and data rate requirements. We theoretically and experimentally evaluate our solution, which significantly outperforms various state-of-the-art and baseline approaches.

As the 5th generation (5G) network develops and rolls out rapidly, user requests can be offloaded to nearby edge servers for processing. This alleviates the pressure on the network backhaul and the remote cloud. Nevertheless, the edge user allocation (EUA) problem, as one of the main research challenges in the 5G era, has become a major obstacle to ensuring users’ Quality of Experience (QoE) in the edge computing environment. Conventional EUA approaches, ranging from static global allocation model to online decision-making model, have ignored the long-term impact of the changes in users’ expectations on user-perceived Quality of Service (QoS). Additionally, most existing approaches have not taken into account the distance between an edge user and an edge server, which impacts the user’s data rate profoundly. In this paper, we tackle these challenges by formulating EUA problem as a spatial-temporal one (ST-EUA), which models distance-aware QoS based on the wireless transmission attenuation and models users’ QoE based on the Expectation Confirmation Theory (ECT). To find an appropriate solution for ST-EUA problem, we develop two fuzzy control-based approaches, namely FC and BFC, for on demand scenarios and batch processing scenarios, respectively. They can balance effectively the user consolidation and server load. We conduct extensive experiments based on two widely-used real-world datasets. The results demonstrate the superiority of our FC and BFC in effectiveness and efficiency over the baselines and state-of-the-art.

With the widespread deployment of various cloud computing and service-oriented systems, there is a rapidly increasing demand for collaborative quality-of-service (QoS) prediction. Existing QoS prediction methods have made great progress in modeling users and services as well as exploiting contexts of service invocations. However, they ignore the completion of service requests/responses relies on the underlying network topology and the complex interactions between Autonomous Systems. To tackle this challenge, we propose a topology-aware neural (TAN) model for collaborative QoS prediction. In the TAN model, the features of users, services, and intermediate nodes on the communication path are projected to a shared latent space as input features. To jointly characterize the invocation process, the path features and end-cross features are captured respectively through an explicit path modeling layer and an implicit cross-modeling layer. After that, a gating layer fuses and transmits these features to the prediction layer for estimating unknown QoS values. In this way, TAN provides a flexible framework that can comprehensively capture the invocation context for making accurate QoS prediction. Experimental results on two real-world datasets demonstrate that TAN significantly outperforms state-of-the-art methods on the tasks of response time, throughput, and reliability prediction. Also, TAN shows better extensibility of using auxiliary information.

In the multi-access edge computing (MEC) environment, app vendors’ data can be cached on edge servers to ensure low-latency data retrieval. Massive users can simultaneously access edge servers with high data rates through flexible allocations of transmit power. The ability to manage networking resources offers unique opportunities to app vendors but also raises unprecedented challenges. To ensure fast data retrieval for users in the MEC environment, edge data caching must take into account the allocations of data, users, and transmit power jointly. We make the first attempt to study the Data, User, and Power Allocation (DUPA$^3$) problem, aiming to serve the most users and maximize their overall data rate. First, we formulate the DUPA$^3$problem and prove its$\mathcal {NP}$-completeness. Then, we model the DUPA$^3$problem as a potential DUPA$^3$game admitting at least one Nash equilibrium and propose a two-phase game-theoretic decentralized algorithm named DUPA$^3$Game to achieve the Nash equilibrium as the solution to the DUPA$^3$problem. To evaluate DUPA$^3$Game, we analyze its theoretical performance and conduct extensive experiments to demonstrate its effectiveness and efficiency.

Edge Computing (EC) enables a new kind of caching system in close geographic proximity to end-users by allowing app vendors to cache popular data on edge servers deployed at base stations. This edge cache system can better support latency-sensitive applications. However, transmitting data from the centralized cloud to the edge servers without proper transmission strategies may cost app vendors dearly. Cost-effective data distribution strategies are of particular importance for applications, whose data to be cached at the edge often changes dynamically. In this paper, we study thisOnline Edge Data Distribution(OEDD) problem, aiming to minimize app vendors’ total transmission cost, while ensuring low transmission latency in the long term. We first model this problem and prove its$\mathcal {NP}$-hardness. We then combine Lyapunov optimization and game theory to propose a novel Latency-Aware Online (LAO) approach for solving this OEDD problem over time in a distributed manner with provable performance guarantees. The evaluation of LAO based on a real-world dataset demonstrates that it can help app vendors formulate cost-effective edge data distribution strategies in an online manner.

Multi-access Edge Computing (MEC), as an extension of cloud computing, provides storage resources at the network edge to enable low-latency data retrieval for users. Due to limited physical sizes and constrained storage resources, individual edge servers cannot store a large amount of data when operating independently. They often need to offload data to other edge servers to serve users collaboratively. Operated by different edge infrastructure providers, edge servers usually work in a distrusted environment. Incentive and trust are the two main challenges in facilitating collaborative edge storage. This article proposes CSEdge, a novel decentralized system that tackles these challenges to enable collaborative edge storage based on blockchain. On CSEdge, edge servers can submit data offloading requests for others to contend for. Winners are selected based on their reputations. They will store the offloaded data and receive rewards for successfully finishing data offloading tasks. Via a distributed consensus, their performance will be recorded on blockchain for future reputation evaluation. A prototype of CSEdge is built on Hyperledger Sawtooth and experimentally evaluated against a baseline system and two start-of-the-art systems in a simulated MEC environment. The results demonstrate that CSEdge can effectively and efficiently facilitate collaborative edge storage among edge servers.

Mobile edge computing (MEC), as an emerging technology, allows application vendors to deploy application instances on edge servers to deliver low-latency services to nearby end-users. However, due to hardware faults, software exceptions, or cyberattacks, edge servers are prone to failures in the highly distributed and dynamic MEC environment. Hence service reliability must be ensured when failures occur. This raises a critical and open problem - improving service reliability when deploying application instances in the MEC environment. In this article, we jointly consider both user coverage and service reliability when deploying application instances on edge servers with a given application deployment budget$\mathcal {K}$. We formally define this jointCoverage-Reliability for$\mathcal {K}$-BudgetedEdgeApplicationDeployment (CR-BEAD) problem and model it as a constrained optimization problem. Next, we propose an optimal approach (namedBEAD-O) based on integer programming to find optimal solutions to small-scale CR-BEAD problems. We also propose a greedy approach namedBEAD-Gwith a constant approximation ratio of$1 - 1/e$to solve large-scale CR-BEAD problems efficiently. Extensive experimental evaluation against three representative approaches illustrates the effectiveness and efficiency of our approaches.

Edge computing, as an emerging and prospective distributed computing paradigm, allows a service provider to serve its users by allocating them to nearby edge servers delivering services with low latency. From the service provider’s perspective, a cost-effective service user allocation aims to allocate maximum service users to minimum edge servers. Such an allocation leverages multi-tenancy to reduce the resources hired by the service provider for serving the service users. However, the allocation of excessive service users to an edge server may result in severe interference and consequently impact their data rates. There is a trade-off between multi-tenancy and interference in the pursuit of a cost-effective service user allocation. In this article, we formally model this service user allocation (SUA) problem, and prove that it is NP-hard to find the optimal solution to an SUA problem. To solve the SUA problem effectively and efficiently, we propose a game-theoretic approach, namely MI-SUAGame, to formulate the SUA problem as a potential game. We analyze the game and prove its admission to a Nash equilibrium. Then, a novel decentralized algorithm is designed for finding a Nash equilibrium in the game as the solution to the SUA problem. The performance of MI-SUAGame is theoretically analyzed and experimentally evaluated against the state-of-the-art approach. The results show that it can solve the SUA problem effectively and efficiently.

The new edge computing paradigm extends cloud computing by allowing service vendors to deploy their service instances and data on distributed edge servers to serve their service users in close geographic proximity to those edge servers. Caching edge data on edge servers profoundly reduces the retrieval latency perceived by users. However, these edge data are subject to corruption due to intentional and/or accidental exceptions. This is a major challenge for service vendors but has been overlooked. Thus, verifying the integrity of edge data accurately and efficiently is a critical security problem in the edge computing environment. A unique characteristic of the edge computing environment is that edge servers suffer from constrained computing capacities. Thus, verifying data integrity on massive edge servers individually is computationally expensive and impractical. In this paper, we tackle this Edge Data Integrity (EDI) problem with an inspection and corruption localization scheme for EDI named ICL-EDI. This scheme allows service vendors to inspect data integrity and localize corrupted edge data cached on multiple edge servers accurately and efficiently. To evaluate its performance, we implement ICL-EDI and conduct extensive experiments to demonstrate its effectiveness and efficiency.

Mobile edge computing (MEC) is a new distributed computing paradigm where edge servers are deployed at, or near cellular base stations in close proximity to end-users. This offers computing resources at the edge of the network, facilitating a highly accessible platform for real-time, latency-sensitive services. A typical MEC environment is highly stochastic with random user arrivals and departures over time. Here, we address the user allocation problem from a service provider's perspective, who needs to allocate its users to the cloud or edge servers in a specific area. A user, who has a multi-dimensional resource requirement, can be allocated to either the remote cloud, which incurs a high latency, or an edge server, which results in a low latency but might require the user to wait in a queue. This article aims to achieve a controllable trade-off between performance (throughput) and several associated costs such as queuing delay and latency costs. We model this problem as a stochastic optimization problem, propose SUAC (Stochastic User AlloCation) – an online Lyapunov optimization-based algorithm, and prove its performance bounds. The experimental results demonstrate that SUAC outperforms existing approaches, effectively allocating users with a desired trade-off while keeping the system strongly stable.

In recent years, edge computing has emerged as a prospective distributed computing paradigm that overcomes several limitations of cloud computing. In the edge computing environment, a service provider can deploy its application instances on edge servers at the edge of the network to serve its own users with low latency. Given a limited budget$\mathcal {K}$for deploying applications on the edge servers in a particular geographical area, a number of approaches have been proposed very recently to determine the optimal deployment strategy that achieves various optimization objectives, e.g., to maximize the servers’ coverage, to minimize the average network latency, etc. However, the robustness of the services collectively delivered by the service provider’s applications deployed on the edge servers has not been considered at all. This is a critical issue, especially in the highly distributed, dynamic and volatile edge computing environment. In this article, we make the first attempt to tackle this challenge. Specifically, we formulate thisRobustness-oriented Edge Application Deployment(READ) problem as a constrained optimization problem and prove its$\mathcal {NP}$-hardness. Then, we provide an integer programming based approach named READ-$\mathcal {O}$for solving this problem precisely. We also provide an approximation algorithm, namely READ-$\mathcal {A}$, for finding near-optimal solutions to large-scale READ problems efficiently. We prove its approximation ratio is not worse than$\mathcal {K}/2$, which is a constant regardless of the total number of edge servers. We evaluate our approaches experimentally on a widely-used real-world dataset against five representative approaches. The experiment results demonstrate that our approaches can solve the READ problem effectively and efficiently.

With the rapid increase in the use of mobile devices in people’s daily lives, mobile data traffic is exploding in recent years. In the edge computing environment where edge servers are deployed in close proximity to mobile users, caching popular data on edge servers can ensure mobile users’ low-latency access to those data and reduce the data traffic between mobile users and the centralized cloud. Existing studies consider the data caching problem with a focus on the reduction of network delay and the improvement of mobile devices’ energy efficiency. In this article, we tackle this data caching problem in the edge computing environment from a service provider’s perspective with the aim to maximize its data caching revenue. This problem is challenging because there is a trade-off between the benefit produced and the cost incurred by caching data on edge servers. In the meantime, the constraint for data access latency must also be fulfilled. In this article, we formulate the data caching problem in the edge computing environment as an integer programming (IP) problem and prove its NP-completeness. To solve this problem effectively and efficiently in large-scale scenarios, we propose an approximation approach to find near-optimal solutions. Extensive experiments are conducted on a widely-used real-world dataset to evaluate our approaches.

Benefiting from the pay-as-you-go business model, cloud-based software applications are becoming more and more popular. A composite cloud system can be constructed by integrating existing component cloud services available over the internet as its system components. In order to fulfill the service-level agreements (SLAs), as well as users’ quality of experience (QoE), a stable execution of the constructed system is desirable in the long term. To achieve this goal, system components at high risk of failing must be identified and fault-tolerated. This is extremely challenging in the dynamic cloud environment that host the component cloud services. However, existing approaches are constrained by their lack of modeling and analysis of system components’ fluctuating reliability time series. To systematically address these issues, in this article, we propose PARS, a perturbation-aware approach, for measuring the reliability sensitivity of component cloud services. It first analyzes the negative perturbations in component cloud services’ historical reliability time series. Then, it calculates the reliability sensitivity of the component cloud services by analyzing how their reliability perturbations impact the reliability of the entire cloud system. Based on PARS, we propose a proactive adaptation approach for constructing and operating composite cloud systems with 1-out-of-2 N-version Programming fault-tolerance. This approach takes the reliability sensitivity of component cloud services estimated by PARS as input to assure the reliability of the cloud system. The results of experiments conducted on two widely used datasets demonstrate the effectiveness and efficiency of the proposed approaches in ensuring the reliability of composite cloud systems.

Neighborhood regularization is highly important for a latent factor (LF)-based Quality-of-Service (QoS)-predictor since similar users usually experience similar QoS when invoking similar services. Current neighborhood-regularized LF models rely prior information on neighborhood obtained from common raw QoS data or geographical information. The former suffers from low prediction accuracy due to the difficulty of constructing the neighborhood based on incomplete QoS data, while the latter requires additional geographical information that is usually difficult to collect considering information security, identity privacy, and commercial interests in real-world scenarios. To address the above issues, this work proposes a posterior-neighborhood-regularized LF (PLF) model for QoS prediction. The main idea is to decompose the LF analysis process into three phases: a) primal LF extraction, where the LFs are extracted to represent involved users/services based on known QoS data, b) posterior-neighborhood construction, where the neighborhood of each user/service is achieved based on similarities between their primal LF vectors, and c) posterior-neighborhood-regularized LF analysis, where the objective function is regularized by both the posterior-neighborhood of users/services and$L_{2}$L2-norm of desired LFs. Experimental results from large scale QoS datasets demonstrate that PLF outperforms state-of-the-art models in terms of both accuracy and efficiency.

In recent years, edge computing, as an extension of cloud computing, has emerged as a promising paradigm for powering a variety of applications demanding low latency, e.g., virtual or augmented reality, interactive gaming, real-time navigation, etc. In the edge computing environment, edge servers are deployed at base stations to offer highly-accessible computing capacities to nearby end-users, e.g., CPU, RAM, storage, etc. From a service provider’s perspective, caching app data on edge servers can ensure low latency in its users’ data retrieval. Given constrained cache spaces on edge servers due to their physical sizes, the optimal data caching strategy must minimize overall user latency. In this article, we formulate this Constrained Edge Data Caching (CEDC) problem as a constrained optimization problem from the service provider’s perspective and prove its$\mathcal {NP}$-hardness. We propose an optimal approach named CEDC-IP to solve this CEDC problem with the Integer Programming technique. We also provide an approximation algorithm named CEDC-A for finding approximate solutions to large-scale CEDC problems efficiently and prove its approximation ratio. CEDC-IP and CEDC-A are evaluated on a real-world data set. The results demonstrate that they significantly outperform four representative approaches.

Currently, the world is experiencing the rapid spread of Coronavirus Disease 2019 (COVID-19). Since the epidemic continues to take a devastating impact on the society, economy, and healthcare, the real-time detection of COVID-19 is essential for fast and cost-effective diagnosis services. Fortunately, deep learning (DL), as a promising technology, enables the COVID-19 diagnosis services on chest X-ray (CXR) images. The training task of DL model is generally implemented at the centralized cloud. However, due to the geo-distributed data sources and the transmission of large amounts of raw data to the centralized cloud, the transmission latency becomes a bottleneck of the COVID-19 diagnosis model training. In this paper, we propose aDistributedCOVID-19 detection model training method on CXR images with edge-cloud collaboration, named DisCOV. Specifically, to improve the training efficiency and guarantee the model accuracy, a distributed lightweight model-based training algorithm is designed with the cooperation of edge computing and cloud computing. In addition, a resource allocation algorithm is developed during the training to jointly minimize the time cost and energy consumption. Extensive experiments based on real-world CXR image datasets demonstrate that DisCOV is better performed and more promising than the existing baselines.

With an growing number of web services available on the Internet, an increasing burden is imposed on the use and management of service repository. Service clustering has been employed to facilitate a wide range of service-oriented tasks, such as service discovery, selection, composition and recommendation. Conventional approaches have been proposed to cluster web services by using explicit features, including syntactic features contained in service descriptions or semantic features extracted by probabilistic topic models. However, service implicit features are ignored and have yet to be properly explored and leveraged. To this end, we propose a novel heuristics-based framework DeepWSC for web service clustering. It integrates deep semantic features extracted from service descriptions by an improved recurrent convolutional neural network and service composability features obtained from service invocation relationships by a signed graph convolutional network, to jointly generate integrated implicit features for web service clustering. Extensive experiments are conducted on 8,459 real-world web services. The experiment results demonstrate that DeepWSC outperforms state-of-the-art approaches for web service clustering in terms of multiple evaluation metrics.

The rapid growth of mobile apps has significantly promoted the use of third-party libraries in mobile app development. However, mobile app developers are now facing the challenge of finding useful third-party libraries for improving their apps, e.g., to enhance user interfaces, to add social features, etc. An effective approach is to leverage collaborative filtering (CF) to predict useful third-party libraries for developers. We employed Matrix Factorization (MF) approaches - the classic CF-based prediction approaches - to make the predictions based on a total of 31,432 Android apps from Google Play. However, our investigation shows that there is a significant lack of diversity in the prediction results - a small fraction of popular third-party libraries dominate the prediction results while most other libraries are ill-served. The low diversity in the prediction results limits the usefulness of the prediction because it lacks novelty and serendipity which are much appreciated by mobile app developers. In order to increase the diversity in the prediction results, we designed an innovative MF-based approach, namely LibSeek, specifically for predicting useful third-party libraries for mobile apps. It employs an adaptive weighting mechanism to neutralize the bias caused by the popularity of third-party libraries. In addition, it introduces neighborhood information, i.e., information about similar apps and similar third-party libraries, to personalize the predictions for individual apps. The experimental results show that LibSeek can significantly diversify the prediction results, and in the meantime, increase the prediction accuracy.

Collaboration efficiency is of paramount importance for software development. Finding suitable developers is critical and challenging due to the difficulty of capturing developers’ expertises, relevance as well as the sparsity of explicit developer-task interactions. To tackle this problem, existing developer recommendation approaches focus on modelling the developer’s expertise or interactions with tasks based on their historical information. However, such approaches often suffer from low performance because they ignore some useful information that might help improve recommendation performance, such as 1) developers’ collaboration relationship; 2) the interaction relationship between developer and task; and 3) the tasks’ similarity relationship. To leverage the above-mentioned relationships, this paper proposes DevRec, a novel multi-relationship embedded approach for software developer recommendation. We first formally define the multi-relationships and then learn the vector representations (aka. embeddings) of developers and tasks. Second, we explicitly encoded the multi-relationships into the embedding process. Third, to refine the embeddings of developers and tasks, we recursively propagate the embeddings from their high-order connectivity based on graph convolution network. Moreover, to reveal the importance of different relationships, we generate their attentive weights based on attention mechanism. Finally, to evaluate the performance of DevRec, we conduct extensive experiments on two real-world datasets, and to investigate the usefulness of DevRec in practice, we conduct a user study at a large software company. The results show that DevRec outperforms other five state-of-the-art approaches significantly.

A non-negative latent factor analysis (NLFA)-based recommender can make precise recommendations by correctly representing the non-negative characteristic of industrial data. It commonly relies on a nonconvex and bilinear optimization process, where the effects of first-order solvers maybe significantly reduced. Higher order solvers like a Newton-type method are expected to make a breakthrough; however, its computation efficiency and scalability are greatly limited due to the numerous parameters involved in a Hessian matrix. To address this issue, this article proposes an approach for assimilating second-order information for building NLFA-based recommenders. The key idea is an inner second-order solver that employs a Hessian-free method for avoiding the highly expensive manipulations of a Hessian matrix. Empirical studies on eight data cases emerging from real industrial applications indicate that the proposed approach outperforms state-of-the-art models in prediction accuracy with affordable computational burden.

Mobile edge computing(MEC) deploys edge servers at the base station in the proximity of users to provide cloud computing-like computing and storage functionalities, which can achieve applications’ low latency requirement at the network edge. Theedge server network(ESN), constituted by edge servers in an area and the links between them, can host app vendors’ services for serving nearby users. Many existing studies have demonstrated that a high ESN density allows for high service performance because edge servers can communicate and share resources with each other effectively over the ESN. However, in the real-world MEC environment, constructing a high-density ESN may incur high construction costs. The trade-off between construction cost and network density plays a vital role in the design of an ESN. Unfortunately, existing studies of MEC have commonly and simply assumed the densities of the ESNs in their experiments. In this paper, we make the first attempt to study the design of cost-effective ESNs with the aim to trade off between the network construction cost and the network density. We model this novelEdge Server Network Design(ESND) problem as a constrained optimization problem and prove its$\mathcal {NP}$-hardness. ESND-O as an optimal approach is proposed based on integer programming to solve small-scale ESND problems. Another approximation approach named ESND-A is designed to solve large-scale ESND problems efficiently. We conduct extensive experiments to test the performance of ESND-O and ESND-A on a real-world dataset, and the experimental results demonstrate their effectiveness and efficiency against four representative approaches.

In recent years, deep neural networks (DNNs) have witnessed a booming of artificial intelligence Internet of Things applications with stringent demands across high accuracy and low latency. A widely adopted solution is to process such computation-intensive DNNs inference tasks with edge computing. Nevertheless, existing edge-based DNN processing methods still cannot achieve acceptable performance due to the intensive transmission data and unnecessary computation. To address the above limitations, we take the advantage of Multi-exit DNNs (ME-DNNs) that allows the tasks to exit early at different depths of the DNN during inference, based on the input complexity. However, naively deploying ME-DNNs in edge still fails to deliver fast and consistent inference in the wild environment. Specifically, 1) at the model-level, unsuitable exit settings will increase additional computational overhead and will lead to excessive queuing delay; 2) at the computation-level, it is hard to sustain high performance consistently in the dynamic edge computing environment. In this paper, we present a Low Latency Edge Intelligence Scheme based on Multi-Exit DNNs (LEIME) to tackle the aforementioned problem. At the model-level, we propose an exit setting algorithm to automatically build optimal ME-DNNs with lower time complexity; At the computation-level, we present a distributed offloading mechanism to fine-tune the task dispatching at runtime to sustain high performance in the dynamic environment, which has the property of close-to-optimal performance guarantee. Finally, we implement a prototype system and extensively evaluate it through testbed and large-scale simulation experiments. Experimental results demonstrate that LEIME significantly improves applications' performance, achieving 1.1–18.7 × speedup in different situations.

The number of maximal cliques could be exponentially large with respect to the number of vertices. A clique summary is a subset of all the maximal cliques and can somehow represent all the maximal cliques. Finding such a summary is deemed important in information distribution, influence estimation, cost-effective marketing, etc. The existing approach that finds a maximal clique summary suffers from long running time due to the excessive number of costly bound calculations that are used to estimate the size of to-be-found cliques during the enumeration process. Furthermore, we found that, sometimes, the bound calculation is not necessary at all. As a result, in order to provide the best study of the problem, we propose four strategies in two directions to speed up the process of finding a maximal clique summary by (1) restricting the bound calculation operation to a particular subset of all search branches and (2) making the best use of the bounds that have been previously calculated. Extensive experiments are conducted on eight real-world datasets to validate our strategies. Results demonstrate that the proposed method can reduce the number of bound calculations by 3 ~ 5 orders of magnitude, and each run of our algorithm can be up to 2.x times faster than the state-of-the-art algorithm while still keeping the summary concise. Our method can potentially benefit other large-output enumeration based problems, such as frequent itemset mining, when a summary of results is needed.

In the edge computing environment, edge servers are geographically distributed around end-users to provide highly accessible and efficient computing capacities and services. At runtime, edge servers are subject to failures for various reasons, e.g., hardware failures, system overloaded, cyber attacks. When an edge server fails, the end-users on the edge sever, i.e., edge users, must be migrated to nearby edge servers to ensure service delivery. An optimal migration strategy must maximize the number of migrated end-users while ensuring low latency for end-users. In the meantime, the service deployment cost, i.e., the cost of deploying services required by migrated end-users, must be considered. In this paper, we model the edge user migration (EUM) problem upon server failures as an Integer Programming problem, and introduce a novel, optimal approach for solving the EUM problem. We also propose a heuristic approach for finding sub-optimal solutions efficiently to large-scale scenarios. The results of experiments conducted on a real-world dataset demonstrate that our approaches significantly outperform two representative baseline approaches.

Edge computing provides task offloading services to extend the computational capacity of mobile users and reduce task latency. The deadline-awareness offloading algorithm plays a key role in guaranteeing the quality of service (QoS) requirement. Prior studies mainly focus on tasks with strict deadlines. However, some tasks may not always have to be finished before hard deadlines, e.g., multimedia tasks. Tasks with soft deadlines can miss their primary deadlines, but not by too much. This has not been properly considered by existing offloading approaches. In this paper, we propose CONFECT to offload tasks with mixed deadlines. We formulate the problem and prove its hardness. Then, we propose two online algorithms with proven competitive ratios to solve the problem collectively, including an algorithm that assigns tasks to edge servers and an algorithm that adjusts the task execution order on each server. Extensive experiments show that CONFECT outperforms five baseline algorithms.

With the development of the vehicular network (VN), emerging driver assistance applications are adhibited in daily life. Commonly, edge computing is adopted to satisfy the timeliness requirements of these applications, as the vehicular devices are usually insufficient in computation resources. Nevertheless, the increasing volume of service requests (SRs) are potential to overload the edge servers (ESs), thus increasing the task execution time. Besides, the randomness and the diversity of the SRs also challenge the dynamic resource allocation for the users. To deal with these challenges, a task offloading and resource allocation scheme based on game theory and reinforcement learning (RL) named TORA is proposed. Specifically, game theory is leveraged to determine the optimal task offloading strategy for improving the quality of service (QoS). Meanwhile, RL is applied to implement the dynamic resource allocation of the ES. Finally, the robust performance of the proposed method is validated by comparative experiments.

Edge data caching has attracted tremendous attention in recent years. Service providers can consider caching data on nearby locations to provide service for their app users with relatively low latency. The key to enhance the user experience is appropriately choose to cache data on the suitable edge servers to achieve the service providers' objective, e.g., minimizing data retrieval latency and minimizing data caching cost, etc. However, Quality of Experience (QoE), which impacts service providers' caching benefit significantly, has not been adequately considered in existing studies of edge data caching. This is not a trivial issue because QoE and Quality-of-Service (QoS) are not correlated linearly. It significantly complicates the formulation of cost-effective edge data caching strategies under the caching budget, limiting the number of cache spaces to hire on edge servers. We consider this problem of QoE-aware edge data caching in this paper, intending to optimize users' overall QoE under the caching budget. We first build the optimization model and prove the NP-completeness about this problem. We propose a heuristic approach and prove its approximation ratio theoretically to solve the problem of large-scale scenarios efficiently. We have done extensive experiments to demonstrate that the MPSG algorithm we propose outperforms state-of-the-art approaches by at least 68.77%.

The skyrocketing growth of mobile apps and mobile devices has significantly fueled the competition among app developers. They have leveraged the app store capabilities to analyse app data and identify app improvement opportunities. Existing research has shown that app developers mostly rely on in-domain (i.e., same domain or same app) data to improve their apps. However, relying on in-domain data results in low diversity and lacks novelty in recommended features. In this work, we present an approach that automatically identifies, classifies and ranks relevant popular features from cross-domain apps for recommendation to any given target app. It includes the following three steps: 1) identify cross-domain apps that are relevant to the target app in terms of their features; 2) filter and group semantically the features of the relevant cross-domain apps that are complementary to the target app; 3) rank and prioritize the complementary cross-domain features (in terms of their domain, app, feature and popularity characteristics) for adoption by the target app’s developers. We have run extensive experiments on 100 target apps from 10 categories over 15,200 cross-domain apps from 31 categories. The experimental results have shown that our approach to identifying, grouping and ranking complementary cross-domain features for recommendation has achieved an accuracy level of over 89%. Our semantic feature grouping technique has also significantly outperformed two existing baseline techniques. The empirical evaluation validates the efficacy of our approach in providing personalised feature recommendation and enhancing app’s user serendipity.

Benefiting from the pay-as-you-go business model, cloud computing has significantly promoted service computing techniques in real-world industrial applications. Software applications based on cloud computing are becoming more and more popular. By integrating existing component cloud services through the internet, composite cloud systems can be built to meet sophisticated application logic. Stable execution of such systems is desirable in the long term so that the service-level agreements (SLAs), as well as users’ quality of experience (QoE), can be fulfilled. To achieve this goal, it is critical to identify and fault-tolerate system components at high risks of failing. This is extremely challenging due to the dynamic and uncertainty of the cloud environment that hosts the component cloud services. Nevertheless, existing approaches pay little attention to the modeling and analysis of system components’ reliability time series. To address the above issues, we first present a reliability evaluation method for component cloud services based on the reliability model and their failure probability under continuous client-side invocation tests. Then, we propose a perturbation-aware reliability sensitivity measurement approach (named PARS) for measuring the reliability sensitivity of component cloud services. It first analyzes the negative perturbations in component cloud services’ historical reliability time series based on the Markov chain rule. Then, it calculates the reliability sensitivity of component cloud services by analyzing how their reliability perturbations impact the reliability of the entire cloud system. To guarantee the execution quality of the composite cloud system, we further propose a proactive adaptation approach named PA-PARS that enables 1-out-of-2 N-version Programming fault-tolerance for composite cloud systems based on PARS. PA-PARS takes the reliability sensitivity of component cloud services estimated by PARS as input to assure the reliability of the cloud system. It consists of four parts: 1) risky system component identification; 2) adaptation trigger; 3) candidate component cloud service selection; and 4) NVP-based system construction as the proactive adaptation for the composite cloud system. The results of experiments conducted on two widely-used datasets demonstrate the effectiveness and efficiency of the proposed approaches in ensuring the reliability of composite cloud systems.

Edge computing has emerged as a promising paradigm for powering a huge number of applications requiring low latency, e.g., real-time navigation, interactive gaming, virtual or augmented reality, etc. Edge computing offers highly accessible computing and storage resources, including CPU, storage and bandwidth, by deploying edge servers at base stations. With more users accessing edge apps, a huge number of data will be transmitted via edge servers between users’ devices and remote cloud servers. A service provider can cache its app data on edge servers to ensure the low data retrieval latency of its users. Due to the physical size limits of edge servers, the optimal data caching strategy must minimize overall user latency with consideration of constrained cache spaces on edge servers. From the service provider’s perspective, we formulate this Constrained Edge Data Caching (CEDC) problem and prove the NP-hardness of this CEDC problem. After that, we propose CEDC-IP, an optimal approach solved by the Integer Programming technique, to find optimal solutions to this CEDC problem. We also propose CEDC-A, an approximation algorithm with theoretical guarantee, to find approximate solutions to large-scale CEDC problems efficiently. Finally, we conduct extensive experiments on a widely-used real-world data set, and the experimental results demonstrate that both CEDC-IP and CEDC-A significantly outperform the other four representative approaches.

The mobile app marketplace has fierce competition for mobile app developers, who need to develop and update their apps as soon as possible to gain first mover advantage. Third-party libraries (TPLs) offer developers an easier way to enhance their apps with new features. However, how to find suitable candidates among the high number and fast-changing TPLs is a challenging problem. TPL recommendation is a promising solution, but unfortunately existing approaches suffer from low accuracy in recommendation results. To tackle this challenge, we propose GRec, a graph neural network (GNN) based approach, for recommending potentially useful TPLs for app development. GRec models mobile apps, TPLs, and their interactions into an app-library graph. It then distills app-library interaction information from the app-library graph to make more accurate TPL recommendations. To evaluate GRec’s performance, we conduct comprehensive experiments based on a large-scale real-world Android app dataset containing 31,432 Android apps, 752 distinct TPLs, and 537,011 app-library usage records. Our experimental results illustrate that GRec can significantly increase the prediction accuracy and diversify the prediction results compared with state-of-the-art methods. A user study performed with app developers also confirms GRec's usefulness for real-world mobile app development.

LLVM is a widely adopted compiler tool-chain, and its quality is critical to the projects relying on it. The bugs in LLVM may lead to an unpredictably large impact on the community. Usually, tools in LLVM need to interact with each other to finish a particular task. This working style brings up a specific type of bugs that involve multiple tools, namely, tool-chain bugs, which complicates the bug detection and debugging. Though there exist prior works about LLVM bugs, none of them has investigated this particular type of bugs in-depth. Thus, in this paper, we conduct an empirical study of the LLVM tool-chain bugs, aiming to provide the first comprehensive understanding of these bugs. Overall, we find 1723 tool-chain bugs. We reveal frequently occurred tool combinations, six typical interaction reasons, as well as four commonly seen failure symptoms. Through the linkages between these bugs and their fixing commits, we identify six common root causes, and summarize debugging manners. Based on the above findings, we highlight three insights into testing and debugging these bugs.

Edge computing (EC) has recently emerged as a novel computing paradigm that offers users low-latency services. Suffering from constrained computing resources due to their limited physical sizes, edge servers cannot always handle all the incoming computation tasks timely when they operate independently. They often need to cooperate through peer-offloading. Deployed and managed by different stakeholders, edge servers operate in a distrusted environment. Trust and incentive are the two main issues that challenge cooperative computing between them. Another unique challenge in the EC environment is to facilitate trust and incentive in a decentralized manner. To tackle these challenges systematically, this paper proposes CoopEdge, a novel blockchain-based decentralized platform, to drive and support cooperative edge computing. On CoopEdge, an edge server can publish a computation task for other edge servers to contend for. A winner is selected from candidate edge servers based on their reputations. After that, a consensus is reached among edge servers to record the performance in task execution on blockchain. We implement CoopEdge based on Hyperledger Sawtooth and evaluate it experimentally against a baseline and two state-of-the-art implementations in a simulated EC environment. The results validate the usefulness of CoopEdge and demonstrate its performance.

The new mobile edge computing (MEC) paradigm fundamentally changes the data caching technique by allowing data to be cached on edge servers attached to base stations within hundreds of meters from users. It provides a bounded latency guarantee for latency-sensitive applications, e.g., interactive AR/VR applications, online gaming, etc. However, in the highly distributed MEC environment, cache data is subject to corruption and their integrity must be ensured. Existing centralized data integrity assurance schemes are rendered obsolete by the unique characteristics of MEC, i.e., unlike cloud servers, edge servers have only limited computing and storage resources and they are deployed massively and distributed geographically. Thus, it is a new and significant challenge to ensure cache data integrity over tremendous geographically-distributed resource-constrained edge servers. This paper proposes the CooperEDI scheme to guarantee the edge data integrity in a distributed manner. CooperEDI employs a distributed consensus mechanism to form a self-management edge caching system. In the system, edge servers cooperatively ensure the integrity of cached replicas and repair corrupted ones. We experimentally evaluate its performance against three representative schemes. The results demonstrate that CooperEDI can effectively and efficiently ensure cache data integrity in the MEC environment.

Bike-sharing systems are becoming popular and generate a large volume of trajectory data. In a bike-sharing system, users can borrow and return bikes at different stations. In particular, a bike-sharing system will be affected by weather, the time period, and other dynamic factors, which challenges the scheduling of shared bikes. In this article, a new shared-bike demand forecasting model based on dynamic convolutional neural networks, called SDF , is proposed to predict the demand of shared bikes. SDF chooses the most relevant weather features from real weather data by using the Pearson correlation coefficient and transforms them into a two-dimensional dynamic feature matrix, taking into account the states of stations from historical data. The feature information in the matrix is extracted, learned, and trained with a newly proposed dynamic convolutional neural network to predict the demand of shared bikes in a dynamical and intelligent fashion. The phase of parameter update is optimized from three aspects: the loss function, optimization algorithm, and learning rate. Then, an accurate shared-bike demand forecasting model is designed based on the basic idea of minimizing the loss value. By comparing with classical machine learning models, the weight sharing strategy employed by SDF reduces the complexity of the network. It allows a high prediction accuracy to be achieved within a relatively short period of time. Extensive experiments are conducted on real-world bike-sharing datasets to evaluate SDF. The results show that SDF significantly outperforms classical machine learning models in prediction accuracy and efficiency.

As ubiquitous and personalized services are growing boomingly, an increasingly large amount of traffic is generated over the network by massive mobile devices. As a result, content caching is gradually extending to network edges to provide low-latency services, improve quality of service, and reduce redundant data traffic. Compared to the conventional content delivery networks, caches in edge networks with smaller sizes usually have to accommodate more bursty requests. In this article, we propose an evolving learning-based content caching policy, named PA-Cache in edge networks. It adaptively learns time-varying content popularity and determines which contents should be replaced when the cache is full. Unlike conventional deep neural networks (DNNs), which learn a fine-tuned but possibly outdated or biased prediction model using the entire training dataset with high computational complexity, PA-Cache weighs a large set of content features and trains the multi-layer recurrent neural network from shallow to deeper when more requests arrive over time. We extensively evaluate the performance of our proposed PA-Cache on real-world traces from a large online video-on-demand service provider. The results show that PA-Cache outperforms existing popular caching algorithms and approximates the optimal algorithm with only a 3.8% performance gap when the cache percentage is 1.0%. PA-Cache also significantly reduces the computational cost compared to conventional DNN-based approaches.

The Internet of Vehicles (IoV) connects vehicles, roadside units (RSUs) and other intelligent objects, enabling data sharing among them, thereby improving the efficiency of urban traffic and safety. Currently, collections of multimedia content, generated by multimedia surveillance equipment, vehicles, and so on, are transmitted to edge servers for implementation, because edge computing is a formidable paradigm for accommodating multimedia services with low-latency resource provisioning. However, the uneven or discrete distribution of the traffic flow covered by edge servers negatively affects the service performance (e.g., overload and underload) of edge servers in multimedia IoV systems. Therefore, how to accurately schedule and dynamically reserve proper numbers of resources for multimedia services in edge servers is still challenging. To address this challenge, a traffic flow prediction driven resource reservation method, called TripRes, is developed in this article. Specifically, the city map is divided into different regions, and the edge servers in a region are treated as a “big edge server” to simplify the complex distribution of edge servers. Then, future traffic flows are predicted using the deep spatiotemporal residual network (ST-ResNet), and future traffic flows are used to estimate the amount of multimedia services each region needs to offload to the edge servers. With the number of services to be offloaded in each region, their offloading destinations are determined through latency-sensitive transmission path selection. Finally, the performance of TripRes is evaluated using real-world big data with over 100M multimedia surveillance records from RSUs in Nanjing China.

Commit messages recorded in version control systems contain valuable information for software development, maintenance, and comprehension. Unfortunately, developers often commit code with empty or poor quality commit messages. To address this issue, several studies have proposed approaches to generate commit messages from commit diffs . Recent studies make use of neural machine translation algorithms to try and translate git diffs into commit messages and have achieved some promising results. However, these learning-based methods tend to generate high-frequency words but ignore low-frequency ones. In addition, they suffer from exposure bias issues, which leads to a gap between training phase and testing phase. In this article, we propose CoRec to address the above two limitations. Specifically, we first train a context-aware encoder-decoder model that randomly selects the previous output of the decoder or the embedding vector of a ground truth word as context to make the model gradually aware of previous alignment choices. Given a diff for testing, the trained model is reused to retrieve the most similar diff from the training set. Finally, we use the retrieval diff to guide the probability distribution for the final generated vocabulary. Our method combines the advantages of both information retrieval and neural machine translation. We evaluate CoRec on a dataset from Liu et al. and a large-scale dataset crawled from 10K popular Java repositories in Github. Our experimental results show that CoRec significantly outperforms the state-of-the-art method NNGen by 19% on average in terms of BLEU.

Internet of Vehicles (IoV) enables numerous in-vehicle applications for smart cities, driving increasing service demands for processing various contents (e.g., videos). Generally, for efficient service delivery, the contents from the service providers are processed on the edge servers (ESs), as edge computing offers vehicular applications low-latency services. However, due to the reusability of the same contents required by different distributed vehicular users, processing the copies of the same contents repeatedly in an edge server leads to a waste of resources (e.g., storage, computation, and bandwidth) in ESs. Therefore, it is a challenge to provide high-quality services while guaranteeing the resource efficiency with edge content caching. To address the challenge, an edge content caching method for smart cities with service requirement prediction, named E-Cache, is proposed. First, the future service requirements from the vehicles are predicted based on the deep spatiotemporal residual network (ST-ResNet). Then, preliminary content caching schemes are elaborated based on the predicted service requirements, which are further adjusted by a many-objective optimization aiming at minimizing the execution time and the energy consumption of the vehicular services. Eventually, experimental evaluations prove the efficiency and effectiveness of E-Cache with spatiotemporal traffic trajectory big data.

Edge computing allows app vendors to deploy their applications and relevant data on distributed edge servers to serve nearby users. Caching data on edge servers can minimize users' data retrieval latency. However, such cache data are subject to both intentional and accidental corruption in the highly distributed, dynamic, and volatile edge computing environment. Given a large number of edge servers and their limited computing resources, how to effectively and efficiently audit the integrity of app vendors' cache data is a critical and challenging problem. This article makes the first attempt to tackle this Edge Data Integrity (EDI) problem. We first analyze the threat model and the audit objectives, then propose a lightweight sampling-based probabilistic approach, namely EDI-V, to help app vendors audit the integrity of their data cached on a large scale of edge servers. We propose a new data structure named variable Merkle hash tree (VMHT) for generating the integrity proofs of those data replicas during the audit. VMHT can ensure the audit accuracy of EDI-V by maintaining sampling uniformity. EDI-V allows app vendors to inspect their cache data and locate the corrupted ones efficiently and effectively. Both theoretical analysis and comprehensively experimental evaluation demonstrate the efficiency and effectiveness of EDI-V.

In the edge computing (EC) environment, edge servers are deployed at base stations to offer highly accessible computing and storage resources to nearby app users. From the app vendor's perspective, caching data on edge servers can ensure low latency in app users' retrieval of app data. However, an edge server normally owns limited resources due to its limited size. In this article, we investigate the collaborative caching problem in the EC environment with the aim to minimize the system cost including data caching cost, data migration cost, and quality-of-service (QoS) penalty. We model this collaborative edge data caching problem (CEDC) as a constrained optimization problem and prove that it is NP-complete. We propose an online algorithm, called CEDC-O, to solve this CEDC problem during all time slots. CEDC-O is developed based on Lyapunov optimization, works online without requiring future information, and achieves provable close-to-optimal performance. CEDC-O is evaluated on a real-world data set, and the results demonstrate that it significantly outperforms four representative approaches.

Edge computing, as an extension of cloud computing, distributes computing and storage resources from centralized cloud to distributed edge servers, to power a variety of applications demanding low latency, e.g., IoT services, virtual reality, real-time navigation, etc. From an app vendor's perspective, app data needs to be transferred from the cloud to specific edge servers in an area to serve the app users in the area. However, according to the pay-as-you-go business model, distributing a large amount of data from the cloud to edge servers can be expensive. The optimal data distribution strategy must minimize the cost incurred, which includes two major components, the cost of data transmission between the cloud to edge servers and the cost of data transmission between edge servers. In the meantime, the delay constraint must be fulfilled - the data distribution must not take too long. In this article, we make the first attempt to formulate this Edge Data Distribution (EDD) problem as a constrained optimization problem from the app vendor's perspective and prove its NP-hardness. We propose an optimal approach named EDD-IP to solve this problem exactly with the Integer Programming technique. Then, we propose an O(k)-approximation algorithm named EDD-A for finding approximate solutions to largescale EDD problems efficiently. EDD-IP and EDD-A are evaluated on a real-world dataset and the results demonstrate that they significantly outperform three representative approaches.

As enterprises around the globe embrace globalization, strategic alliances among enterprises have become an important means to gain competitive advantages. Enterprises cooperate to improve the quality or lower the prices of their services, which introduce quality correlations, i.e., the quality of a service is associated with other services. Existing approaches for service composition have not fully and systematically considered the quality correlations between services. In this paper, we propose a novel approach named Q2C (Query of Quality Correlation) to systematically model quality correlations and enable efficient queries of quality correlations for service compositions. Given a service composition and a set of candidate services, Q2C first preprocesses the quality correlations among the candidate services and then constructs a quality correlation index graph to enable efficient queries for quality correlations. Extensive experiments are conducted on a real-world web service dataset to demonstrate the effectiveness and efficiency of Q2C.

The number of Web services on the Internet has been growing rapidly. This has made it increasingly difficult for users to find the right services from a large number of functionally equivalent candidate services. Inspecting every Web service for their quality value is impractical because it is very resource consuming. Therefore, the problem of quality prediction for Web services has attracted a lot of attention in the past several years, with a focus on the application of the Matrix Factorization (MF) technique. Recently, researchers have started to employ user similarity to improve MF-based prediction methods for Web services. However, none of the existing methods has properly and systematically addressed two of the major issues: 1) retrieving appropriate neighborhood information, i.e., similar users and services; 2) utilizing full neighborhood information, i.e., both users’ and services’ neighborhood information. In this paper, we propose CNMF, acovering-based quality prediction method for Web services vianeighborhood-awarematrixfactorization. The novelty of CNMF is twofold. First, it employs a covering-based clustering method to find similar users and services, which does not require the number of clusters and cluster centroids to be prespecified. Second, it utilizes neighborhood information on both users and services to improve the prediction accuracy. The results of experiments conducted on a real-world dataset containing 1,974,675 Web service invocation records demonstrate that CNMF significantly outperforms eight existing quality prediction methods, including two state-of-the-art methods that also utilize neighborhood information with MF.

With the widespread application of service-oriented architecture (SOA), a flood of similarly functioning services have been deployed online. How to recommend services to users to meet their individual needs becomes the key issue in service recommendation. In recent years, methods based on collaborative filtering (CF) have been widely proposed for service recommendation. However, traditional CF typically exploits only low-dimensional and linear interactions between users and services and is challenged by the problem of data sparsity in the real world. To address these issues, inspired by deep learning, this article proposes a new deep CF model for service recommendation, named location-aware deep CF (LDCF). This model offers the following innovations: 1) the location features are mapped into high-dimensional dense embedding vectors; 2) the multilayer-perceptron (MLP) captures the high-dimensional and nonlinear characteristics; and 3) the similarity adaptive corrector (AC) is first embedded in the output layer to correct the predictive quality of service. Equipped with these, LDCF can not only learn the high-dimensional and nonlinear interactions between users and services but also significantly alleviate the data sparsity problem. Through substantial experiments conducted on a real-world Web service dataset, results indicate that LDCF's recommendation performance obviously outperforms nine state-of-the-art service recommendation methods.

Mobile edge computing has emerged as a new distributed computing paradigm that overcomes the limitations of traditional cloud computing. In an edge computing environment, an app vendor can hire computing and storage resources on edge servers for deploying their applications to deliver lower-latency services to their app users. Under a budget constraint, an optimal edge application deployment strategy allows an app vendor to deploy application instances on edge servers in a specific area and provide services to the most app users in the area. In this paper, we make the first attempt to tackle this edge application deployment (EAD) problem. Specifically, we formulate the EAD problem as a constrained optimization problem and prove its NP-hardness. Then, we propose an optimal approach named EAD-opt to find the optimal solution of EAD based on integer programming, and an approximation approach named EAD-apx to find approximate solutions in large-scale EAD scenarios efficiently. We evaluate our approaches by conducting experiments on a widely used real-world data set and a synthetic data set with comparison against two baseline approaches. The experimental results demonstrate that our approaches can solve the EAD problem effectively and efficiently.

Most existing researches on cloud workflow systems have focused on resource scheduling with the aims to minimize system delay under budget constraints or optimize system cost under deadline constraints. However, cloud providers cannot guarantee a failure-free cloud environment, a compact scheduling plan is prone to failure, thus, workflow system reliability has been identified as a critical and challenging issue in the volatile cloud environment. With the ability of cloud, it is easy for users to implement the active fault tolerance schemes, e.g., Scale-Out. However, it will lead to issues like security problem and extra management cost. In this paper, we first investigate Scale-Up and Scale-Hybrid schemes to fully explore the possibilities offered by the ability of cloud. We formally model the problem of optimizing the reliability of a cloud workflow system under budget constraints with these three fault-tolerance schemes. These optimization problems are discrete and non-convex. Thus, we propose a genetic algorithm based method for workflow fault tolerance (GA4WFT). Finally, we evaluate the effectiveness and efficiency of proposed GA4WFT with three different fault-tolerance schemes through experiments conducted on Amazon EC2 data.

Mobile data traffic is exploding in recent years with the exponential growth of mobile users. In the edge computing environment where edge servers are deployed around mobile users, caching data on edge servers can ensure mobile users' fast access to those data and reduce the data traffic between mobile users and the centralized cloud. Existing studies consider the data cache and replacement problem with the consideration of the reduction of network delay and the improvement of mobile devices' energy efficiency. In this paper, we attack proactive data caching and replacement problem in the edge computing environment from the service providers' perspective, who would like to maximize their venues of caching their data. This problem is complicated because data caching produces benefits at a cost and there usually is a trade-off in-between. In this paper, we formulate the data caching and replacement problem as an integer programming problem, and maximizes the revenue of the service provider while satisfying a constraint for data access latency. We also propose an online algorithm to solve problems in large-scale scenarios. Extensive experiments are conducted on a real-world dataset that contains the locations of edge serPvers and mobile users.

Mobile Edge Computing (MEC) is an emerging and prospective computing paradigm that supports low-latency content delivery. In a MEC environment, edge servers are attached to base stations or access points in closer proximity to endusers to reduce the end-to-end latency in their access to online content. From an edge infrastructure provider's perspective, a cost-effective k edge server placement (kESP) places k edge servers within a particular geographic area to maximize their coverage. However, in the distributed MEC environment, edge servers are often subject to failures due to various reasons, e.g., software exceptions, hardware faults, cyberattacks, etc. End-users connected to a failed edge server have to access online content from the remote cloud if they are not covered by any other edge servers. This significantly jeopardizes endusers' quality of experience. Thus, the robustness of an edge server network must be considered in edge server placement. In this paper, we formally model this Robustness-oriented k Edge Server Placement (RkESP) problem, and prove that finding the optimal solution to this problem is .,Vl'-hard. Thus, we firstly propose an integer programming based optimal approach, namely Opt, to find optimal solutions to small-scale RkESP problems. Then, we propose an approximate approach, namely Approx, for solving large-scale RkESP problems efficiently with an O(k)-approximation ratio. Finally, the performance of the two approaches is experimentally evaluated against five state-of-the-art approaches on a real-world dataset and a large-scale synthesized dataset.

We present BiDaML (Big Data Analytics Modeling Languages), an integrated suite of visual languages and supporting tool to help end-users with the engineering of big data analytics solutions. BiDaML, our visual notations suite, comprises six diagrammatic notations: brainstorming diagram, process diagram, technique diagrams, data diagrams, output diagrams and deployment diagram. BiDaML tool provides a platform for efficiently producing BiDaML visual models and facilitating their design, creation, code generation and integration with other tools. To demonstrate the utility of BiDaML, we illustrate our approach with a realworld example of traffic data analysis. We evaluate BiDaML using two types of evaluations, the physics of notations and a cognitive walkthrough with several target end-users e.g. data scientists and software engineers.

As many applications and services are moving towards a more human-centered design, app vendors are taking the quality of experience (QoE) increasingly seriously. End-to-end latency is a key factor that determines the QoE experienced by users, especially for latency-sensitive applications such as online gaming, health care, critical warning systems and so on. Recently, edge computing has emerged as a promising solution to the high latency problem. In an edge computing environment, edge servers are deployed at cellular base stations, offering processing power and low network latency to users within their geographic proximity. In this paper, we tackle the user allocation problem in edge computing from an app vendor's perspective, where the vendor needs to decide which edge servers to serve which users in a specific area. Also, the vendor must consider the various levels of quality of service (QoS) for its users. Each QoS level results in a different QoE level; thus, the app vendor needs to decide the QoS level for each user so that the overall user experience is maximized. To tackle the NP-hardness of this problem, we formulate it as a potential game then propose QoEGame, an effective and efficient game-theoretic approach that admits a Nash equilibrium as a solution to the user allocation problem. Being a distributed algorithm, QoEGame is able to fully utilize the distributed nature of edge computing. Finally, we theoretically and empirically evaluate the performance of QoEGame, which is illustrated to be significantly better than the state of the art and other baseline approaches.

In mobile edge computing (MEC), edge servers are deployed at base stations to provide highly accessible computational resources and storage capacities to nearby mobile devices. Caching data on edge servers can ensure the service quality and network latency for those mobile devices. However, an app vendor needs to ensure that the data caching cost does not exceed its data caching budget. In this paper, we present the budgeted edge data caching (BEDC) problem as a constrained optimization problem to maximize the overall reduction in data retrieval for all its app users within the budget, and prove that it is NP-hard. Then, we provide an approach named IP-BEDC for solving the BEDC problem optimally based on Integer Programming. We also provide an O(k) -approximation algorithm, namely α-BEDC, to find near-optimal solutions to the BEDC problems efficiently. Our proposed approaches are evaluated on a real-world data set and a synthesized data set. The results demonstrate that our approaches can solve the BEDC problem effectively and efficiently while significantly outperforming five representative approaches.

Nowadays, the world is witnessing a rapid development of edge computing. As an important issue in the edge computing paradigm, the edge user allocation (EUA) problem has attracted considerable attention. EUA aims at allocating the end-users in a specific area to the edge servers in that area, and ensure end-users' low-latency access to app vendor's services deployed on those edge servers. However, existing approaches simply assume that each edge server has a specific coverage and neglect the complexity of wireless signal transmission. To ensure end-users' low latency, an EUA approach must take into account the distance between end-users and their nearby edge servers, as it significantly impacts their Quality of Experience (QoE). Accordingly, EUA must maximize the overall QoE of the app vendor's users. To tackle this new distance-aware EUA problem, we propose two novel approaches, namely DEUA-O and DEUA-H. DEUA-O aims to find the optimal solution while DEUA-H aims to find the sub-optimal solution in large-scale scenarios efficiently. Four series of experiments are conducted on a real-world dataset to evaluate DEUA-O and DEUA-H. The results demonstrate the substantial gains of our approaches over the state-of-the-art.

Competition in mobile applications (i.e., apps) is becoming more and more intense with the increase in popularity of smart phones and mobile devices. Previous research shows that app developers spent considerable amount of time in exploiting user feedback to improve their apps. However, relying on own user feedback is insufficient for app survival in such competitive environment. It is highly important for an app developer to learn from competitors in order to keep the rank higher or become topper in the store (e.g., Google Play1). In this work, we present an approach to automatically classify and rank popular and un-popular features from the competitor apps. We follow 3 steps- (1) extract features from competitor app updates (i.e., whatsNew) and user feedback (i.e, reviews), (2) filter and group the review features that are relevant to whatsNew features, then, classify whatsNew features to binary classes, such as, popular and unpopular, (3) rank and prioritize those popular and unpopular features from competitors in order to recommend developers to adopt or avoid those features. The ranking of whatsNew features are done based on whatsNew-to-review relevance, user sentiments, and popularity of those features. We conduct extensive experiments on 840 updates and 262000 reviews of 84 different competitor apps of 10 categories. We found encouraging results from the experiments and the empirical evaluation validates the efficacy of our approach, hence, potential usefulness to the developers.

Big data and analytics are increasingly used in different domains to gain insights and to improve decision-making. Developing big data analytics solutions is a complex task involving multidisciplinary teams and users - with no data science and programming background - to professional data scientists and software engineers. Different stakeholders work with a variety of data types, tasks and concepts in different languages from high- level domain concepts to low level programming languages and technical concepts. In order to advance the level of abstraction beyond low-level data analysis technical details, we demonstrate our BiDaML tool. BiDaML brings all stakeholders around one tool to specify, model and document their big data applications using a novel set of domain-specific visual languages (DSVLs).

Cloud Computing (CC) is an emerging technology that can potentially revolutionise the application and delivery of IT. There has been little research, however, into the adoption of CC in Small and Medium-Sized Enterprises (SMEs). The indicators show that CC has been adopted very slowly. There is also a significant research gap in the investigation of the adoption of this innovation in SMEs. This article explores how the adoption of CC in Australia is related to technological factors, risk factors, and environmental factors. The study provides useful insights that can be utilised practically by SMEs, policymakers, and cloud vendors.

Quality of service (QoS) has been mostly applied to represent non-functional properties of Web services and differentiate those with the same functionality. How to accurately predict service QoS has become a key research topic. Researchers have employed neighborhood information into matrix factorization (MF) for service QoS prediction in recent years. However, they are restricted to traditional matrix factorization that may incur a couple of limitations. 1) Conventional MF for QoS prediction linearly combines the multiplication of the latent feature representation of users and services through inner product, failing to fully capture the implicit features of user and service. 2) Most of approaches integrate user or service neighborhood as heuristics into MF model, where either location context or historical invocation records are used to calculate similar users or services. Nevertheless, combining both of them together in a collaborative way is ignored for neighborhood selection that has yet to be properly explored. To deal with the challenges, we propose a novel approach for service QoS prediction called Neighborhood-integrated Deep Matrix Factorization (NDMF), which integrates user neighborhood selected by a collaborative way into an enhanced matrix factorization model via deep neural network (DNN). We implement a prototype system and conduct extensive experiments on public and real-world large Web service dataset with almost 2,000,000 service invocations called WS-DREAM which is widely used in service QoS prediction. The experimental results demonstrate that our proposed approach significantly outperforms state-of-the-art ones in terms of multiple evaluation metrics.

Edge Computing provides mobile and Internet-of-Things (IoT) app vendors with a new distributed computing paradigm which allows an app vendor to deploy its app at hired edge servers distributed near app users at the edge of the cloud. This way, app users can be allocated to hired edge servers nearby to minimize network latency and energy consumption. A cost-effective edge user allocation (EUA) requires maximum app users to be served with minimum overall system cost. Finding a centralized optimal solution to this EUA problem is NP-hard. Thus, we propose EUAGame, a game-theoretic approach that formulates the EUA problem as a potential game. We analyze the game and show that it admits a Nash equilibrium. Then, we design a novel decentralized algorithm for finding a Nash equilibrium in the game as a solution to the EUA problem. The performance of this algorithm is theoretically analyzed and experimentally evaluated. The results show that the EUA problem can be solved effectively and efficiently.

The popularity of cloud computing has fueled the growth in multi-tenant service-based systems (SBSs) that are composed of selected cloud services. In the cloud environment, a multi-tenant SBS simultaneously serves multiple tenants that usually have differentiated QoS requirements. This unique characteristic further complicates the problems of QoS-aware service selection at build-time and system adaptation at runtime, and renders conventional approaches obsolete and inefficient. In the dynamic and volatile cloud environment, the efficiency of building and adapting a multi-tenant SBS is of paramount importance. In this paper, we present two service recommendation approaches for multi-tenant SBSs, one for build-time and one for runtime, based on K-Means clustering and Locality-Sensitive Hashing (LSH) techniques respectively, aiming at finding appropriate services efficiently. Extensive experimental results demonstrate that our approaches can facilitate fast multi-tenant SBS construction and rapid system adaptation.

Multiple multi-dimensional data streams are ubiquitous in the modern world, such as IoT applications, GIS applications and social networks. Detecting anomalies in such data streams in real-time is an important and challenging task. It is able to provide valuable information from data and then assists decision-making. However, exiting approaches for anomaly detection in multi-dimensional data streams have not properly considered the correlations among multiple multi-dimensional streams. Moreover, for multi-dimensional streaming data, online detection speed is often an important concern. In this paper, we propose a fast yet effective anomaly detection approach in multiple multi-dimensional data streams. This is based on a combination of ideas, i.e., stream pre-processing, locality sensitive hashing and dynamic isolation forest. Experiments on real datasets demonstrate that our approach achieves a magnitude increase in its efficiency compared with state-of-the-art approaches while maintaining competitive detection accuracy.

Maximal clique enumeration (MCE) is a fundamental problem in graph theory and is used in many applications, such as social network analysis, bioinformatics, intelligent agent systems, cyber security. Most existing MCE algorithms focus on improving the efficiency rather than reducing the size of the output, which could consist of a large number of maximal cliques. In this paper, we study how to report a summary of less overlapping maximal cliques. The problem was studied before, however, after examining the pioneer approach, we consider it still not satisfactory. To advance the research along this line, this paper attempts to make two contributions: (a) We propose a more effective sampling strategy, which produces a much smaller summary but still ensures that the summary can somehow witness all the maximal cliques and the expectation of each maximal clique witnessed by the summary is above a predefined threshold. (b) To verify experimentally, we tested ten real benchmark datasets that have a variety of graph characteristics. The results show that our new sampling strategy consistently outperforms the state-of-the-art method by producing smaller summaries and running faster on all the datasets.

Mobile Edge Computing (MEC) has already developed into a key component of the future mobile broadband network due to its low latency. In MEC, mobile devices can access data-intensive applications deployed at edge, which are facilitated by service and computing resources available on edge servers. However, it is difficult to handle such issues while data transmission, user mobility and load balancing conditions change constantly among mobile devices, edge servers and the cloud. In this paper, we propose an approach for formulating Data-intensive Application Edge Deployment Policy (DAEDP) that maximizes the latency reduction for mobile devices while minimizing the monetary cost for Application Service Providers (ASPs). The deployment problem is modelled as a Markov decision process, and a deep reinforcement learning strategy is proposed to formulate the optimal policy with maximization of the long-term discount reward. Extensive experiments are conducted to evaluate DAEDP. The results show that DAEDP outperforms four baseline approaches.

A mobile-edge computing (MEC) system integrating energy harvesting (EH) techniques is a promising paradigm for supporting computation-intensive and delay-sensitive mobile applications. While computation offloading reduces users' perceived latency, EH techniques mitigate the limitation of mobile devices' battery capacity. However, when considering a scenario with multiple EH devices, the advantages of MEC systems with EH devices may be compromised due to the competition among multiple devices for available computational resources and wireless bandwidth. In this paper, the joint computation offloading and dynamic resource allocation (JCODRA) that minimizes the long-term average execution cost is formulated as a stochastic optimization problem. In particular, both the long-term average execution delay and the penalty delay are included in the optimization objective. The former intends to handle the competition and random and uncontrollable EH processes properly and the latter aims to reduce the ratio of dropped tasks. An online algorithm based on Lyapunov optimization is proposed to transform the original problem into a per-time slot deterministic problem. The results of experiments demonstrate that our algorithm significantly outperforms three representative baseline approaches.

An edge computing environment features multiple edge servers and multiple service clients. In this environment, mobile service providers can offload client-side computation tasks from service clients' devices onto edge servers to reduce service latency and power consumption experienced by the clients. A critical issue that has yet to be properly addressed is how to allocate edge computing resources to achieve two optimization objectives: 1) minimize the service cost measured by the service latency and the power consumption experienced by service clients; and 2) maximize the service capacity measured by the number of service clients that can offload their computation tasks in the long term. This paper formulates this long-term problem as a stochastic optimization problem and solves it with an online algorithm based on Lyapunov optimization. This NPhard problem is decomposed into three sub-problems, which are then solved with a suite of techniques. The experimental results show that our approach significantly outperforms two baseline approaches.

With the rapid increase in the use of mobile devices in people's daily lives, mobile data traffic is exploding in recent years. In the edge computing environment where edge servers are deployed around mobile users, caching popular data on edge servers can ensure mobile users' fast access to those data and reduce the data traffic between mobile users and the centralized cloud. Existing studies consider the data cache problem with a focus on the reduction of network delay and the improvement of mobile devices' energy efficiency. In this paper, we attack the data caching problem in the edge computing environment from the service providers' perspective, who would like to maximize their venues of caching their data. This problem is complicated because data caching produces benefits at a cost and there usually is a trade-off in-between. In this paper, we formulate the data caching problem as an integer programming problem, and maximizes the revenue of the service provider while satisfying a constraint for data access latency. Extensive experiments are conducted on a real-world dataset that contains the locations of edge servers and mobile users, and the results reveal that our approach significantly outperform the baseline approaches.

In the past decade, a large number and variety of services have been published on the Internet, e.g., public services, web services, cloud services, etc., offering customers a wide range of choices for fulfilling their demands. This has made it a challenge for customers to select from many mutually-substitutable competing services, especially when they are offered with differentiated quality. Thus, service recommendation that helps customers find the right services has become of paramount research and practical importance. The correlations in customers' preferences for different quality dimensions make service recommendation an even more complicated problem. It has not been properly addressed by existing approaches. This paper proposes DQCSR (diversified quality centric service recommendation), an approach that finds diversified services that are representative in different quality dimensions with respect to customer's quality preferences. The results of experiments conducted on a real-world dataset demonstrate the representativeness and diversity in the recommendation results produced by DQCSR.

Mobile Edge Computing has already become a new paradigm to reduce the latency in data transmission for resource-limited mobile devices by offloading computation tasks onto edge servers. However, for mobility-aware computation-intensive services, existing offloading strategies cannot handle the offloading procedure properly because of the lack of collaboration among edge servers. A data stream application is partitionable if it can be presented by a directed acyclic dataflow graph, which makes cross-edge collaboration possible. In this paper, we propose a cross-edge computation offloading (CCO) framework for partitionable applications. The transmission, execution and coordination cost, as well as the penalty for task failure, are considered. An online algorithm based on Lyapunov optimization is proposed to jointly determine edge site-selection and energy harvesting without priori knowledge. By stabilizing the battery energy level of each mobile device around a positive constant, the proposed algorithm can obtain asymptotic optimality. The-oretical analysis about the complexity and the effectiveness of the proposed framework is provided. Experimental results based on a real-life dataset corroborate that CCO can achieve superior performance compared with benchmarks where crossedge collaboration is not allowed.

Correlative approaches have attempted to cluster web services based on either the explicit information contained in service descriptions or functionality semantic features extracted by probabilistic topic models. However, the implicit contextual information of service descriptions is ignored and has yet to be properly explored and leveraged. To this end, we propose a novel framework with deep neural network, called DeepWSC, which combines the advantages of recurrent neural network and convolutional neural network to cluster web services through automatic feature extraction. The experimental results demonstrate that DeepWSC outperforms state-of-the-art approaches for web service clustering in terms of multiple evaluation metrics.

SUMMARY: Tandem mass spectrometry based database searching is a widely acknowledged and adopted method that identifies peptide sequence in shotgun proteomics. However, database searching is extremely computationally expensive, which can take days even weeks to process a large spectra dataset. To address this critical issue, this paper presents SW-Tandem, a new tool for large-scale peptide sequencing. SW-Tandem parallelizes the spectrum dot product scoring algorithm and leverages the advantages of Sunway TaihuLight, the No. 1 supercomputer in the world in 2017. Sunway TaihuLight is powered by the brand new many-core SW26010 processors and provides a peak computation performance greater than 100PFlops. To fully utilize the Sunway TaihuLights capacity, SW-Tandem employs three mechanisms to accelerate large-scale peptide identification, memory-access optimizations, double buffering and vectorization. The results of experiments conducted on multiple datasets demonstrate the performance of SW-Tandem against three state-of-the-art tools for peptide identification, including X!! Tandem, MR-Tandem and MSFragger. In addition, it shows high scalability in the experiments on extremely large datasets sized up to 12 GB. AVAILABILITY AND IMPLEMENTATION: SW-Tandem is an open source software tool implemented in C++. The source code and the parameter settings are available at https://github.com/Logic09/SW-Tandem. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The increasing number of web APIs registered in service sharing communities (e.g., http://ProgrammableWeb.com that provides a platform benefiting the social interactions between different software developers) has provided a promising way to quickly build various apps with diverse functions. Generally, an app developer can manually discover, select, and compose a set of appropriate web APIs to build a new app satisfying the developer's functional and nonfunctional business requirements, economically and conveniently. However, the above manual web APIs selection process is usually time-consuming and cumbersome as most app developers often do not have much background knowledge of candidate web APIs. Moreover, the manually selected web APIs cannot always guarantee to be integrated successfully as the compatibilities between different web APIs are often varied and not validated. In view of these challenges, we define a weighted APIs correlation graph (W-ACG) in this paper to model the APIs functions and compatibilities. Furthermore, we propose a novel web APIs recommendation approach named Keywords-based and Compatibility-aware APIs Recommendation (K-CAR) based on the defined W-ACG. Through analyzing the input keywords describing the functions expected by an app developer, K-CAR can return the app developer a set of optimal APIs that are not only functional-qualified but also compatibility-guaranteed. Extensive experiments are deployed on 18 478 real-world web APIs and 6146 real-world apps to evaluate the usefulness and efficiency of K-CAR.

Service composition is an important research problem in service computing systems, which combines simple and individual services into composite services to fulfill users' complex requirements. Service composition usually consists of four stages, i.e., service discovery, candidate selection, service negotiation and task execution. In self-organising systems, there is the fifth stage of service composition: self-evolution. Most of existing works study only some of the five stages. However, these five stages should be systematically studied so as to develop an integrated and efficient service composition approach. Against this background, this paper proposes an agent-based integrated self-evolving service composition approach. This approach systematically takes the five stages of service composition into consideration. It is also decentralised and self-evolvable. Experimental results demonstrate that the proposed approach can achieve almost the same success rates while uses much less communication overhead and time consumption in comparison with three existing representative approaches.

The classical disjoint shortest path problem has recently recalled interests from researchers in the network planning and optimization community. However, the requirement of the shortest paths being completely vertex or edge disjoint might be too restrictive and demands much more resources in a network. Partially disjoint shortest paths, in which a bounded number of shared vertices or edges is allowed, balance between degree of disjointness and occupied network resources. In this paper, we consider the problem of finding k shortest paths which are edge disjoint but partially vertex disjoint. For a pair of distinct vertices in a network graph, the problem aims to optimally find k edge disjoint shortest paths among which at most a bounded number of vertices are shared by at least two paths. In particular, we present novel techniques for exactly solving the problem with a runtime that significantly improves the current best result. The proposed algorithm is also validated by computer experiments on both synthetic and real networks which demonstrate its superior efficiency of up to three orders of magnitude faster than the state of the art.

Advances in cloud computing and

The applications of mobile devices are increasingly becoming computationally intensive while the computing capability of the user’s mobile device is limited. Traditional approaches offload the tasks of mobile applications to the remote cloud. However, the rapid growth of mobile devices has made it a challenge for the remote cloud to provide computing and storage capacities with low communication delays due to the fact that the remote cloud is geographically far away from mobile devices. Reducing the completion time of applications in mobile devices through the technical expending mobile cloudlets which are moving collocated with Access Points (APs) is necessary. To address the above issues, this paper proposes EACP-CA (Enhanced Adaptive Cloudlets Placement approach based on Covering Algorithm), an enhanced adaptive cloudlet placement approach for mobile applications in a given network area. We apply the CA (Covering Algorithm) to adaptively cluster the mobile devices based on their geographical locations, the aggregation regions of the mobile devices are identified, and the cloudlet destination locations are also confirmed according to the clustering centers. In addition, we can also obtain the traces between the original and destination locations of these mobile cloudlets. To increase the efficiency, we parallelize CA on Spark. Extensive experiments show that the proposed approach outperforms the existing approach in both effectiveness and efficiency.

The proliferation of cloud computing has fueled the rapid growth of multi-tenant service-based systems (SBSs), which serve multiple tenants simultaneously by composing existing services in the form of business processes. In a distributed and volatile operating environment, runtime anomalies may occur to the component services of an SBS and cause end-to-end quality violations. Engineering multi-tenant SBSs that can quickly handle runtime anomalies cost effectively has become a significant challenge. Different approaches have been proposed to formulate fault tolerance strategies for engineering SBSs. However, none of the existing approaches has sufficiently considered the service criticality based on multi-tenancy where multiple tenants share the same SBS instance with different multi-dimensional quality preferences. In this paper, we propose Criticality-based Fault Tolerance for Multi-Tenant SBSs (CFT4MTS), a novel approach that formulates cost-effective fault tolerance strategies for multi-tenant SBSs by providing redundancy for the critical component services. First, the criticality of each component service is evaluated based on its multi-dimensional quality and multiple tenants sharing the component service with differentiated quality preferences. Then, the fault tolerance problem is modelled as an Integer Programming problem to identify the optimal fault tolerance strategy. The experimental results show that, compared with three existing representative approaches, CFT4MTS can alleviate degradation in the quality of multi-tenant SBSs in a much more effective and efficient way.

Mining big data often requires tremendous computational resources. This has become a major obstacle to broad applications of big data analytics. Cloud computing allows data scientists to access computational resources on-demand for building their big data analytics solutions in the cloud. However, the monetary cost of mining big data in the cloud can still be unexpectedly high. For example, running 100 m4-xlarge Amazon EC2 instances for a month costs approximately $17,495.00. On this ground, it is a critical issue to analyze the cost effectiveness of big data mining in the cloud, i.e., how to achieve a sufficiently satisfactory result at the lowest possible computation cost. In certain big data mining scenarios, 100% accuracy is unnecessary. Instead, it is often more preferable to achieve a sufficient accuracy, e.g., 99%, at a much lower cost, e.g., 10%, than the cost of achieving the 100% accuracy. In this paper, we explore and demonstrate the cost effectiveness of big data mining with a case study using well known k-means. With the case study, we find that achieving 99% accuracy only needs 0.32%-46.17% computation cost of 100% accuracy. This finding lays the cornerstone for cost-effective big data mining in a variety of domains.

Anomaly or outlier detection is a major challenge in big data analytics because anomaly patterns provide valuable insights for decision-making in a wide range of applications. Recently proposed anomaly detection methods based on the tree isolation mechanism are very fast due to their logarithmic time complexity, making them capable of handling big data sets efficiently. However, the underlying similarity or distance measures in these methods have not been well understood. Contrary to the claims that these methods never rely on any distance measure, we find that they have close relationships with certain distance measures. This implies that the current use of this fast isolation mechanism is only limited to these distance measures and fails to generalise to other commonlyused measures. In this paper, we propose a generic framework named LSHiForest for fast tree isolation based ensemble anomaly analysis with the use of a Locality-Sensitive Hashing (LSH) forest. Being generic, the proposed framework can be instantiated with a diverse range of LSH families, and the fast isolation mechanism can be extended to any distance measures, data types and data spaces where an LSH family is defined. In particular, the instances of our framework with kernelised LSH families or learning based hashing schemes can detect complicated anomalies like local or surrounded anomalies. We also formally show that the existing tree isolation based detection methods are special cases of our framework with the corresponding distance measures. Extensive experiments on both synthetic and real-world benchmark data sets show that the framework can achieve both high time efficiency and anomaly detection quality.

Multi-tenant service-based systems (SBSs) have gained unprecedented prominence in recent years. Network-accessible Web services are composed in the form of business process to simultaneously fulfill multiple tenants' functional and multi-dimensional quality-of-service (QoS) requirements. Those services often operate in a distributed and volatile environment. It is of tremendous importance to monitor the services to detect or predict the anomalies timely to avoid QoS violations and Service Level Agreement (SLA) breaches. However, monitoring Web services consumes resources and incurs system overhead. It is impractical to constantly monitor all the services in an SBS. Thus, it is a significant challenge to monitor the services of a multi-tenant SBS in a cost-effective manner. In this paper, we propose CM4MTS (Criticality-based Monitoring for Multi-Tenant SBSs) for formulating cost-effective monitoring strategy. The criticality of a service is evaluated based on its impacts on the quality of the SBS upon runtime anomalies and the tenants sharing the service. The services with higher criticalities in an SBS are given higher priorities in monitoring resource allocation. Extensive experiments show that CM4MTS outperforms representative approaches in ensuring the quality of multi-tenant SBSs.

Service monitoring is an important research problem in service-based systems (SBSs), which aims to monitor the failure of services in a timely manner while using resources as few as possible. Most of the existing service monitoring approaches are centralised which suffer the potential of single point of failure and are not suitable in distributed large scale SBSs. Moreover, these centralised monitoring approaches are designed only in single-tenant SBSs. Nowadays, the scale of SBSs is extremely large, i.e., including a large number of services and clients. Thus, it is essential for service monitoring approaches to work well in distributed large scale SBSs and support multi-tenancy. Towards this end, in this paper, an agent-based decentralised service monitoring approach is developed in multi-tenant SBSs. Compared to the centralised approaches, the proposed decentralised approach can avoid the single point of failure and can balance the computation over the monitoring agents. Also, unlike existing approaches, the proposed approach is developed in multi-tenant SBSs. Experimental results demonstrate that the proposed approach can respond as quickly as centralised approaches but has much less computation overhead than centralised approaches.

Summary: Tandem mass spectrometry-based de novo peptide sequencing is a complex and time-consuming process. The current algorithms for de novo peptide sequencing cannot rapidly and thoroughly process large mass spectrometry datasets. In this paper, we propose MRUniNovo, a novel tool for parallel de novo peptide sequencing. MRUniNovo parallelizes UniNovo based on the Hadoop compute platform. Our experimental results demonstrate that MRUniNovo significantly reduces the computation time of de novo peptide sequencing without sacrificing the correctness and accuracy of the results, and thus can process very large datasets that UniNovo cannot. Availability and Implementation: MRUniNovo is an open source software tool implemented in java. The source code and the parameter settings are available at http://bioinfo.hupo.org.cn/MRUniNovo/index.php. Contact: [email protected] ; [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

BACKGROUND: Recent advances in omics technology have produced a large amount of liver-related data. A comprehensive and up-to-date source of liver-related data is needed to allow biologists to access the latest data. However, current liver-related data sources each cover only a specific part of the liver. It is difficult for them to keep pace with the rapid increase of liver-related data available at those data resources. Integrating diverse liver-related data is a critical yet formidable challenge, as it requires sustained human effort. RESULTS: We present LiverWiki, a first wiki-based database that integrates liver-related genes, homolog genes, gene expressions in microarray datasets and RNA-Seq datasets, proteins, protein interactions, post-translational modifications, associated pathways, diseases, metabolites identified in the metabolomics datasets, and literatures into an easily accessible and searchable resource for community-driven sharing. LiverWiki houses information in a total of 141,897 content pages, including 19,787 liver-related gene pages, 17,077 homolog gene pages, 50,251 liver-related protein pages, 36,122 gene expression pages, 2067 metabolites identified in the metabolomics datasets, 16,366 disease-related molecules, and 227 liver disease pages. Other than assisting users in searching, browsing, reviewing, refining the contents on LiverWiki, the most important contribution of LiverWiki is to allow the community to create and update biological data of liver in visible and editable tables. This integrates newly produced data with existing knowledge. Implemented in mediawiki, LiverWiki provides powerful extensions to support community contributions. CONCLUSIONS: The main goal of LiverWiki is to provide the research community with comprehensive liver-related data, as well as to allow the research community to share their liver-related data flexibly and efficiently. It also enables rapid sharing new discoveries by allowing the discoveries to be integrated and shared immediately, rather than relying on expert curators. The database is available online at http://liverwiki.hupo.org.cn /.

From smart watch to remote healthcare system, wireless body sensor networks (WBSNs) play an important role in modern healthcare system. However, the weak capacity of devices has limited WBSNs development. Considering the huge processing and storage capacity of the cloud, it can be merged with WBSNs to make up for the deficiencies of weak capacity. Based on this consideration, the concept of cloud-assisted WBSNs has been proposed recently. In contrast to generic data, the data in cloud-assisted WBSNs will be used for providing medical diagnosis, so the integrity of data is very important because any modification will result in severe consequences such as misdiagnosis. The public auditing scheme could provide an efficient solution to check the data integrity remotely without downloading them. However, the traditional public auditing scheme for cloud cannot be used directly due to the high data density and weak processing capacity in WBSNs. So, in this paper, we proposed a lightweight and efficient public auditing scheme, LEPA, for cloud-assisted WBSNs. Compared with similar schemes, the WBSNs’ client only needs to do one symmetrical encryption with low computational cost in LEPA. Security proof shows that LEPA can resist two types of adversaries in random oracle model. The efficiency evaluation also shows that LEPA outperforms previous proposals.

In a distributed, dynamic and volatile operating environment, runtime anomalies occurring in service-oriented systems (SOSs) must be located and fixed in a timely manner in order to guarantee successful delivery of outcomes in response to user requests. Monitoring all component services constantly and inspecting the entire SOS upon a runtime anomaly are impractical due to excessive resource and time consumption required, especially in large-scale scenarios. We present a spectrum-based approach that goes through a five-phase process to quickly localize runtime anomalies occurring in SOSs based on end-to-end system delays. Upon runtime anomalies, our approach calculates the similarity coefficient for each basic component (BC) of the SOS to evaluate their suspiciousness of being faulty. Our approach also calculates the delay coefficients to evaluate each BC's contribution to the severity of the end-to-end system delays. Finally, the BCs are ranked by their similarity coefficient scores and delay coefficient scores to determine the order of them being inspected. Extensive experiments are conducted to evaluate the effectiveness and efficiency of the proposed approach. The results indicate that our approach significantly outperforms random inspection and the popular Ochiai-based inspection in localizing single and multiple runtime anomalies effectively. Thus, our approach can help save time and effort for localizing runtime anomalies occuring in SOSs.

With the fast growth of applications of service-oriented architecture (SOA) in software engineering, there has been a rapid increase in demand for building service-based systems (SBSs) by composing existing Web services. Finding appropriate component services to compose is a key step in the SBS engineering process. Existing approaches require that system engineers have detailed knowledge of SOA techniques which is often too demanding. To address this issue, we propose Keyword Search for Service-based Systems (KS3), a novel approach that integrates and automates the system planning, service discovery and service selection operations for building SBSs based on keyword search. KS3 assists system engineers without detailed knowledge of SOA techniques in searching for component services to build SBSs by typing a few keywords that represent the tasks of the SBSs with quality constraints and optimisation goals for system quality, e.g., reliability, throughput and cost. KS3 offers a new paradigm for SBS engineering that can significantly save the time and effort during the system engineering process. We conducted large-scale experiments using two real-world Web service datasets to demonstrate the practicality, effectiveness and efficiency of KS3.

Along with the progress of the enterprise globalization, alliance and cooperation have become an important means for enterprises to improve their competitiveness in the market. Yet, most current methods for Service Composition Optimization (SCO) fail to address the Alliance Relation (AR) between services and assume that services are independent of each other. To address this issue, this paper presents an alliance-aware service composition method. Firstly, the fundamental properties of the AR are given based on a multi-granularity service composition model. Secondly, alliance relation granularity is coarsened into a relation granulation quotient space and the domain elements are matched reversely with service compositions, thereby reducing the complexity of query and computation of the AR. Finally, a Relation Granularity-aware Particle Swarm Optimization Algorithm (RG-PSO) is proposed based on relation granulation quotient space to solve the alliance-aware SCO prolem. Substantial experimental results show that the proposed model and algorithm are effective and efficient.

Multi-tenant service-based systems (SBSs) have become a major paradigm in software engineering in the cloud environment. Instead of serving a single end-user, a multitenant SBS provides multiple tenants with similar and yet customised functionalities with potentially different quality-of service (QoS) values. Thus, existing approaches to service selection for single-tenant SBSs are no longer suitable. Furthermore, the target multi-tenancy maturity level also needs to be considered in the service selection approach for an SBS. In this paper, we propose three novel QoS-aware service selection approaches for composing multi-tenant SBSs that achieve three different multi-tenancy maturity levels. Extensive and comprehensive experiments are conducted and the experimental results show that our approaches outperform the existing approach in both effectiveness and efficiency.

Finding the best deployment configuration that maximises energy efficiency while guaranteeing system performance of cloud applications is an extremely challenging task. It requires the evaluation of system performance and energy consumption under a wide variety of realistic workloads and deployment configurations. This paper demonstrates StressCloud, an automatic performance and energy consumption analysis tool for cloud applications in real-world cloud environments. StressCloud supports 1) the modelling of realistic cloud application workloads, 2) the automatic generation and running of load tests, and 3) the profiling of system performance and energy consumption.

In cloud environments, IT solutions are delivered to users via shared infrastructure, enabling cloud service providers to deploy applications as services according to user QoS (Quality of Service) requirements. One consequence of this cloud model is the huge amount of energy consumption and significant carbon footprints caused by large cloud infrastructures. A key and common objective of cloud service providers is thus to develop cloud application deployment and management solutions with minimum energy consumption while guaranteeing performance and other QoS specified in Service Level Agreements (SLAs). However, finding the best deployment configuration that maximises energy efficiency while guaranteeing system performance is an extremely challenging task, which requires the evaluation of system performance and energy consumption under various workloads and deployment configurations. In order to simplify this process we have developed Stress Cloud, an automatic performance and energy consumption analysis tool for cloud applications in real-world cloud environments. Stress Cloud supports the modelling of realistic cloud application workloads, the automatic generation of load tests, and the profiling of system performance and energy consumption. We demonstrate the utility of Stress Cloud by analysing the performance and energy consumption of a cloud application under a broad range of different deployment configurations.

In cloud environments, IT solutions are delivered to users via shared infrastructure. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A key objective of cloud providers is thus to develop resource provisioning and management solutions at minimum energy consumption while still guaranteeing Service Level Agreements (SLAs). However, a thorough understanding of both system performance and energy consumption patterns in complex cloud systems is imperative to achieve a balance of energy efficiency and acceptable performance. In this paper, we present StressCloud, a performance and energy consumption analysis tool for cloud systems. StressCloud can automatically generate load tests and profile system performance and energy consumption data. Using StressCloud, we have conducted extensive experiments to profile and analyse system performance and energy consumption with different types and mixes of runtime tasks. We collected fine-grained energy consumption and performance data with different resource allocation strategies, system configurations and workloads. The experimental results show the correlation coefficients of energy consumption, system resource allocation strategies and workload, as well as the performance of the cloud applications. Our results can be used to guide the design and deployment of cloud applications to balance energy and performance requirements.

Aspect mining improves the modularity of legacy software systems through identifying their underlying crosscutting concerns (CCs). However, a realistic CC is a composite one that consists of CC seeds and relative program elements, which makes it a great challenge to identify a composite CC. In this paper, inspired by the state-of-the-art information retrieval techniques, we model this problem as a semi-supervised learning problem. First, the link analysis technique is adopted to generate CC seeds. Second, we construct a coupling graph, which indicates the relationship between CC seeds. Then, we adopt community detection technique to generate groups of CC seeds as constraints for semi-supervised learning, which can guide the clustering process. Furthermore, we propose a semi-supervised graph clustering approach named constrained authority-shift clustering to identify composite CCs. Two measurements, namely, similarity and connectivity, are defined and seeded graph is generated for clustering program elements. We evaluate constrained authority-shift clustering on numerous software systems including large-scale distributed software system. The experimental results demonstrate that our semi-supervised learning is more effective in detecting composite CCs. Copyright © 2013 John Wiley & Sons, Ltd.

When operating in volatile environments, service-based systems (SBSs) that are dynamically composed from component services must be monitored in order to guarantee timely and successful delivery of outcomes in response to user requests. However, monitoring consumes resources and very often impacts on the quality of the SBSs being monitored. Such resource and system costs need to be considered in formulating monitoring strategies for SBSs. The critical path of a composite SBS, i.e., the execution path in the service composition with the maximum execution time, is of particular importance in cost-effective monitoring as it determines the response time of the entire SBS. In volatile operating environments, the critical path of an SBS is probabilistic, as every execution path can be critical with a certain probability, i.e., its criticality. As such, it is important to estimate the criticalities of different execution paths when deciding which parts of the SBS to monitor. Furthermore, cost-effective monitoring also requires management of the trade-off between the benefit and cost of monitoring. In this paper, we propose CriMon, a novel approach to formulating and evaluating monitoring strategies for SBSs. CriMon first calculates the criticalities of the execution paths and the component services of an SBS and then, based on those criticalities, generates the optimal monitoring strategy considering both the benefit and cost of monitoring. CriMon has two monitoring strategy formulation methods, namely local optimisation and global optimisation. In-lab experimental results demonstrate that the response time of an SBS can be managed cost-effectively through CriMon-based monitoring. The effectiveness and efficiency of the two monitoring strategy formulation methods are also evaluated and compared.

The service-oriented paradigm offers support for engineering service-based systems (SBSs) based on service composition where existing services are composed to create new services. The selection of services with the aim to fulfil the quality constraints becomes critical and challenging to the success of SBSs, especially when the quality constraints are stringent. However, none of the existing approaches for quality-aware service composition has sufficiently considered the following two critical issues to increase the success rate of finding a solution: 1) the complementarities between services; and 2) the competition among service providers. This paper proposes a novel approach called combinatorial auction for service selection (CASS) to support effective and efficient service selection for SBSs based on combinatorial auction. In CASS, service providers can bid for combinations of services and apply discounts or premiums to their offers for the multi-dimensional quality of the services. Based on received bids, CASS attempts to find a solution that achieves the SBS owner's optimisation goal while fulfilling all quality constraints for the SBS. When a solution cannot be found based on current bids, the auction iterates so that service providers can improve their bids to increase their chances of winning. This paper systematically describes the auction process and the supporting mechanisms. Experimental results show that by exploiting the complementarities between services and the competition among service providers, CASS significantly outperforms existing quality-aware service selection approaches in finding optimal solutions and guaranteeing system optimality. Meanwhile, the duration and coordination overhead of CASS are kept at satisfactory levels in scenarios on different scales.

Cloud computing delivers IT solutions as a utility to users. One consequence of this model is that large cloud data centres consume large amounts of energy and produce significant carbon footprints. A common objective of cloud providers is to develop resource provisioning and management solutions that minimise energy consumption while guaranteeing Service Level Agreements (SLAs). In order to achieve this objective, a thorough understanding of energy consumption patterns in complex cloud systems is imperative. We have developed an energy consumption model for cloud computing systems. To operationalise this model, we have conducted extensive experiments to profile the energy consumption in cloud computing systems based on three types of tasks: computation-intensive, data-intensive and communication-intensive tasks. We collected fine-grained energy consumption and performance data with varying system configurations and workloads. Our experimental results show the correlation coefficients of energy consumption, system configuration and workload, as well as system performance in cloud systems. These results can be used for designing energy consumption monitors, and static or dynamic system-level energy consumption optimisation strategies for green cloud computing systems.

Software architecture recovery is to gain the architectural level understanding of a software system while its architecture description does not exist. In recent years, researchers have adopted various software clustering techniques to detect hierarchical structure of software systems. Most graph clustering techniques focus on the connectivity between program elements, but unreasonably ignore the similarity which is also a key measure for finding elements of one module. In this paper we propose a novel hierarchy graph clustering algorithm DGHC, which considers both similarity and connectivity between program elements. During the transformation of program dependence graph edges representing similarity between elements are added. Then similar elements are grouped by density-based approaches. The alternative strategy is adopted to find groups of closely connected and similar elements. Meanwhile we adjust the contribution of connectivity and similarity by a flexible clustering algorithm based on short random walk model, which can obtain more structure information of software to find its multiple layers. Furthermore a new method called Multi-layer Propagation Gap is proposed to suggest stable layers of hierarchy clustering result as multiple layers of software system. Extensive experimental results illustrate the effectiveness and efficiency of DGHC in detecting hierarchy structure of software through comparison with various software clustering methods.

Service-Oriented Computing (SOC) is emerging as a paradigm for developing distributed applications. A critical issue of utilizing SOC is to have a scalable, reliable, and robust service discovery mechanism. However, traditional service discovery methods using centralized registries can easily suffer from problems such as performance bottleneck and vulnerability to failures in large scalable service networks, thus functioning abnormally. To address these problems, this paper proposes a peer-to-peer-based decentralized service discovery approach named Chord4S. Chord4S utilizes the data distribution and lookup capabilities of the popular Chord to distribute and discover services in a decentralized manner. Data availability is further improved by distributing published descriptions of functionally equivalent services to different successor nodes that are organized into virtual segments in the Chord4S circle. Based on the service publication approach, Chord4S supports QoS-aware service discovery. Chord4S also supports service discovery with wildcard(s). In addition, the Chord routing protocol is extended to support efficient discovery of multiple services with a single query. This enables late negotiation of Service Level Agreements (SLAs) between service consumers and multiple candidate service providers. The experimental evaluation shows that Chord4S achieves higher data availability and provides efficient query with reasonable overhead.

Cloud-based software applications (Software as a Service - SaaS) for multi-tenant provisioning have become a major development paradigm in Web engineering. Instead of serving a single end-user, a multi-tenant SaaS provides multiple end-users with the same functionality but with potentially different quality-of-service (QoS) values. The service selection for such a SaaS is a complex decision-making process which involves a number of stakeholders with different QoS requirements. SaaS developers need to compose services with different QoS values to meet end-users' different multidimensional QoS constraints for the SaaS. Furthermore, they also need to satisfy SaaS providers' optimisation goals for the SaaS, such as least resource cost and best system performance. Existing QoS-aware service selection approaches are oriented at a single tenant. They do not consider the characteristics of multi-tenant SaaS and hence are ineffective and inefficient when applied to compose multi-tenant SaaS. In this paper, we introduce a novel QoS-driven approach for helping SaaS developers select the services for composing multi-tenant SaaS, which achieves SaaS providers' optimisation goals while fulfilling the end-users' different levels of QoS constraints. The proposed approach is evaluated using an example SaaS synthetically generated based on a dataset of real-world Web services. Experimental results show that our approach significantly outperforms existing approaches in terms of both effectiveness and performance.

In the Web services environment, service level agreements (SLA) refer to mutually agreed understandings and expectations of service provision between service consumers and providers. Although management of SLA is critical to wide adoption of Web services technologies in the real world, support for it is very limited nowadays. There lacks adequate frameworks and technologies supporting various SLA operations. This paper presents an agent-based framework which utilises the agents' ability of negotiation, interaction, and cooperation to facilitate autonomous and flexible SLA management. Based on this framework, mechanisms for autonomous SLA formation, recovery, and profiling are proposed and discussed.