The Fog computing paradigm makes use of dispersed, diverse, and resource-limited devices located at the network edge to effectively implement Internet of Things (IoT) application services that demand low latency and substantial bandwidth. At the same time, the adoption of microservice-based architectures in the IoT domain is on the rise due to their ability to align with the swift evolution and deployment demands of highly dynamic IoT applications and to elastically scale to fulfill load demands. In complex environments like Fog federations, characterized by highly heterogeneous computing and networking resources, the effective allocation of microservices to available nodes, while ensuring compliance with required Quality of Service (QoS) constraints, represents a significant challenge. In this paper, we present the design and implementation of OptiFog, a comprehensive framework that enables users to model, simulate, and validate microservice placement solutions within a realistic testbed environment. Compared to state-of-the-art approaches, OptiFog offers developers a controlled environment for experimenting with placement solutions while providing the assurance that the resulting deployments will meet the targeted QoS requirements in real-world scenarios, specifically in terms of service execution time and energy consumption of Fog nodes. To demonstrate the feasibility of the proposed approach, we implemented and evaluated a representative use case, involving both sub-optimal and optimal microservice placement, and utilizing real-world microservices drawn from the IoT domain.

The adoption of Service-Based Architecture (SBA) in 5G Core Networks (5GC) has significantly transformed the design and operation of the control plane, enabling greater flexibility and agility for cloud-native deployments. While the infrastructure has initially evolved by implementing key functions, there remains significant potential for additional services, such as localization, paving the way for the integration of the Location Management Function (LMF). However, the extensive functional decomposition within SBA leads to consequences, such as the increase of control plane operations. Specifically, we observe that the additional signaling traffic introduced by the presence of the LMF overwhelms the Access and Mobility Management Function (AMF) which is responsible for authentication and mobility. In fact, in mobile positioning, each connected mobile device requires a significant amount of control traffic to support location algorithms in the 5GC. To address this scalability challenge, we analyze the impact of three well-known optimization techniques on location procedures to reduce control message traffic in the specific context of the 5GC, namely a caching system, a request aggregation system, and a service scalability system. Our solutions are evaluated in an OpenAirInterface (OAI) emulated environment with real hardware. After the analysis in the emulated environment, we select the caching system – due to its feasibility – for being analyzed in a real 5G testbed. Our results demonstrate a significant reduction in the additional overhead introduced by the LMF, improving scalability by minimizing the impact on AMF processing time up to a 50% reduction.

Traditional SDN controllers are usually deployed as monolithic systems or tightly coupled service chains, limiting their adaptability in distributed or federated network domains. This paper presents eMSN, a microservice-based SDN framework that enables controller decomposition and decentralized multidomain orchestration, providing a foundation for scalable, and domain-aware SDN experimentation. The framework introduces lightweight, containerized microservices that interact via REST APIs and coordinate through a shared ETCD cluster. The main one, called FlowBlocker, collects topology and host information from the emitter, builds a policy-aware decision table, and shares domain-scoped state in ETCD. The latter stores only host/topology data and never flow rules; enforcement decisions remain within FlowBlocker, which installs rules locally via Ryu Core or coordinates with peer FlowBlockers across domains. The architecture supports centralized, partially decentralized, and fully decentralized deployment models. The proof-of-concept implementation uses Docker and Mininet for reproducibility. Functional evaluation demonstrates sub-millisecond Packet-In responsiveness, tens-of-milliseconds policy enforcement latency, and correct blocking of unauthorized traffic.

In today’s fast-evolving technological landscape, the Internet of Things (IoT) is fundamentally reshaping how systems connect, interact, and exchange data. As billions of devices become interconnected, the IoT brings both unprecedented opportunities and significant challenges across various domains. Emerging technologies, particularly quantum communication networks, offer transformative solutions by enabling ultra-secure data exchange within IoT infrastructures through advanced techniques such as Quantum Key Distribution (QKD). However, quantum link delay remains one of the key challenges that hinder the effective utilization of these networks. Traffic engineering is a robust method to address this challenge and optimize the performance of quantum networks. This technique involves assessing the current state of the network and making dynamic adjustments based on real-time conditions. Despite its proven benefits in classical systems, its role in quantum networks remains largely unexplored, with no comprehensive framework to date. As a result, in this paper, we propose a framework for quantum traffic engineering and discuss its core components—non-invasive measurements, quantum traffic matrices, data analysis, and performance control. Additionally, we integrate the Particle Swarm Optimization (PSO) algorithm into our framework to minimize the quantum link delay. Ultimately, this work establishes the foundation for researchers in quantum network traffic engineering and sets the stage for continuous, and dynamic monitoring of these networks.

In today’s fast-evolving technological landscape, the Internet of Things (IoT) is fundamentally reshaping how systems connect, interact, and exchange data. As billions of devices become interconnected, the Internet of Things brings unprecedented opportunities and significant challenges in various domains. Emerging technologies, particularly quantum communication networks, offer transformative solutions by enabling ultra-secure data exchange within IoT infrastructures through advanced techniques such as quantum key distribution. However, a significant obstacle to the efficient use of these networks is the lack of current status knowledge of network parameters, which is essential for continuous and dynamic management through quantum traffic engineering. In this paper, we propose the novel concept of quantum traffic matrices as a foundational framework to capture the dynamic operational state of quantum networks. We begin by distinguishing classical traffic matrices from their quantum counterparts. We then identify and present mathematical models for eleven key quantum network parameters that are essential to enable continuous and dynamic management of quantum networks. Our approach and results serve as crucial input for the development of quantum traffic engineering strategies, paving the way for intelligent control, optimization, and resilience enhancement of future quantum communication infrastructures.

Software-Defined Networking traditionally relies on the separation of the control and data planes, centralizing network intelligence within a logically unified controller. However, centralizing control functionalities often introduces limitations that negatively impact the overall Quality of Service, particularly in distributed and heterogeneous network scenarios. In this paper, we explore an adaptive approach to orchestrate a microservice-based SDN controller dynamically at the Edge. Building upon our previously introduced frameworks for Microservice-based SDN Controller and for flexible service-model-aware orchestration, we investigate the benefits of adaptively deploying our microservice-based SDN controller’s functionalities at the Edge to enhance QoS. We leverage our orchestration framework to dynamically decide and execute the optimal placement of latency-critical microservices according to real-time monitoring data and evolving user demands. We evaluate the performance by comparing various deployment strategies, focusing on the tradeoff between control plane latency and placement of controller functionalities. Results demonstrate that our adaptive Edge deployment approach has the potential to reduces control plane latency, demonstrating the practical benefits of integrating SDN controller modularity with intelligent service orchestration in dynamic and heterogeneous network environments.

The Open RAN specification introduces the Near Real-Time RAN Intelligent Controller (Near-RT RIC) as a closer point of orchestration, providing real-time analysis and control of gNBs through Operator-defined Near-Real Time Applications (xApps). The continuous gathering, storing, and processing of metrics and logs from various hardware and software components of the network is a complex task. In the cloud community, this complexity has been addressed through approaches and tools under the umbrella of observability. An observation framework applied to O-RAN can assure a holistic view of the infrastructure and services running on it, helping optimize performance, ensure interoperability among multi-vendor systems, enhance scalability, bolster security, and reduce operational costs by providing realtime insights. However, existing observability frameworks poorly fit O-RAN use cases due to their service-oriented architecture, which impacts performance and scalability. To fill this gap, we propose CLO5ER, a framework that facilitates the creation of observability workflows through the composition of O-xApp. Our solution integrates seamlessly with existing observability frameworks, providing an accelerated alternative path for processing signals from gNBs. Additionally, we introduce a novel xApp controller that optimizes O-xApp placement and instrumentation. Furthermore, our solution features a hierarchical message-oriented middleware that enhances xApp composability and data exchange.

Function as a Service (FaaS) is a serverless cloud computing model that enables customers to encapsulate their business logic in functions. The platform automatically executes these functions each time a specified event occurs and are terminated once the triggering event is processed. Function as a Service (FaaS) workflows are often supported by ready-to-use and fully managed services hosted by cloud providers belonging to the so-called Backend as a Service (BaaS). The integration of these two serverless models offers comprehensive support to developers, enabling them to focus on business logic development while benefiting from a fully managed and automated infrastructure. However, state-of-the-art FaaS platforms currently lack direct support for integrating Backend as a Service (BaaS) services in FaaS functions, often resulting in service calls being hard-coded within function code. This approach reduces the modularity of functions, enforces vendor lock-in, and negatively impacts the performance of FaaS workloads.In this work, we propose SFIOC, an architecture that facilitates the integration of BaaS services into FaaS functions through Dependency Injection. SFIOC enhances existing FaaS solutions by enabling dynamic and at-runtime resolution of function service dependencies. SFIOC automates dependency management, thereby improving the maintainability and modularity of serverless functions while preserving workflow performance.The capabilities of our solution are demonstrated through an extensive testbed that encompasses the enhancement with SFIOC of a public cloud provider and an open-source-based private edge environment.

Open Cloud (O-Cloud) is the Open Radio Access Network (O-RAN) computing infrastructure spanning edge to cloud sites defined by the O-RAN Alliance to support and coordinate RAN infrastructure management shared among multiple Mobile Network Operators (MNO). In the standard definition, O-Cloud ensures reliable connectivity, active coordination, and efficient distribution of the Radio Access Network (RAN) deployments. Thanks to these properties, O-Cloud can support Mobile Network and Infrastructure operators to achieve fully automated infrastructure management, self-service MNO portals, enhanced security measures, and O-Cloud infrastructure-agnostic management. In this paper, we present TORNADO: TOSCA-enabled Orchestration for RAN in Next-Generation Networks Automating DevOps in O-Cloud, an O-Cloud automation infrastructure designed to ease DevOps deployment and operation phases of RAN network functions for multiple MNOs. Our solution introduces infrastructure-agnostic automation for multi-site, multi-MNO RAN components, offering high-level, secure self-service MNO portals for defining RAN deployments. Performance evaluation of our solution for various RAN network functions demonstrated the capability of TORNADO to automate the deployment in a multi-site, multi-MNO heterogeneous infrastructure.

The dynamic optimization of antenna tilt angles represents a critical challenge in 5 G networks, directly impacting coverage, capacity, and overall network performance. Traditional manual configuration approaches are becoming inadequate for managing the complexity of modern cellular networks. This paper explores the feasibility of automating 5 G antenna tilt angle optimization using Deep Deterministic Policy Gradient (DDPG) reinforcement learning and explainable AI (XAI) techniques. We train and evaluate a DDPG agent on a publicly available dataset from Kaggle, demonstrating the algorithm’s ability to learn effective tilt adjustment strategies. Counterfactual explanations provide transparency in the decision-making process, addressing the need for interpretable AI systems in critical infrastructure. This initial implementation, designed for eventual integration as an O-RAN compliant xApp, lays the groundwork for future development and experimental validation in simulated and real-world 5 G environments.

Kubernetes (K8S) is a widely used orchestration solution that helps manage complex IT applications by providing mechanisms for autoscaling, health checking, cluster formation, and replication, which are essential to deploy and manage the multitude of connected microservices. However, they may suffer in case of unexpected faults which can severely change the underlying computing infrastructure and lead to service outages, highlighting the need for resilient solutions capable of mitigating the adverse effects of faults. To address this, the TELKA sched-uler integrates Chaos Engineering (CE), Reinforcement Learning (RL), and Digital Twin (DT) to reallocate K8S pods evicted due to unexpected faults. While TELKA showed promising results in reallocating evicted pods, its preliminary implementations suffered from scalability issues, as the RL agent could only effectively operate on scenarios with the same number of nodes seen during training. To overcome this limitation, this paper improves TELKA by incorporating a neural network architecture called Deep Sets (DS), which can generalize the operation of TELKA on different numbers of nodes. Experimental results not only demonstrate the validity of the improved TELKA but also show how it can be used to identify good operating conditions.

Digital Twins (DTs) are emerging as key enablers for Connected and Autonomous Vehicles (CAVs), offering virtual representations that support various applications ranging from offline, large-scale traffic analysis to real-time driver assistance. These use cases pose significantly diverse Quality of Service (QoS) requirements on DTs, including ultra-low latency for real-time synchronization with the physical counterparts. Deploying DTs at the network edge offers a promising solution, considering the increasingly advanced compute and network resources potentially available in a city-wide infrastructure. However, edge deployments introduce additional complexity: DT developers must deal with heterogeneous resources, optimize their usage for different QoS levels, and handle vehicle mobility. That process requires a high level of specialization and makes development time-consuming and error-prone. In this paper, we first introduce a DT communication model based on three key interfaces: to physical devices, to peer DTs, and to centralized applications. We then analyze the distinct QoS requirements of these interfaces and propose the adoption of a data distribution platform that maps them directly to edge network capabilities, hiding complexity and easing the DT development process. Early evaluations on a real testbed demonstrate the platform's potential to meet CAV DTs' QoS demands efficiently.

The Software-Defined Networking (SDN) paradigm disaggregates the data plane, embodied by switches that only forward data, from the control plane, embodied by SDN controllers that communicate with said switches. SDN also proposes a third, application layer, which implements various functions such as firewalls or service discovery by communicating with the controllers through the northbound interface. However, while state-of-the-art works propose the deployment of multiple, distributed SDN controllers, the software architecture of these controllers is still monolithic, requiring not only the controller runtime but also all the network-level applications to be deployed across all SDN controller hardware. On the other hand, state-of-the-art SDN controllers such as MSN allow treating network-level applications as microservices, which comes with the challenge of orchestrating the microservices across the network. In this paper, we present Grex, a framework to orchestrate network-level applications across microservice-based SDN controllers. We test and validate the optimization model of Grex by performing experiments in a realistic network testbed using the MSN controller.

A major weakness of Mobile CrowdSensing Platforms (MCS) is the willingness of users to participate, as this implies disclosing their private data (for example, concerning mobility) to the MCS platform. In the effort to enforce data privacy in the creation of mobility coverage maps using an MCS platform, recent work proposes the use of a spatially distributed approach that, however, is vulnerable to data injection attacks. In this contribution, we define and implement a progressive attacker model following a statistical approach. We propose a novel mitigation strategy based on unsupervised anomaly detection. Accessing the coverage performance with real-world mobility data indicates that the mean value of the attacker’s profile determines the probability of being revealed. In particular, we are able to identify the attacker and filter out the data injected by the attackers with high precision.

The paradigm shift towards the fifth generation of mobile networks (5G) has entailed a transition from a monolithic architecture to a completely disaggregated microservice-based architecture of network functions. This architectural renovation decouples and distributes Containerized Network Functions (CNF), tailored to address the specialized requirements of the Open Radio Access Network (Open RAN) deployment. Specifically, Kubernetes has emerged as a pivotal orchestrator for CNF within the context of disaggregated Open RAN. In particular, the deployment of Operator-defined Applications (xApps) for Near Real-Time RAN Intelligent Controller (Near-RT RIC) scenarios calls for communication substrates to facilitate the communication and coordination with other RAN components, and consequently, to dial with increased dynamicity and flexibility of 5G deployments. To face these issues, we propose service mesh as a cloud-native technology extending the Kubernetes communication infrastructure. Service mesh fulfills diverse requisites of xApps running in the RAN Intelligent Controller for traffic management, instrumentation, security, and observability, inherently facilitating a zero-trust architecture through the incorporation of sidecar proxies co-located with microservices. Moreover, we evaluate the performances of another emerging paradigm, raising as a lightweight solution that allows a more incremental adoption compared to service mesh, represented by ambient mesh. This paper analyses service and ambient mesh for deploying xApps in a state-of-the-art Near-RT RIC, i.e., the O-RAN SC implementation, evaluating both qualitative and quantitative attributes.

The advent of the Open Radio Access Network (O-RAN) specifications for 5G and 6G Radio Access Networks (RANs) has brought forth a great interest in the use of machine learning to perform control and management tasks. The integration of machine learning in the O-RAN architecture is initially envisioned to be implemented through xApps, applications that act in a near-real timescale and that have machine learning models meant for specific tasks. However, the development of machine learning-based xApps presents challenges, as although the xApp architecture facilitates component reusability for the RAN, the state-of-the-art architectures for xApps themselves require the implementation of an ad-hoc xApp for each machine learning model. Therefore, these architectures limit the reusability of the components of xApps as applications, even for xApps meant for the same purpose. To address these issues, we propose the Intelligent xApp Architecture (IxAA), a software architecture to simplify the implementation of machine learning-based xApps with a focus on reuse, easing the comparison of machine learning models. As a proof of concept, we developed xAssessment, an xApp to evaluate the performance of data prediction models. Our evaluation shows the performance results of five machine learning models predicting three different RAN metrics through xAssessment in a simulated O-RAN testbed.

In recent years, Software-Defined Networking (SDN) research literature has proposed the integration of multiple SDN controllers into the same network, improving the scalability and reliability of the network. However, while this evolution has focused on control plane hardware, the architecture of SDN controller software is still monolithic, and its communication with the application plane through the northbound interface is done by the integration of the network-level applications' codebase with the controller software. The proposal of SDN Mi-croservices Architectures (SDN MSAs) is aimed at transforming the application plane, from a monolithic architecture to a set of independently deployable modules named SDN microservices. However, the promising paradigm of SDN MSAs also increases the complexity of network management, as these microservices must be placed through the SDN controllers. This placement is especially complex due to its NP-hard nature. In this work, we present Genetic Algorithm for SDN MSA (GASM), an evolutionary computation-based heuristic to solve this issue in tractable times. Experimental results show that GASM represents an average speed-up of 846.33 × compared to optimal solvers.

The softwarization of networks is increasingly spreading, and one of the main paradigms is SDN (Software-Defined Networking), which allows overcoming the limitations mainly arising from the integration of the control plane and the forwarding plane within the network devices. It extracts the control plane to place it within a new logically centralized component: the SDN controller. Since this is a monolithic architecture that limits reliability and scalability, distributed solutions based on microservices have been proposed in the literature. In parallel, Agents are fully intelligent, atomic, and autonomous decision-making units that can be flexibly recomposed to create a completely autonomous network system. They also have the ability to replicate single or multiple decision-making processes that collaborate with each other. The development of future networks such as 5G, including 6G, is pushing towards the concept of network management automation and integration of intelligence, making agents an excellent means to meet this trend. This paper first introduces intelligence in the form of agents to a distributed SDN controller based on microservices, by implementing two new functionalities: topology _learning and shortest path, Then, it leverages a microservices-based SDN solution based on Ryu SDN framework, named MSN, to run agents in a Docker Container environment. Multiple measurements were performed locally in a single machine. Results show the topology learning performances compared with several network topologies. Moreover, the shortest patti agent experimental evaluations show the knowledge size depends on the network topology and the performances of different algorithms.

The recent trend pushing towards reliance on edge computing, virtualization and programmatic 5G network control has sparked the development of a myriad of open-source resource management and orchestration projects for improved control and added flexibility, making up for a rich and complex ecosystem of frameworks and tools with varying degree of support for standardized features. In this technological panorama, the ETSI Multi-Access Edge Computing (MEC) standard proposes a conceptual reference architecture, standardizing edge integration, interoperability and application management in an extended 5G edge-core architecture. In this context, we propose an application-aware orchestration solution for 5G edge-core distributed deployments, currently lacking support in state-of-the-art frameworks and tools. The proposal is built on an experimental and distributed deployment of the OpenAirInterface minimal MEC platform implementation and relies on the Kubernetes Operator pattern for the automatic MECApp lifecycle management. To validate our approach, we conduct a series of experiments, reporting key metrics of interest.

Smart logistics is paramount within the multifaceted framework of smart cities. The evolving landscape of sales channels has intensified the demand for efficient logistics solutions capable not only of optimizing the delivery process but also of controlling the environmental impact within the urban setting. Digital Twin solutions have emerged as promising tools to navigate the complexities of modern logistics scenarios. Acc2Twin is an ongoing project, targeting the optimization of the pallet wrapping cycle for package delivery, reducing plastic waste. The solution accurately reconstructs delivery routes and relies on driving profiles to infer the physical forces experienced by delivery trucks, used to assess the stretch and integrity of the plastic film. In this article, we focus our attention on RouteVel, a system component adopted to enrich topological data by augmenting point density through a mix of interpolation techniques, enabling precise analysis of acceleration profiles used as input to the physics-based Digital Twin to assess the pallet wrapping cycle stability. The paper also provides insight into the performance assessment of the tool to understand its potential and the potential points for improvement.

The convergence of multi-access edge computing and fog computing in industrial settings offers benefits such as scalability, low latency, availability, and high-level management. In this context, service virtualization plays a pivotal role, and the Kubernetes microservice orchestrator performs the platform role in numerous industrial environments. However, Kubernetes does not provide advanced support for service migration and service data synchronization. The management of application migration, whether stateless or stateful, between cluster nodes is crucial, and it is not currently addressed. To fill this gap, we propose stateful service migration support entirely managed within Kubernetes. The approach we present in this article enhances platform awareness by integrating a proactive preparation mechanism for migration. This mechanism manages information and makes it available for applications, dynamically applying specific policies. We tested the solution in a challenging real industrial environment, stressing the mechanism with demands such as availability requests, network congestion, and a high volume of data to migrate. The experimental results confirm the effectiveness of the proposed solution in terms of reliability, good uptime, and easy applicability to Industry 4.0 stateful services.

Chaos engineering is the discipline of injecting computing and network faults, such as increased network latency and unavailability of computing nodes, into an IT system to help developers in identifying problems that could arise in a production environment and tackle them. Several tools have emerged to ease the application of chaos engineering to complex IT systems, leveraging microservice and container-based applications deployed on Kubernetes. However, applying of such tools requires several phases to be put into practice, from defining a steady state to establishing an effective response plan if something goes wrong. To ease the application of chaos engineering in improving the resilience of Kubernetes applications, this work presents a smart scheduler for Kubernetes called TELKA: a Twin-Enhanced Learning for Kubernetes Applications, which combines chaos engineering, Digital Twin (DT), and Reinforcement Learning (RL) methodologies to mitigate the effects of computing and network faults. Instead of interacting directly with the physical Kubernetes application, TELKA learns by interacting with a digital twin, thus reducing the learning time and the operation costs related to the application of chaos engineering. Experiment results compare TELKA with other approaches to show its effectiveness in mitigating the adverse effects of injected faults.

Microservice architecture is a service-oriented paradigm that enables the decomposition of cumbersome monolithic-based software systems. Using microservice design principles, it is possible to develop flexible, scalable, reusable, and loosely coupled software that could be containerized and deployed in a distributed edge/cloud environment. The flexible deployment of microservices in an edge environment increases system performance in terms due to dynamic service function placement and chaining possibly resulting in latency reduction, fault tolerance, scalability, efficient resource utilization, cost reduction, and energy consumption reduction. On the other hand, virtualization and containerization of microservices add processing and communication overheads. Therefore, to evaluate end-to-end microservices-based system performance, we need to have an end-to-end mathematical formulation of the overall microservice-based network system. Incorporating the virtualization overhead, here we provide end-to-end mathematical formulation considering system parameters: latency, throughput, computational resource usage, and energy consumption. We then evaluate the formulation in a testbed environment with the Microservice-based SDN (MSN) framework that decomposes the Software-defined Networking (SDN) controller in microservices with Docker Container. The final result validates the presented mathematical modeling of the system’s dynamic behavior which can be used to design a microservice-based system.

Kubernetes is a well-known orchestration and management solution for complex and large-scale service architectures in the Cloud Continuum. While it provides very valuable functions from the operation perspective, the high number of control loops it implements significantly enlarges the already wide space of configuration parameters and policies to consider for management purposes. We argue that optimizing complex Kubernetes deployments considering a multi-cloud and edge computing environment would significantly benefit from a Digital Twin approach, enabling an accurate virtual representation of a Kubernetes application to optimize its deployment and management policies. Towards that goal, this work illustrates the design of KubeTwin, a framework to implement Digital Twins of Kubernetes deployments. Furthermore, we present a validation of KubeTwin in a Multi-access Edge Computing (MEC) scenario, which shows its soundness in reenacting realistic Digital Twins of complex and highly distributed Kubernetes deployments. We believe that KubeTwin can provide useful guidance to the research community working in this field.

Open Radio Access Network (O-RAN) is an emerging paradigm proposed for enhancing the 5G network infrastructure. O-RAN promotes open vendor-neutral interfaces and virtualized network functions that enable the decoupling of network components and their optimization through intelligent controllers. The decomposition of base station functions enables better resource usage, but also opens new technical challenges concerning their efficient orchestration and allocation. In this paper, we propose Proactive Resource Orchestrator based on Reinforcement Learning (PRORL), a novel solution for the efficient and dynamic allocation of resources in O-RAN infrastructures. We frame the problem as a Markov Decision Process and solve it using Deep Reinforcement Learning; one relevant feature of PRORL is that it learns demand patterns from experience for proactive resource allocation. We extensively evaluate our proposal by using both synthetic and real-world data, showing that we can significantly outperform the existing algorithms, which are typically based on the analysis of static demands. More specifically, we achieve an improvement of 90% over greedy baselines and deal with complex trade-offs in terms of competing objectives such as demand satisfaction, resource utilization, and the inherent cost associated with allocating resources.

The widespread adoption of Internet of Things (IoT) motivated the emergence of mixed workload scenarios in smart cities, where fast arriving geo-referenced massive amounts data streams need to be joined with archive tables, at scale. This aims at enriching streams with descriptive attributes that enable deeper insightful analytics. More applications are now relying on finding, in real-time, to which geographical region each data streaming spatially-tagged tuple belongs. This problem requires a computationally intensive stream-static join operation, where one side of join is a dynamic stream while the other is a disk-resident static table. Even with emergence of some libraries that solve this problem in static-static fashion, their adoption for live scenarios is challenging because join operations are expensive in real-time. In addition, the time-varying nature of fluctuation and skewness in the geospatial data loads arriving online calls for an approximate solution that can trade-off QoS constraints in a way which ensures that the system survives sudden spikes in data loads. In this paper, we present SpatialSSJP, an adaptive spatial-aware approximate query processing system that specifically focuses on stream-static joins in a way that guarantees achieving an agreed set of Quality-of-Service goals and maintains geo-statistics of stateful online aggregations over stream-static join results. SpatialSSJP employs a state-of-art stratified-like sampling design to select well-balanced representative geospatial data stream samples and serve them to a stream-static geospatial join operator downstream. We implemented a prototype atop Spark Structured Streaming. Our extensive evaluations on big real datasets show that our system can survive and mitigate harsh join workloads and outperform state-of-art baselines by significant magnitudes, without risking rigorous error bounds in terms of the accuracy of the output results. SpatialSSJP achieves a relative accuracy gain against plain Spark joins of approximately 10% in worst cases but reaching up to 50% in best case scenarios.

The use of microservice-based applications is becoming more prominent also in the telecommunication field. The current 5G core network, for instance, is already built around the concept of a “Service Based Architecture”, and it is foreseeable that 6G will push even further this concept to enable more flexible and pervasive deployments. However, the increasing complexity of future networks calls for sophisticated platforms that could help network providers with their deployments design. In this framework, a central research trend is the development of digital twins of the physical infrastructures. These digital representations should closely mimic the behavior of the managed system, allowing the operators to test new configurations, analyze what-if scenarios, or train their reinforcement learning algorithms in safe environments. Considering that Kubernetes is becoming the de-facto standard platform for container orchestration and microservice-based application lifecycle management, the implementation of a Kubernetes digital twin requires an accurate characterization of the microservice response time, possibly leveraging suitable Machine Learning techniques trained with measurement data collected in the field. In this paper we introduce a new methodology, based on Mixture Density Networks, to accurately estimate the statistical distribution of the response time of microservice-based applications. We show the improvement in performance with respect to simulation-based inference procedures proposed in literature.

The Software-Defined Networking (SDN) paradigm enables network administrators to manage the behavior of the network thanks to a centralized control plane. By programming network-level applications, it is possible to determine how traffic flows must be handled programmatically. In the same manner that computing applications have evolved from monolithic to microservice-based architectures, network-level applications are expected to evolve into microservice-based SDN controllers, implementing each application as a replicable and individually deployable component of the SDN controller. In such a scenario, the Quality of Service (QoS) experienced by traffic flows depends on how these microservices are placed and deployed through the network topology. In this work, we provide a system to optimize the QoS of the traffic in microservice-based SDN networks by optimally placing and replicating the network-level microservices. Experimental results show the effectiveness of the proposed solution over a real network topology with varying traffic loads.

The advent of Industry 4.0, resulting from the digitalization of industry, has led to a surge in demand for Industrial IoT and Edge Computing, which in turn has prompted major IT players and Foundations to launch their own IIoT frameworks and service solutions. Despite the huge advantages that this service solutions offer, the ready-to-use IoT-Cloud services the big players provide are seen by companies as black boxes, and more than often these services do not fit with the real industrial scenario of the companies or do not fully meet the use case requirements. To address these issues, we propose an integration solution to empower and adapt the services of one of the most used IoT Cloud solutions, AWS IoT. In addition, we show the implementation of our work in a real scenario provided by the company Northvolt. Finally, we demonstrate the goodness and efficiency of the proposed solution by showing the performance and latency tests along with their experimental results.

The emerging paradigm of resource disaggregation enables the deployment of cloud-like services across a pool of physical and virtualized resources, interconnected using a network fabric. This design embodies several benefits in terms of resource efficiency and cost-effectiveness, service elasticity and adaptability, etc. Application domains benefiting from such a trend include cyber-physical systems (CPS), tactile internet, 5G networks and beyond, or mixed reality applications, all generally embodying heterogeneous Quality of Service (QoS) requirements. In this context, a key enabling factor to fully support those mixed-criticality scenarios will be the network and the system-level support for time-sensitive communication. Although a lot of work has been conducted on devising efficient orchestration and CPU scheduling strategies, the networking aspects of performance-critical components remain largely unstudied. Bridging this gap, we propose KuberneTSN, an original solution built on the Kubernetes platform, providing support for time-sensitive traffic to unmodified application binaries. We define an architecture for an accelerated and deterministic overlay network, which includes kernel-bypassing networking features as well as a novel userspace packet scheduler compliant with the Time-Sensitive Networking (TSN) standard. The solution is implemented as tsn-cni, a Kubernetes network plugin that can coexist alongside popular alternatives. To assess the validity of the approach, we conduct an experimental analysis on a real distributed testbed, demonstrating that KuberneTSN enables applications to easily meet deterministic deadlines, provides the same guarantees of bare-metal deployments, and outperforms overlay networks built using the Flannel plugin.

The Internet of Things has brought digitalization to intensive domains through the automation of their real-world processes. However, the criticality of these processes is reflected in high Quality of Service (QoS) requirements for the application to work properly. Moreover, business-level QoS, such as the operational cost, are also key to the feasibility of these applications. This QoS depends on three, closely-related dimensions: the application software, the computing devices and the communication network, which provide high flexibility to obtain different performances at different costs. Thus, to achieve optimal QoS in these scenarios, the application, computing and networking dimensions must be optimized, considering their crucial interplay in a joint effort. Furthermore, this solution must allow multi-objective optimization, finding the optimal trade-off between operational cost and application performance. In this paper, we present Multi-Objective SDN Fog Optimization (MO-SFO), a holistic framework that allows for the optimization of both the response time and the deployment cost. MO-SFO is evaluated over an emulated smart city case study, showing the cost and performance trade-off achieved in different topologies.

Recently, the Industrial Internet of Things (IIoT) and Industry 4.0 paradigms have attracted considerable relevance by leading all manufacturing companies to invest in IIoT solutions in order to be competitive on the market. Despite the huge number of IIoT solutions on the market, all platforms refer to a common architectural model and share the same functionalities. Among these functionalities, the most relevant is the capability of deploy and run custom containerized applications. This feature has a pivotal role of customizing the IIoT platform and tailoring it to each business scenario. In this paper, we define the general architectural model and common functionalities of the IIoT platforms. Then, we analyze and compare two of the most used IIoT solutions on the market, Siemens Industrial Edge, and EdgeX Foundry. We also define three metrics for evaluating the functionality of deploying custom services on the edge, and we test these two platforms. Finally, we present their comparative performance, advantages, and limitations.

Kubernetes provides several functions that can help service providers to deal with the management of complex container-based applications. However, most of these functions need a time-consuming and costly customization process to address service-specific requirements. The adoption of Digital Twin (DT) solutions can ease the configuration process by enabling the evaluation of multiple configurations and custom policies by means of simulation-based what-if scenario analysis. To facilitate this process, this paper proposes KubeTwin, a framework to enable the definition and evaluation of DTs of Kubernetes applications. Specifically, this work presents an innovative simulation-based inference approach to define accurate DT models for a Kubernetes environment. We experimentally validate the proposed solution by implementing a DT model of an image recognition application that we tested under different conditions to verify the accuracy of the DT model. The soundness of these results demonstrates the validity of the KubeTwin approach and calls for further investigation.

Effective communication and information sharing among different districts and cities are crucial for the management of utility flows, traffic, and emergencies in smart cities. In this scenario, a smart city requires cloud-native solutions to collect and analyze data from various sources, including traffic sensors and public transport vehicles. Thus, a multicloud observability approach is proposed to aggregate data from different localities. The solution aims to provide a complete suite for observability capable of collecting data across layers of a multicloud and integrating already existing open-source projects.

Edge computing aims at better supporting low-latency applications. One of its key techniques is computation offloading, the process that outsources computing tasks from resourced-constrained mobile devices and moves them to edge data centers. In this paper, we tackle an emerging problem within the umbrella of computation offloading, i.e., migration of offloaded inference tasks of Artificial Intelligence (AI) trained models. Such context tailors migration aspects of data-sensitive services where i) the value of the updates is inversely proportional to the data age and ii) outage is highly detrimental to accuracy. To tackle this challenge, we propose Mobile Edge Data-handoff (MED) a framework able to relocate inference or online training tasks from one edge datacenter to another by moving only the necessary data to minimize any accuracy drop during the process. We implemented MED in a well-known edge computing emulator, openLEON, and experimentally verified its performance with an AI-based Industry 4.0 application that forecasts the gas flow in a chemical plant. For our experiments, we use a real, open-source dataset that contains sensors readings. Collected results show that MED, employing proactive data handoff algorithms, is able to minimize the packet loss during the handoff thereby providing guarantees on the inference accuracy.

The continuous digitalization and application of modern ICT technologies in the Smart City and Tourism domains are paving the way to new, integrated and connected experiences with strong economic and social impact. However, the integration of services and data coming from different providers is hindered by a rapidly evolving ecosystem of tools and techniques, introducing substantial delays and costs in solution design, deployment, testing, and refinement. Serverless computing is a novel cloud computing model where the customers' business logic, structured as lightweight functions, is automatically put into execution in response to an incoming event. This emergent paradigm promises to be an appealing solution, lowering the development and management barrier for the adaptation, integration, and roll-out of services. However, the ephemeral nature of serverless functions clashes with the necessity of creating and maintaining persistent connections to the various data providers. In this work, we present the Serverless Persistence Support (SPS) service, a novel, distributed, and scalable service implementing an adaptation layer able to improve data access performance from serverless functions. To validate our proposal, we conduct a thorough analysis on a realistic testbed, contrasting various data layer supports and assessing SPS performance when compared to the classic approach where connections and operations are executed inside the business logic. The experimental analysis shows that our solution exhibits better performance both in terms of latency and throughput, while also improving resource utilization.

Nowadays, Machine learning (ML) plays a significant role in Industrial Analytics. It enables predictive analytics, and helps uncovering essential insights to transform industries. As a result, real-time data analytics has become an essential requirement for industrial engineering jobs. Edge computing enables local intelligence and real-time analytics that are key for industry processes to take autonomous decisions locally at the edge of the network. However, outages in edge datacenters can jeopardize the whole plant security. In this paper, we proposed a practical approach to effectively handling service and data migration of ML-based applications in Industrial Analytics scenarios in the presence of a lack of computing resources at the edge. We argue that in this context the value of data is inversely proportional to their age and is very important to work with fresher data. In this paper, we describe our architectural approach for service and data handoff and show a predictive diagnostics case study deployed in an edge-enabled IIoT infrastructure. We evaluate our proposed approach in terms of drop of accuracy in a well-known edge computing emulator, i.e., openLEON. The experimental results show the benefit of our solution with respect to standard techniques.

The pervasive presence of the Internet results in an increasing number of people and systems interacting with each other. However, since each system only captures its own representation of reality, the information of the involved actors is scattered and even duplicated. This scattering generates problems by itself since only a small fragmented vision of the actors’ reality is available for each system. At the same time, the duplication of information in different systems can lead to inconsistencies that impede the correct integration of different systems. In this paper, we present an architecture based on the use of an upper blockchain in charge of federating a set of lower-level blockchains. This way, actor-centered data integration is achieved, offering a single, global vision of the dispersed information of each actor. This solution, called FedBlocks, relies on the blockchains’ federation concept where all interconnected systems are communicated and can share authorized data of their actors, providing a benefit for these systems and for the actors themselves. The proposed implementation has been successfully tested integrating 5689 records from 50 different institutions, belonging to 1156 actors.

The recent trend of moving cloud computing capabilities to the edge of the network is reshaping the way applications and their middleware supports are designed, deployed, and operated. This new model envisions a continuum of virtual resources between the traditional cloud and the network edge, which is potentially more suitable to meet the heterogeneous Quality of Service (QoS) requirements of the supported application domains. Yet, mission-critical applications such as those in manufacturing, automation, or automotive, still rely on communication standards like the Time-Sensitive Networking (TSN) protocol and 5G to ensure a deterministic network behavior: in this context, virtualization might introduce unacceptable network perturbations. In this paper, we demonstrate that latency-sensitive applications can execute in virtual machines without disruptions to their network operations. We propose a novel approach to support the TSN protocol in virtual machines through a precise clock synchronization method and we implement it in integration with state-of-the-art and highly-efficient network virtualization techniques. Our experimental results show that it is possible to achieve deterministic and ultra-low latency end-to-end communication in the cloud continuum, for example providing a guaranteed sub-millisecond latency between remote virtual machines.

In smart city advanced analytical scenarios, tremendous amounts of georeferenced big time series data arrive continuously to time series databases, requiring the shared analytics on both geospatial and time dimensions. Mostly, the focus has been given to optimizing the storage and processing of each workload alone, either geospatial or time dimensions. To close this gap, in this paper, we have designed a pyramid-like indexing scheme that we term as geoTSI (short for geo time series index) which twists two dimensionality reduction geospatial encoding methods (geohash and S2) sequentially with a time series index to efficiently enable such mixed workload scenarios. This method enables geospatial and time indexes to collaborate synergistically in an aim to reduce the time required for accessing the disk and retrieving the time series data that comprises the answer for the mixed workload query. We show how our indexing scheme can be efficiently exploited to run a hybrid geospatial proximity query on time series data. Also, we evaluate our index on real-world georeferenced time series data, where we obtain, on average, a significant 34 % reduction in the query running time by applying our method against the baseline.

The increasing pace of sensing and communication technology rollout is paving the way for concrete deployments of smart city applications, enabling a data-driven modeling of processes and the environment. In particular, the Urban Facility Management (UFM) process is growing in importance, recognized to have a direct impact on the sustainability and the development of our cities. In [1] we presented a system's view of a Digital Twin solution for the UFM process. The solution relies on (near)real-time data to quantify the activity index in an area of interest, used as a basis for planning decisions. In this study, we focus on the predictive subsystem, tasked with computing near-to-mid term predictions of the activity index, equipping UFM operators with a flexible decision-support system. Without loss of generality, we present an analysis of the vehicular traffic component, part of the activity index, assessing the accuracy of different predictive schemes, discussing some operational implications.

In Cross-device Federated Learning, communication efficiency is of paramount importance. Sparse Ternary Compression (STC) is one of the most effective techniques for considerably reducing the per-round communication cost of Federated Learning (FL) without significantly degrading the accuracy of the global model, by using ternary quantization in series to topksparsification. In this paper, we propose an original variant of STC that is specifically designed and implemented for convolutional layers. Our variant is originally based on the experimental evidence that a pattern exists in the distribution of client updates, namely, the difference between the received global model and the locally trained model. In particular, we have experimentally found that the largest (in absolute value) updates for convolutional layers tend to form clusters in a kernel-wise fashion. Therefore, our primary novel idea is to a-priori restrict the elements of STC updates to lay on such a structured pattern, thus allowing us to further reduce the STC communication cost. We have designed, implemented, and evaluated our novel technique, called Structured Sparse Ternary Compression (SSTC). Reported experimental results show that SSTC shrinks compressed updates by a factor of x3 with respect to traditional STC and with a reduction up to x104 with respect to uncompressed FedAvg, at the expense of negligible degradation of the global model accuracy.

The advent of the Internet of Things (IoT) paradigm to intensive domains, such as industry, is a key enabler for the automation of critical, real-world processes. The strict Quality-of-Service (QoS) requirements of these domains make low-latency computing paradigms, such as fog computing, very attractive for meeting these requirements. Moreover, the requirements of scalability and flexibility in the underlying network communications motivate the use of software-defined networking (SDN) in the infrastructure. To enable these fog-SDN environments, fog nodes (FNs) that have both computing and SDN capabilities can be deployed, thus easing the deployment of fog in SDN networks. However, the exact placement of these FNs is key to the latency of the hosts that make use of them, and thus, must be carefully assessed to meet the stringent QoS requirements of critical, time-strict IoT applications. This article focuses on this FN placement problem by formalizing it and solving it through both optimal and approximated methods, including comparisons with state-of-the-art benchmarks. In particular, we analyze the performance of each of these methods in terms of latency and execution time in both SDN Internet topologies and Industrial IoT infrastructures. Our proposed heuristic provides placements with near-optimal latencies, with smaller optimality gaps than the benchmark, and computes them in tractable times.

The maturity of heterogeneous and hybrid public Cloud environments enables service providers to deploy there their complex IT services trusting these large and complex infrastructures. At the same time, evaluating the impact of changes at service configuration before and at the runtime is still a very challenging and difficult task. Moreover, a comprehensive performance evaluation of IT service configurations should not be limited just to costs for IT resource acquisition, but also include risk related elements such as Service Level Agreement (SLA) violation penalties and other intangibles. To support IT service providers in this difficult task, we developed Business-Driven Management as a Service Plus (BDMaaS+), a novel decision support tool that can evaluate IT service configuration through simulation with realistic service and network models. By allowing service providers to define expanded operational parameters, BDMaaS+ also enables what-if scenario analysis, thereby opening interesting possibilities at the planning level. Experimental results, collected from our thorough evaluations, demonstrate how a service provider can leverage BDMaaS+ to explore the potential of high-level business SLA changes and data center additions.

Federated Dropout has emerged as an elegant solution to conjugate communication-efficiency and computation-reduction on Federated Learning (FL) clients. We claim that Federated Dropout can also efficiently cope with device heterogeneity by exploiting a server that broadcasts custom and differently-sized sub-models, selected from a discrete set of possible sub-models, to match the computation capability constraints of FL clients. In addition, we further reduce the up-link communication cost by applying per-layer or traditional Sparse Ternary Compression (STC) to sub-model updates. We demonstrate the effectiveness of our solution by reporting results for a well-known CNN used for classification tasks considering the Federated EMNIST dataset.

The application of Internet of Things (IoT)-based solutions to intensive domains has enabled the automation of real-world processes. The critical nature of these domains requires for very high Quality of Service (QoS) to work properly. These applications often use computing paradigms such as fog computing and software architectures such as the Microservices Architecture (MSA). Moreover, the need for transparent service discovery in MSAs, combined with the need for network scalability and flexibility, motivates the use of Software-Defined Networking (SDN) in these infrastructures. However, optimizing QoS in these scenarios implies an optimal deployment of microservices, fog nodes, and SDN controllers. Moreover, the deployment of each of the different elements affects the optimality of the others, which calls for a joint solution. In this paper, we motivate the joining of these three optimization problems into a single effort and we present Umizatou, a holistic deployment optimization solution that makes use of Mixed Integer Linear Programming. Finally, we evaluate Umizatou over a healthcare case study, showing its scalability in topologies of different sizes.

The Multi-access Edge Computing (MEC) enables a new layer of edge middleboxes, acting as local proxies with virtualized resources deployed at edge localities. To support scalable, low-latency, and locally managed service provisioning, MEC relies on computation offloading, the process that outsources computing tasks from resourced constrained mobile devices and moves it to edge data centers. In this paper, we tackle a specific sub-problem within the umbrella of computation offloading. We argue that it is convenient to migrate a service because of the lack of computing resources in the anchor edge data center even if a device, such as industrial IoT devices, is not moving. In this paper, we extensively evaluate the efficiency of data and service handoff protocols. Specifically, we thoroughly assess protocols, that we designed in our past work, in a well-known edge computing emulator, i.e., openLEON. These protocols migrate data and service either in a reactive fashion, i.e., upon realizing of resource exhaustion, or proactively, i.e., beforehand to swiftly minimize the downtime. We experimentally verify their performance for a typical MEC use case, i.e., video. Our results show that by being proactive, the service interruption downtime reduces by a factor of 4 times.

In the last recent years Internet of Things has extended its adoption to the industrial manufacturing field. The digitalization of industry brings the new concept of Industry 4.0 to light. In fact, the enormous value for the companies generated from the adoption of Industrial IoT and Edge Computing has led to a vertiginous increments of IIoT platform demands. Therefore, world-wide big IT players and Foundations start to introduce their own flagship IIoT platforms into market. This multitude of platforms presents similar common features, but with different APIs. These platforms are hardly interoperable, and they are frequently bounded to their own vertical solution stack. To overcome these limitations, in this paper, we propose MIINT, a Middleware for IIoT platforms INTegration. MIINT groups the common functionalities of IIoT frameworks, and it integrates different platforms by providing standard access APIs. To prove the feasibility of MIINT, in this paper, we show an integration use case of Azure IoT and EdgeX Foundry IIoT platforms. Moreover, we also thoroughly assess MIINT by executing on the edge a field data reading functionality in the two considered IIoT platforms, by showing their advantages and limitations.

Industry 4.0 environments pose unique challenges for the realization of the communication substrate at the shop floor, due to the strict Quality of Service (QoS) requirements, the high heterogeneity of the employed data exchange protocols, and the different network technologies and addressing schema toward the machines. To address those issues, the paper proposes a distributed support based on a Message Oriented Middleware (MOM) and a Software Defined Network (SDN) control plane that coordinate to enable semantic routing by also allowing traffic differentiation as well as in-network processing at intermediate network nodes. Seminal results, collected in realistic industrial settings, confirm the feasibility of our proposal.

Industry 4.0 aims to revolutionize and digitize the manufacturing sector by enabling and facilitating interoperability, solution agility, flexible (re)configuration of production chain(s) while, at the same time, reducing costs by exploiting real time data. These capabilities require to link the plant floor with data flows from/to the enterprise borders and include as core enabling technologies the Internet of Things (IoT), cloud, and edge computing key to move and execute parts of the business logic. The new capabilities might be leveraged in an innovative way, especially in the plant floor to dynamically change the monitoring/control logic of the smart machinery. In our reference scenario, the data flows originating from the plant floor can be processed and filtered locally, creating the basis for a selective Quality of Service (QoS) mechanism allowing for the implementation of reactive services, such as predictive maintenance. To that end, we propose an innovative serverless edge processing solution used for monitoring geo-distributed industrial plants. The proposal is validated in realistic settings, under different operational regimes, exhibiting acceptable performance trends under realistic periodic and variable traffic scenarios.

Leveraging the Internet of Things (IoT) in intensive domains, such as in the Industrial Internet of Things (IIoT) or Internet of Medical Things (IoMT), provides automation and sensing solutions for complex environments through the interconnection of different sensors and actuators. However, these scenarios usually demand to meet stringent Quality of Service (QoS) requirements to work properly. Fog computing, a paradigm that brings computation and storage closer to the edge, and Software-Defined Networking (SDN), a networking paradigm that enables for network scalability and flexibility, can be combined. To do so, fog nodes that integrate both, computation resources and SDN capabilities, are leveraged to meet these stringent needs. Clearly, the placement of such fog nodes plays a key role in the achieved QoS. In this paper, an optimal fog node placement formulation is evaluated in an emulated fog and SDN environment. Results show that an optimal fog node placement can achieve a reduction of up to 59% in the network latency with a minimal jitter compared with other well-known placement methods.

The problem of information overloading is prevalent in recommendations websites and social networks. Users seek relevant recommendations from like-minded connections. User-item interactions (i.e., ratings) are prevalent in recommendation websites such as Netflix, whereas user-user connections are the interaction sought in social websites such as Twitter. Social recommender systems seek to generate recommendations for users based on similar preferences of their close friends. Because social networks do not normally contain user-item interactions, social recommender systems are typically hybridized with other recommenders (e.g., website recommenders such as Netflix) that provide such interaction. However, current systems are unaware of the user’s additional contextual information when coupled with social counterparts. In this paper, we propose a context-aware deep learning-based recommender system, US-NCF, in support for social recommender systems. Our experiments show US-NCF outperforms state-of-art counterparts.

The current trend of smart environments is leading towards a world where everything is considered as a service. Internet-connected smart devices make these environments largely manageable and controllable through services. In these environments, not only devices offer services, but lately, people through their smartphones can also offer services such as personal information provided by the name, the preferences, or the location, promoting the offer of almost anything as a service. However, this high supply of services makes it more difficult for IoT systems to identify which services to use to solve a particular need. This paper proposes a solution to characterize services homogeneously and a service selection mechanism is outlined considering the properties of the services and the context in which they are found. With this proposal, services are defined commonly to facilitate a smart selection by IoT applications.

Serverless platforms are increasingly gaining importance in the cloud computing landscape due to their benefit of shortening the time to market of solutions and capability of automatic, fast, fine-grained scaling of resources. In this context, function chaining represents an appealing feature, allowing the composition of two or more functions to create a complex computation from simpler units while incentivizing modularity and reusability of functions. In this paper, we propose a portable and transparent, container-based serverless architecture that introduces an innovative infrastructural support, enabling an efficient composition of functions co-located on the same host. The proposal relies on a shared-memory approach and a message-oriented middleware serving as a communication medium among components. The experimental assessment shows the approach comes with the benefit of optimized resource usage and performance benefits measured in terms of request completion rate and a decrease in response latency.

Smart cities use Information and Communication Technologies (ICT) to enrich existing public services and to improve citizens' quality of life. In this scenario, Mobile CrowdSensing (MCS) has become, in the last few years, one of the most prominent paradigms for urban sensing. MCS allow people roaming around with their smart devices to collectively sense, gather, and share data, thus leveraging the possibility to capture the pulse of the city. That can be very helpful in emergency scenarios, such as the COVID-19 pandemic, that require to track the movement of a high number of people to avoid risky situations, such as the formation of crowds. In fact, using mobility traces gathered via MCS, it is possible to detect crowded places and suggest people safer routes/places. In this work, we propose an edge-anabled mobile crowdsensing platform, called ParticipAct, that exploits edge nodes to compute possible dangerous crowd situations and a federated blockchain network to store reward states. Edge nodes are aware of all critical situation in their range and can warn the smartphone client with a smart push notification service that avoids firing too many messages by adapting the warning frequency according to the transport and the specific subarea in which clients are located.

Current cloud-enabled NoSQL database frameworks support flexible and scalable storage of huge amounts of data arriving through various and often heterogeneous channels. However, they do not natively provide optimised processing of spatial data, thus making it more difficult to perform accurate data analytics needed in many smart city application scenarios. To improve the performance of spatial data computation in the NoSQL MongoDB storage framework, this article proposes a novel data partitioning method based on dimensionality reduction. The underlying key idea is to reduce a spatial data representation from multi to single dimensionality, by still maintaining its geometrical meaning and by employing a specific geo-encoding scheme, i.e., a geohash string. In particular, the geohash string is used as a sharding key in order to store geometrically-nearby objects into the same chunks (and consequently into the same shard). In addition, as a distinctive feature, we have extended the MongoDB framework with a custom spatial QoS-aware optimizer that exploits our novel partitioning scheme to support two, typically expensive, types of spatial queries with QoS guarantees. Those queries are containment (and consequently top-N) and proximity. The paper also contributes to the existing literature with extensive experimental results about the performance of both our partitioning method and query optimizer; the reported results show that our solutions outperform baselines by orders of magnitude.

Several relevant research and innovation activities have recently investigated the technical and economic advantages of cloud computing for the provisioning of telco service infrastructures, in particular towards all-IP next generation 5G networks. In fact, the evolution of telco service infrastructures traditionally requires a significant upfront investment (and a long adoption process). Conversely, cloud exploitation significantly lowers investment risks by potentially providing elasticity in service provisioning via flexible Virtual Network Functions (VNFs) on top of a Network Functions Virtualization (NFV) Infrastructure. In this context, the paper presents novel solutions that we have designed, implemented, and evaluated within the EU FP7 Mobile Cloud Networking project (MCN). Their aim is to achieve cost-effective elastic provisioning of telco services over heterogeneous and federated cloud providers, with the specific focus of supporting the extreme quality levels that are demanded by traditional, non-virtualized, and dedicated telco infrastructures. In particular, we concentrate on how to effectively and efficiently automate service state migration for coarse-grained telco service (cloudified) components by leveraging industry-mature orchestration technologies and cloud management frameworks. While our proposed state migration model and procedure are general, its implementation is experimented for MCN's Rating, Charging, and Billing as a Service (RCBaaS). This MCN functionality has been chosen by purpose due to its challenging reliability and uptime requirements. The reported experimental and simulation results show the technical feasibility of the proposed solution under different and realistic load conditions for next-generation and cloudified 5G services.

The combination of the edge computing paradigm with Mobile CrowdSensing (MCS) is a promising approach. However, the selection of the proper edge nodes is a crucial aspect that greatly affects the performance of the extended architecture. This work studies the performance of an edge-based MCS architecture with ParticipAct, a real-word experimental dataset. We present a community-based edge selection strategy and we measure two key-metrics, namely latency and the number of requests satisfied. We show how they vary by adopting three evolutionary community detection algorithms, TILES, Infomap and iLCD configured by changing several configuration settings. We also study the two metrics, by varying the number of edge nodes selected so that to show its benefit.

The Mobile CrowdSensing (MCS) paradigm has been increasingly adopted in the last years. Its adoption has been proved as beneficial for different scenarios, such as environmental monitoring and mobility analysis. However, one of the major barriers of the MCS initiatives, is the difficulty in recruiting users for the purpose of collecting data. We focus in this work to such limitation, and we analyze the mobility traces collected with a real-world MCS experiment, namely ParticipAct. Our goal is to discuss how to exploit the mobility features of the recruited users, as grounding information to plan and optimize a MCS data collection campaign. In detail, we analyze the quality of the data set, its accuracy and several features of human mobility such as radius of gyration and the real entropy of the locations visited. We discuss the impact of such metrics on the task scheduling, allocation and how to obtain a certain Tcoverage of data from visited locations.

The Industrial Internet of Things (IIoT) provides automation solutions for industrial processes through the interconnection of different sensors, actuators and robotic devices to the Internet, enabling for the automation of manufacturing processes through Factory Automation. However, IIoT processes are often critical, and require very high Quality of Service (QoS) to work properly, as well as network scalability and flexibility. Fog computing, a paradigm that brings computation and storage devices closer to the edge of the network to enhance QoS, as well as Software-Defined Networking (SDN), which enables for network scalability and flexibility, can be integrated into IIoT architectures in the form of fog nodes that integrate both, computation resources and SDN capabilities, to meet these needs. However, the QoS of the IIoT system depends on the placement of these fog nodes, creating a need to obtain placements that optimize QoS in order to meet the requirements by minimizing the latency between the fog nodes and the IIoT devices that consume their services. In this paper, this fog node placement problem is formalized and solved by means of Mixed Integer Programming. We also show relevant experimental results of our formulation and analyze its performance.

The easily reachable IoT edge devices have caused the accumulation of vast amounts of geo-referenced data traces that can help in performing deep insightful analytics. Geospatial data in real geometries are normally clumped into batches and has strong autocorrelation properties which can be exploited in discovering interesting insights. Current plain Cloud computing frameworks are not attuned to the shape of data. Most importantly, data splitting is an important precursor in data parallelization mechanisms. Current systems mostly focus on general data workloads, thus are giving attention mostly to load balancing while splitting the data to Cloud computing resources. However, many benefits can be reaped by being attuned to the spatial characteristics while distributing the data, thus striking a plausible balance between load balancing and spatial data locality preservation normally leads to achieving better time-based QoS goals, which then leads to an optimized provisioning of Cloud computing resources. In this paper, we have designed a spatial batch processing engine that comprises a custom spatial data locality aware partitioning method for disseminating spatial data loads in Cloud computing clusters. We have also extended a state-of-art benchmark density-based clustering method that is known as DBSCAN-MR and implemented a standard compliant prototype on top of a best-in-breed de facto Cloud-based main memory processing framework, Apache Spark. Our results show that our partitioning method with the associated spatial query optimizers can achieve gains that significantly outperform baselines.

Cloud computing has transformed the way IT services are provisioned and consumed, shifting the burden of configuration and management to the cloud provider. A lot of research has been conducted in the field aimed at optimizing and/or devise new in-cloud service delivery models. Yet, service outages are a norm rather than an exception. A natural evolution for application developers, especially for those applications that must meet high availability requirements, is to rely on resources and services provisioned by multiple cloud simultaneously. The so-called multi-cloud, distributed deployment model coupled with a monitoring solution spanning multiple domains could help in a timely identification and isolation of faulty components, alleviating service impairment issues. To this end, we propose an extension to NoMISHAP, a Platform as a Service (PaaS) multicloud middleware, introducing a cross layer monitoring solution for use in distributed cloud environments. Experimental results show the effectiveness of our approach and its ease of adoption for management tasks.

Edge Computing is becoming more and more essential for the Industrial Internet of Things (IIoT) for data acquisition from shop floors. The shifting from central (cloud) to distributed (edge nodes) approaches will enhance the capabilities of handling real-time big data from IoT. Furthermore, these paradigms allow moving storage and network resources at the edge of the network closer to IoT devices, thus ensuring low latency, high bandwidth, and location-based awareness. This research aims at developing a reference architecture for data collecting, smart processing, and manufacturing control system in an IIoT environment. In particular, our architecture supports data analytics and Artificial Intelligence (AI) techniques, in particular decentralized and distributed hybrid twins, at the edge of the network. In addition, we claim the possibility to have distributed Machine Learning (ML) by enabling edge devices to learn local ML models and to store them at the edge. Furthermore, edges have the possibility of improving the global model (stored at the cloud) by sending the reinforced local models (stored in different shop floors) towards the cloud. In this paper, we describe our architectural proposal and show a predictive diagnostics case study deployed in an edge-enabled IIoT infrastructure. Reported experimental results show the potential advantages of using the proposed approach for dynamic model reinforcement by using real-time data from IoT instead of using an offline approach at the cloud infrastructure.

Hyperledger Fabric, created and supported by the Linux Foundation and IBM, is one of the most popular open-source blockchain permissioned platforms that has been already used in many industrial scenarios. One of the main characteristics of this platform is that it provides a smart contract system that relies on general-purpose languages instead of an ad hoc one. In fact, a chaincode in the Fabric platform (the equivalent of the Ethereum smart contract) is a software program which encapsulates the business logic for the creation and modification of logical assets in the ledger that can be written in different general-purpose programming languages (currently Java, Go, and Node.js). This paper analyses the transaction performance of the Fabric platform by identifying at a fine-grained degree level the factors that most contribute to the overall overhead. In particular, we focus on how the transaction latency is affected by the programming language adopted for implementing the chaincode and by varying the number of participating endorser peers. Finally, the paper shows a thorough test assessment aimed at evaluating the impact of the different chaincode implementation on performance overhead. As it emerges from our experimental results, Go is the most performing programming language.

The technological progress is leading to an increase of instrument sensitivity in the field of rotational spectroscopy. A direct consequence of such a progress is an increasing amount of data produced by instruments, for which the currently available analysis software is becoming limited and inadequate. In order to improve data analysis performance, parallel computing techniques and distributed computing technologies like Cloud and High Performance Computing (HPC) can be exploited. Despite the availability of computer resources, neither Cloud nor HPC have been fully investigated for identifying unknown target spectra in rotational spectrum. This paper proposes the design and implementation of a Highly Scalable AUTOFIT (HS-AUTOFIT), an enhanced version of a fitting tool for broadband rotational spectra that is capable of exploiting the resources offered by multiple computing nodes. Compared to the old program version, the new one is capable of scaling on multiple computing nodes, thus guaranteeing higher accuracy of the fit function and consistent boost of execution time. The result of tests conducted in real Cloud and HPC environments show that HS-AUTOFIT is a viable solution for the analysis of huge amount of data in the addressed scientific field.

Nowadays, cloud computing has become a key paradigm in distributed applications thanks to the rise of low-power Internet-connected devices as commonplace. However, stringent Quality of Service (QoS) requirements are complicated to achieve when a pure cloud computing paradigm is applied, due to the physical distance between end devices and cloud servers. This motivated the appearance of fog computing, a paradigm that adds computation and storage resources, named fog nodes, closer to the end devices in order to reduce response time and latency. However, the placement of fog nodes, as well as the relative placement of the end devices each fog node serves, can affect the QoS obtained. This can be crucial to those services that have stringent QoS requirements. In this work, we analyze the effects that different placements of fog nodes have on QoS and present the problem of placing fog nodes to obtain an optimal QoS, with a focus on the Industrial Internet of Things domain because of its strict QoS requirements. We conclude that an optimized placement of the fog nodes can minimize latency to support the QoS requirements of IIoT applications.

Industry 4.0 outlines the trend of the massively adoption of Internet of Things (IoT) nodes in supply chains, manufacturing, and factories in general. The industry digitalization is the key enabler to ease the productive process, drastically reduce its costs, and boost up the associated business. In this context, Arrowhead Tools (AHT) is a H2020 EU project provided by ECSEL that targets automation and digitalization solutions for the industry in Europe. AT is based on a framework, named Arrowhead Framework (AHF), developed and provided by the previous Arrowhead (AH) project. AHF is open source and addresses IoT-based automation and integration by abstracting IoT objects to services. AHF enables IoT interoperability and provides real time data handling, security features, automation system engineering, and automation systems scalability. In this paper, after a rapid overview of the AT project and the AHF architecture, we originally introduce the concept of Tool and Tool Chain for Industry 4.0 in AH. We also present a vertical AHT use case along with its implementation, as well as all the steps to turn a service/application into an AH-compliant Tool.

Human-enabled edge computing (HEC) is a recent smart city technology designed to combine the advantages of massive mobile crowdsensing (MCS) techniques with the potential of multiaccess edge computing (MEC). In this context, the architectural hierarchy of the network shifts the management of sensing information close to terminal nodes through the use of intermediate entities (edges) bridging the direct Cloud-Device communication channel. Recent proposals suggest the implementation of those edges, not only employing fixed MEC nodes, but also opportunistically using as edge nodes mobile devices selected among the terminal ones. However, inappropriate selection techniques may lead to an overestimation or an underestimation of the number of nodes to be used in such a layer. In this article, we propose a probabilistic model for the estimation of the number of mobile nodes to be selected as substitutes of fixed ones. The effectiveness of our model is verified with tests performed on real-world mobility traces.

Elastic resource outsourcing is a growing trend that simplifies and makes more efficient the management of resources, by embracing all the features of the execution of services over public clouds, such as high availability and automated scalability management of resources. Therefore, modern enterprise services are increasingly leveraging inter/intra-cloud deployments and the choice of the right cloud provider to support the execution of them becomes a fundamental operational choice. The paper presents our Audit4Cloud platform, an open-source tool for auditing the performance of virtual resources made available by various commercial cloud providers, with specific focus on cloud networking. In particular, we claim that Audit4Cloud is an enabling key in choosing the right cloud vendor as it not only offers the visibility of current values of some significant performance indicators about the offered cloud resources, but also provides users with a complete picture of those performance indicators over time. We have already performed a large experimental campaign by considering primary commercial cloud providers; the collected results show the feasibility of the approach and that Audit4Cloud can play the role of a solid third-party auditing tool to estimate real performance and costs of cloud resources.

The widespread adoption of ubiquitous IoT edge devices and modern telemetry spewing out unprecedented avalanches of spatially-tagged datasets that if could interactively be explored would offer deep insights into interesting natural phenomena, which might remain otherwise illusive. Online application of spatial queries is expensive, a problem that is further inflated by the fact that we, more than often, do not have access to a full dataset population in non- stationary settings. As a way of coping up, sampling stands out as a natural solution for approximating estimators such as averages and totals of some interesting correlated parameters. In any sampling design, representativeness remains the main issue upon which a method is regarded good or bad. In a loose way, in a spatial context, this means fairly sampling quantities in a way that preserves spatial characteristics so as to provide more accurate approximates for spatial query responses. Current big data management systems either do not offer over-the-counter spatial-aware online sampling solutions or, at best, rely on randomness, which causes too many imponderables for an overall estimation. We herein have designed a QoS- spatial-aware online sampling method that outperforms vanilla baselines by statically significant magnitudes. Our method sits atop Apache Spark Structured Streaming's codebase and have been tested against a benchmark that is consisting of millions-records of spatially- augmented dataset.

In the last decade, novel software architectural patterns, such as microservices, have emerged to improve application modularity and to streamline their development, testing, scaling, and component replacement. To support these new trends, new practices as DevOps methodologies and tools, promoting better cooperation between software development and operations teams, have emerged to support automation and monitoring throughout the whole software construction lifecycle. That affected positively several IT companies, but also helped the transition to the softwarization of complex telco infrastructures in the last years. Container-based technologies played a crucial role by enabling microservice fast deployment and their scalability at low overhead; however, modern container-based applications may easily consist of hundreds of microservices services with complex interdependencies and call for advanced orchestration capabilities. While there are several emerging container orchestration engines, such as Docker Swarm, Kubernetes, Apache Mesos, and Cattle, a thorough functional and performance assessment to help IT managers in the selection of the most appropriate orchestration solution is still missing. This paper aims to fill that gap. Collected experimental results show that Kubernetes outperforms its counterparts for very complex application deployments, while other engines can be a better choice for simpler deployments.

This work proposes a novel mechanism for management, orchestration and flow control in the context of the device-to-device (D2D) to deal with load balancing using the deep Q-learning (DQN) technique. To do so, we implemented a D2D network simulation environment, using the ParticiptAct dataset to evaluate the load of the cell towers in a region of Italy. The Gauss-Markov and Gilbert-Elliott models were used for mobility and packet loss, respectively, where it was considered that the towers had a disconnected coverage area, hence forming a Voronoi space. We used a Gaussian process to predict the load of the towers when they receive the packet, and a DQN to perform the balance of load of the network. This proposal presents better results than the baseline, concerning the metrics used, as well as presenting some perspectives for a future unfolding of this work.

The ever-increasing diffusion rate of mobile devices, able to continuously gather sensing data, creates favorable conditions for the development of smart city infrastructures. In this field the analysis of spatial data plays a pivotal role, due to the relevance they assume in urban scenarios. To satisfy this need, the usage of large distributed computing infrastructures comes into play, supported by efficient frameworks, such as Apache Spark, one of the most relevant platforms to date. However, in order to better take advantage of data and computing resources, it is also necessary to have at disposal flexible and easy-to-use specialized instruments, granting domain specific capabilities for the analysis of spatial data. This paper focuses on a novel framework for processing of spatial data called STARK, giving an overview of its functionalities and presenting an in-depth assessment study of its performances when implementing spatial data clustering, namely DBSCAN. In particular, we focus on two implementations, called MR-DBSCAN and NG-DBSCAN. Of the latter we introduced an implementation in STARK, in order to enrich the framework and to test its capabilities.

Nowadays, the massive usage of mobile and IoT applications generate large amounts of data. Due to several reasons, including latency and bandwidth, it is not practical to send all generated data to the cloud. Recent standardization efforts, namely, Fog computing and the Multi-access Edge Computing (MEC), provide an extension of Cloud computing storage and network resources placed in a geographically distributed manner at the edge of the network closer to mobiles and IoT devices. These paradigms allow low latency, high bandwidth, and location-based awareness. In this paper, we present an infrastructure to support distributed Machine Learning (ML) by enabling edge devices to collaboratively learn a shared model while keeping local knowledge stored at the edge of the network. In addition, we claim the possibility of improving the model through the cloud that acts as a supervisor of the system that contains the global knowledge of the entire system through the integration of local edge models. We describe our architectural proposal and analyze a case study, namely video streaming processing for face recognition, deployed in a collaborative edge network. Finally, we report experimental results that show the potential advantages of using our approach instead of ML algorithms completely expected at the cloud infrastructure.

Distributed architectures where the Internet of Things (IoT) and the cloud are efficiently integrated play an increasingly important role for IoT solutions. Among these architectures, there is a growing interest in the ones that support the opportunity of functionality offloading towards either intermediate fog nodes or IoT end devices. Relevant existing research work has mainly focused so far on virtual machine and container migration to intermediate fog nodes and on migration of very simple functions to IoT endpoints (to preserve their limited resources available). In this paper, we originally concentrate on the gap associated with benefitting from fog functionality at resource-powerful IoT endpoints, to create a continuum deployment that glues IoT devices and the cloud. In particular, this paper originally presents a middleware that manages application deployment and life-cycle by simplifying and optimizing management operations such as device configuration and application constraint satisfaction. The proposed solution particularly fits highly articulated scenarios with large numbers of IoT devices and intermediate fog nodes, by supporting the opportunity to offload functionality in a split way between IoT endpoints and edge nodes. The reported experimental results confirm the feasibility of our approach in term of overhead, scalability, and application life-cycle management.

Mobile crowdsensing aims at the collection of sensor data on the environment by leveraging personal devices, usually smartphones. Its popularity is due to the ability of reaching capillary even the most remote areas (provided humans live there), with no infrastructure costs. This is possible because it leverages on existing 4G/5G communication infrastructures that are now rapidly evolving towards edge computing models. In this work we address the synergy between mobile crowdsensing and multi-access edge computing by analysing and assessing strategies for the selection of fixed and mobile edges to support the collection of mobile crowdsensing data.

Mobile Crowd-Sensing and Fog Computing are fundamental Internet of Things technologies tailored for smart cities. The former enables user's devices to collect and share data in urban environments. The latter shifts the computation close to end users, lightening the work that their devices have to perform to communicate sensed data in the Cloud. In a fog-based MCS campaign a large number of devices with heterogeneous resources executes sensing tasks generally distributed by remote servers. A careful selection of some of these users' devices for sensing operations can bring benefits to the whole platform in terms of computational costs and energy saving. In this paper, we propose a novel users' recruitment model based on distance, computational capacity, and residual battery of devices. The selection process is carried out in a scenario where devices of the MCS campaign periodically share their battery and Central Processing Unit status to fog nodes through their short-range communication interfaces. Based on this information, fog nodes select devices suitable for performing specific tasks. To verify the effectiveness of the proposed model, we compare our solution with a selection model based only on distances, using an MCS simulator suitably modified for fog-based scenarios as testbed. Results show that our model is able to achieve a more accurate task resolution and a more effective recruitment selection, detecting those devices that can perform sensing operations better than others, thus, guaranteeing an overall average saving of computational and energy resources.

The majority of cloud computing deployment environments follow the typical service delivery models, i.e., Software as a Service (SaaS), Platform as a Service (PaaS) and the Infrastructure as a Service (IaaS), that provide specific functionalities to users depending on the service delivery layer and clear resource isolation among different tenants. However, in certain scenarios, such as the Cultural heritage one, it is often required to provide SaaS, IaaS and PaaS cross-functionality support to users and cross-tenant resource visibility among isolated tenants. In the CH scenario, for example, restorers and professionals may often need to connect and to share temporarily data among different tenants or to work with old customized software requiring the possibility to exploit specific management functionalities that are typically available not at the SaaS but at the IaaS or PaaS layers. This paper proposes a middleware called Registry that can provide additional functionalities to different user categories by joining the SaaS with the IaaS layer and that offers also multi-tenancy operations making private data available to certain project on-demand and enabling, in this way, cross-tenant data sharing. The Registry has been designed, developed and tested within the context of the SACHER project that provides a cloud-based infrastructure for the management of the cultural data lifecycle.

The Internet of Things (IoT) is about connecting dynamically billion of devices to the Internet. This large-scale and dynamic topology is very challenging for IoT deployment and management. Software-Defined Networking (SDN) has been applied more and more in recent years as a solution for IoT challenges. The SDN concept of decoupling the control plane from the data plane promotes logically centralized visibility of the entire network and enables the applications to innovate through network programmability. At the same time, there are still some open issues, such as scalability in large IoT environments that include several devices. To face scalability challenges, SDN proposes distributed controllers as a solution to decentralize the control plane while maintaining the logically centralized network view. However, SDN-based architecture, that provides the flexibility and scalability, still lacks the smart or intelligent management to self-adapt to possible dynamic network topology changes. To over-come such issues, we propose a framework that answers automatically the business demands and makes the network self-adaptive. The topology deployment decision is made based on information that the controller gives. So for making sure that our proposed framework gives the best results, we have to study first the topology discovery mechanism in a distributed controller. In this paper, we introduce a self-adaptive management framework of SDN controllers for highly dynamic IoT networks. We evaluate performances of the two most popular distributed SDN controllers (i.e. ONOS and ODL) in a realistic scenario where the network topology changes dynamically. Results show the outperforming of ONOS compared to ODL in discovering the highly dynamic IoT network.

Smart factory management is going through a remarkable change, in terms of quality and diversity of services provided to customers. The companies that produce manufacturing machines now can follow the products throughout the production chain, from the project to the deployment in real scenarios. Industry 4.0 is pushing this trend forward, demanding for servitization of products and machines, mainly for the manufacturing sector where human and production machine are in strict collaboration. The data produced by the machines must be processed quickly to allow the implementation of reactive services such as predictive maintenance and remote control, always taking care of the safety of nearby people. This paper proposes a multilayer architecture to tackle the main issues in monitoring legacy manufacturing machines and to provide general guidelines to solve them. We derived some guidelines from a real Industry 4.0 transition experiment performed together with the company technical departments to accomplish an efficient system for monitoring and servitization of manufacturing machines, with a scalable platform that confirms its usefulness in many production facilities with different needs.

Computation offloading is employed by mobile apps running over resource-constrained devices to leverage the cloud in overcoming their resource limits. The advent of the Multi-access Edge Computing (MEC) paradigm further extends the potential opportunities of mobile-cloud offloading, allowing new service provisioning scenarios, such as mobile gaming and multimedia, where responsiveness of mobile devices at the network edge significantly benefits from low latency interactions. However, state-of-the-art offloading platforms for MEC architectures have not addressed the technical challenge of supporting specific file systems for this MEC-enabled class of applications, with components running at three hosting environments, i.e., mobile, edge, and cloud. This paper proposes the Mobile Edge File System (MEFS), an application-level distributed file system designed to be highly resilient and able to efficiently maintain consistency among the mobile, edge, and cloud entities. MEFS supports application handoff through live migration as end devices move between edges. The cloud transparently helps with recovery from faulty edge nodes or in the case of unavailability of edges in the user's proximity. We implemented a MEFS prototype in Android along with MEFS-based MEC-enabled mobile apps. The experimental results show how MEFS can achieve low latency and low overhead.

Performance evaluation is still a topic that attracts a lot of attention in both distributed and mobile systems as well as web-based services architectures. Due to the recent advances in Internet based applications as well as distributed and mobile communication systems, we are witnessing a variety of new technologies. However, these systems are becoming very large and complex at the same time. Several challenges remain to be resolved before these systems become a commodity. Guaranteeing Quality of Service (QoS) and effective provisioning of web-based systems as well as distributed and mobile systems as well as evaluating their communication performance still represent open issues in the design of these systems. Quantitative analysis can be very difficult and may be intractable because of the state space explosion.

Many IT companies are embracing the new softwarization paradigm through the adoption of new architecture models, such as software-defined network and network function virtualization, primarily to limit the costs of maintaining and deploying their network infrastructures, by giving the possibility to service/application providers to reconfigure and programmatically perform actions on the network. Accordingly, the dynamic management of the data center networks requires complex operations to ensure high availability and continuous reliability in order to guarantee full functionality of the virtualized resources. In this context, simulator-based approaches are helpful for planning and evaluating the deployment of the cloud data center networking, but existing cloud simulators have several limitations: they have too high overhead for wide-scale data center networks, complex configuration, and too abstract deployment models. For these motivations, we propose DCNs-2, a novel extension for the Ns-2 simulator, as a valid solution to efficiently simulate a cloud network infrastructure, with all the involved entities, such as switches, physical/virtual machines, and racks. The proposed solution not only makes configuration easier, but through extensive tests, we show that its execution overhead is limited to less than 130 MB of memory and the execution time is acceptable even for very wide-scale and complex deployment environments.

Nowadays, most users carry high computing power mobile devices where speech recognition is certainly one of the main technologies available in every modern smartphone, although battery draining and application performance (resource shortage) have a big impact on the experienced quality. Shifting applications and services to the cloud may help to improve mobile user satisfaction as demonstrated by several ongoing efforts in the mobile cloud area. However, the quality of speech recognition is still not sufficient in many complex cases to replace the common hand written text, especially when prompt reaction to short-term provisioning requests is required. To address the new scenario, this paper proposes a mobile cloud infrastructure to support the extraction of semantics information from speech recognition in the Social Care domain, where carers have to speak about their patients conditions in order to have reliable notes used afterward to plan the best support. We present not only an architecture proposal, but also a real prototype that we have deployed and thoroughly assessed with different queries, accents, and in presence of load peaks, in our experimental mobile cloud Platform as a Service (PaaS) testbed based on Cloud Foundry.

The widespread adoption of information and communication technologies (ICT) is profoundly changing manufacturing. Several Internet-of-Things (IoT) and industry 4.0 solutions deployed in production environments have pushed for standardization efforts, most notably reference architecture model industrie 4.0 (RAMI 4.0), typically focusing on smart factory environments. However, the ICT evolution is also enabling novel smart appliance scenarios, where relatively cheap machines, connected and integrated, are deployed outside the typical industrial environment with a wide range of stakeholders involved. The paper reports about a real world use case composed of more than 12000 ice cream machines connected worldwide and shows how, anticipating the state of the art, the underlying design of the ICT platform presents many interesting similarities with RAMI 4.0. The integration of appliances in a smart value chain enables to develop novel services for different stakeholders, ranging from ice cream manufacturer and maintenance technicians to ice cream shop owners and final consumers. The important synergies with RAMI 4.0 and the extensive on-the-field validation make the proposed solution a compelling reference application, from which to draw useful and generally applicable guidelines for the development of future Industry 4.0 smart appliance platforms.

Cloud computing nowadays is the cornerstone for all the business applications, mainly because of its high fault tolerance characteristic. High resilience and availability typical of cloud-native applications are achieved using different technologies. Regarding the file system, the main fault tolerant application examples are distributed file systems, such as HDFS, Ceph, GlusterFS, and XtremeFS. These file systems have different architectures and deployment models than the Traditional Distributed File Systems (TDFSs), such as NFS. The primary goal of this work is to analyze and compare different Cloud Distributed File Systems (CDFSs) in terms of characteristics, architecture, reliability, and components. As a key feature, the paper benchmarks them considering as use case an IaaS platform.

A key aspect that Telco operators must carefully consider when deploying Network Function Virtualization (NFV) solutions is the level of performance that cloud computing software platforms can guarantee in support of the offered network services. OpenStack is widely considered as one of the most relevant open-source frameworks that could accelerate the NFV adoption, also because the evolution of its networking components sees a progressive integration of SDN-based solutions that could significantly improve the performance of cloud-based connectivity services. In this paper, we discuss some of the most recent OpenStack innovations that enable a native SDN-like approach to firewalling functions in the data plane, as well as a native SDN-oriented control of the virtual network infrastructure. Then we present a detailed performance analysis of the aforementioned innovations at both the data and control/management plane, showing the potentials of native SDN adoption within OpenStack toward an integrated solution for production-level NFV deployments.

Consolidation of Internet of Things and Fog computing paradigms requires effective and efficient application-layer protocols between service seekers and providers. As most of these nodes run on batteries, discovering service providing devices in an IoT-Fog environment has to be performed in an energy-efficient way. In this paper, we propose Power Efficient Node Discovery (PEND), which is an MQTT-driven IoT-fog integration solution for the sustainability of object discovery in a publish/subscribe environment. By enabling the MQTT broker to serve as a fog node to trigger turning on/off of the Bluetooth interfaces of subscriber objects, Bluetooth Low Energy Scanner (BLE-S), and to monitor the trajectories of publishers/advertisers, Bluetooth low Energy Advertiser (BLE-A), we introduce significant reduction in the Bluetooth, CPU, and process- specific power consumption of the mobile devices. The reduction in the battery drain by the BLE interface under the proposed scheme can be as low as 10%-20% of a naive, locality based discovery benchmark whereas the process specific battery drain of the proposed scheme can be as low as 55%-62% of the node discovery benchmark. Furthermore, with the synchronization of the publishers and subscribers at the fog layer entity, i.e., MQTT broker, 100% node discovery can be achieved by the scanning/service subscriber devices.

In an increasingly interconnected world, mobile and wearable devices, through short range communication interfaces and sensors, become needful tools for collecting and disseminating information in high population density environments. In this context Mobile Crowdsensing (MCS), leveraging people's roaming and their devices' resources, raised the citizen from mere walk-on parts to active participant in the knowledge building and data dissemination process. At the same time, Mobile Edge Computing (MEC) architecture has recently enhanced the two-layer cloud-device architectural model easing the exchange of information and shifting most computational cost from devices towards middle-layer proxies, namely, network edges. We introduce Human-driven Edge Computing, a new model which melts together the power of MEC platform and the large-scale sensing of MCS to realize a better data spreading and environmental coverage in smart cities. In addition, it will be briefly discussed the main sociological aspects related to human behavior and how they can influence the exchange of data in large-scale sensor networks.

Mobile Edge Computing (MEC) opens to the opportunity of moving high-volumes of data from the cloud to locations where the information is actually accessed. In turn, the combination of MEC with the Mobile Crowdsensing approach, using a restricted number of devices with respect the number of base stations, matches the performance of the conventional MEC middleware layer ensuring the same spatial coverage. In this work, we envision a MEC architecture composed by mobile and fixed edges. Their goal is to optimize the share of contents among users by exploiting their mobility and sociality. We first present an algorithm to identify a suitable set of mobile edges and we show how such selection increases the performance of a content-sharing scenario. Our experiments are based on the ParticipAct dataset, which captures the mobility of about 170 users for 10 months. The experiments show that the number of requests that can be served mobile edges is similar to that of requests served by fixed edges, and then that mobile edges can be considered a viable (and lowcost) alternative to fixed edges.

The ever-progressing advancements in urban growth and technological development in recent decades have caused a noticeable increase of the phenomenon of socialenvironmental deterioration, leading to a decline in quality of life, reduction of social welfare and difficult urban mobility for people living in cities. The concept of Smart City can be used to mitigate several of the challenges arising from the aforementioned issues, relying on multiple tools and techniques (such as crowdsensing) to gather essential context data about how actual citizens consume resources and commute throughout their everyday lives. In this paper, we show how an urban mobility data analytics tool may help to determine the most visited regions and interconnections in an urban area. This information has been obtained using data gathered from a pool of users participating in a crowdsensing campaign, using the ParticipAct Brazil platform. The obtained results confirm the reliability of the information produced, highlighting the regions with the highest concentration of people during the geolocation monitoring process and their connections; therefore, this data may be used to plan possible future changes to how the city allocates its resources, to better suit the mobility needs of its citizens.

The advancement of networking and sensor-enabled devices have motivated the emergence of unprecedented initiatives, including Industry 4.0 and smart cities. Those are entwined in a way that makes their operation duly interconnected. Industry 4.0 will sooner become the biggest consumer of smart city big data. That data is geo-referenced, and its storage and processing need spatial-awareness, which is currently absent within the constellation of biggest big data management players of the market. We aim to fill this gap by providing spatial-aware big data management strategies in support for Industry 4.0 main principles. Our experimental results show that our strategies outperform those of state-of-the-art by orders of magnitude.

The widespread exploitation of cloud technologies forces cloud providers to forecast peaks of requests to guarantee always the adequate quality of service to their currently served customers. Static resource provisioning is rarely affordable for large Data Center Networks (DCNs) and dynamic resource management can be rather complex, in particular for networking. Hence, we claim the relevance of simulator-based approaches, helpful in planning DCN deployment and in analyzing performance behaviors in response to expected traffic patterns. However, existing cloud simulators exhibit non-negligible limitations for what relates to the modeling of networking issues of cloud Infrastructure as a Service (IaaS) deployments. Therefore, we propose DCNs-2, a novel extension package for the ns-2 simulator, as a valid solution to efficiently simulate DCNs with all their primary entities, such as switches, physical machines, racks, virtual machines, and so on.

This paper is motivated by the concept that the successful, effective, and sustainable implementation of the smart city paradigm requires a close cooperation among researchers with different, complementary interests and, in most cases, a multidisciplinary approach. It first briefly discusses how such a multidisciplinary methodology, transversal to various disciplines such as architecture, computer science, civil engineering, electrical, electronic and telecommunication engineering, social science and behavioral science, etc., can be successfully employed for the development of suitable modeling tools and real solutions of such sociotechnical systems. Then, the paper presents some pilot projects accomplished by the authors within the framework of some major European Union (EU) and national research programs, also involving the Bologna municipality and some of the key players of the smart city industry. Each project, characterized by different and complementary approaches/modeling tools, is illustrated along with the relevant contextualization and the advancements with respect to the state of the art.

The emergence of large-scale Cloud computing environments characterized by dynamic resource pricing schemes enables valuable cost saving opportunities for service providers that could dynamically decide to change the placement of their IT service components in order to reduce their bills. However, that requires new management solutions to dynamically reconfigure IT service components placement, in order to respond to pricing changes and to control and guarantee the high-level business objectives defined by service providers. This paper proposes a novel approach based on Genetic Algorithm (GA) optimization techniques for adaptive business-driven IT service component reconfiguration. Our proposal allows to evaluate the performance of complex IT services deployments over large-scale Cloud systems in a wide range of alternative configurations, by granting prompt transitions to more convenient placements as business values and costs change dynamically. We deeply assessed our framework in a realistic scenario that consists of 2-tier service architectures with real-world pricing schemes. Collected results show the effectiveness and quantify the overhead of our solution. The results also demonstrate the suitability of business-driven IT management techniques for service components placement and reconfiguration in highly dynamic and distributed Cloud systems.

Advancements in mobile communication technologies and the continuously increasingly diffusion of smartphones equipped with several physical and virtual sensors and with different network support are promoting novel mobile healthcare scenarios where patients with critical physical/behavioral conditions can be provided with anywhere and anytime care assistance even while on the move. In particular, this recent technology evolution simplifies the formation of mobile health communities (MHC) for prompt assistance in the case of emergency situations, where a MHC can be defined as a dynamic team of care givers formed by passing by mobile users physically co-located with the patient in need of help while on the move. Crowdsensing, through the massive use of smartphone sensors, further enhances the potential of supporting participatory management of MHCs for emergency scenarios. This paper presents a crowdsensing-based middleware called COLLEGA that provides several management functionalities for supporting prompt assistance to mobile patients in the case of a medical emergency. In particular, the paper claims to exploit the novel emerging LTE Direct technology to facilitate dynamic formation of MHCs and data dissemination. Our LTE-based support for participatory MHCs is described and experimental results showing the feasibility and effectiveness of the approach are also provided.

Some recent research projects, inspired by the widespread availability of sensor-provided smartphones, have built harvesting experiments to collect large quantities of data in urban areas. These efforts produced new real-world datasets, typically focusing on different technological aspects (GPS and Bluetooth mobility traces or WiFi indicators) and, more recently, also on user-related data, from low-level accelerometer samples to higher-level social networking data. At the same time, Mobile Crowd Sensing (MCS) blossomed with a few very recent project, with the goal to efficiently coordinate user participation, both to collect sensor data and to allow active collaboration in participatory tasks. This paper aims to shed some light and to propose new research directions on the MCS by employing the notable results already obtained in the Mobile Social Network area to the study of human dynamics. The reported results, comparing three MCS datasets available in the literature, lead to an in-depth discussion of some lessons we learned about sociotechnical management aspects of MCS. The results we present are valuable for the MCS community to design new MCS campaigns and to refine the whole MCS process to the purpose of better efficiency and scalability.

The exponential amount of geospatial data that has been accumulated in an accelerated pace has inevitably motivated the scientific community to examine novel parallel technologies for tuning the performance of spatial queries. Managing spatial data for an optimized query performance is particularly a challenging task. This is due to the growing complexity of geometric computations involved in querying spatial data, where traditional systems failed to beneficially expand. However, the use of large-scale and parallel-based computing infrastructures based on cost-effective commodity clusters and cloud computing environments introduces new management challenges to avoid bottlenecks such as overloading scarce computing resources, which may be caused by an unbalanced loading of parallel tasks. In this paper, we aim to fill those gaps by introducing a generic framework for optimizing the performance of big spatial data queries on top of Apache Spark. Our framework also supports advanced management functions including a unique self-adaptable load-balancing service to self-tune framework execution. Our experimental evaluation shows that our framework is scalable and efficient for querying massive amounts of real spatial datasets.

The exploitation of mobile social networking technologies merging crowdsensing systems enable mobile users to opportunistically create participatory mobile social networks based on not only common attributes, interests or contacts, but also mobility-related context, such as physical location and co-presence. Disaster management and mobile healthcare applications can benefit from the possibility of creating participatory communities based on physical closeness. Co-located people can dynamically form ad-hoc mobile community response networks (CRNs) to provide anywhere and anytime care assistance to users with critical physical/behavioral conditions after a disaster occurrence or even during their normal day-life while on the move. The effectiveness of mobile CRNs depends, however, on the possibility to select among co-located users the ones with the most appropriate competence to understand and execute required assistance actions. The paper introduces the concept of competence-based mobile CRNs and describes how competence-based mobile CRNs can be created within the specific framework of a crowdsensing-based middleware called COLLEGA that provides comprehensive management functionalities for supporting prompt assistance in emergency situations. In particular, the paper discusses our proposed competence model and its implementation within COLLEGA enabling to extract the competence of mobile CRN's members from data available on social networks, such as LinkedIn.

Players across the health ecosystem are initiating studies of thousands, even millions, of participants to gather diverse types of data, including biomedical, behavioral, and lifestyle in order to advance medical research. These efforts to collect multi-modal data sets on large cohorts coincide with the rise of broad activity and behavior tracking across industries, particularly in healthcare and the growing field of mobile health (mHealth). Government and pharmaceutical sponsored, as well as patient-driven group studies in this arena leverage the ability of mobile technology to continuously track behaviors and environmental factors with minimal participant burden. However, the adoption of mHealth has been constrained by the lack of robust solutions for large-scale data collection in free-living conditions and concerns around data quality. In this work, we describe the infrastructure Evidation Health has developed to collect mHealth data from millions of users through hundreds of different mobile devices and apps. Additionally, we provide evidence of the utility of the data for inferring individual traits pertaining to health, wellness, and behavior. To this end, we introduce and evaluate deep neural network models that achieve high prediction performance without requiring any feature engineering when trained directly on the densely sampled multivariate mHealth time series data. We believe that the present work substantiates both the feasibility and the utility of creating a very large mHealth research cohort, as envisioned by the many large cohort studies currently underway across therapeutic areas and conditions.

With the increasing acceptance of Network Function Virtualization (NFV) and Software Defined Networking (SDN) technologies, a radical transformation is currently occurring inside network providers infrastructures. The trend of Software-based networks foreseen with the 5th Generation of Mobile Network (5G) is drastically changing requirements in terms of how networks are deployed and managed. One of the major changes requires the transaction towards a distributed infrastructure, in which nodes are built with standard commodity hardware. This rapid deployment of datacenters is paving the way towards a different type of environment in which the computational resources are deployed up to the edge of the network, referred to as Multi-access Edge Computing (MEC) nodes. However, MEC nodes do not usually provide enough resources for executing standard virtualization technologies typically used in large datacenters. For this reason, software containerization represents a lightweight and viable virtualization alternative for such scenarios. This paper presents an architecture based on the Open Baton Management and Orchestration (MANO) framework combining different infrastructural technologies supporting the deployment of container-based network services even at the edge of the network.

Mobile Crowd Sensing (MCS) is a technique that aims to obtain the participation of volunteers willing to use their smartphones to harvest large quantities of data as they move in urban areas. Those volunteers typically move inside a limited area and can encounter other volunteers during their day activity. From the number and duration of their encounters, it is possible to categorize relations between volunteers. From this knowledge, we classified volunteers in communities that will cooperate to complete a data collection. The main idea is that users inside a cooperation group are more willing to participate in a sensing campaign. The paper presents results of an implementation of our solution in a real testbed, an ongoing experiment that involves more than 170 students from Bologna University campus. In particular, this article focuses on community identification and cooperative task execution. Shown results confirm the feasibility of the proposed approach and report how user activity can be increased leveraging cooperation among them.

New sensors embedded into modern smartphones has led into a new data collection prospective in which people directly collect all the sensitive data. This feature has found different applications, in particular in the Smart Cities area, in order to establish dynamic communications between the citizens and the city government. This category of application is nestled into the Mobile Crowd Sensing (MCS) application group, due to their final purpose of sharing sensing data to an open platform that includes a huge number of people. This paper presents an extension of the general-purpose ParticipAct platform, a MCS application developed by the University of Bologna, focused on the needs of people with impaired mobility. The goal is specializing ParticipAct to enable a crowdsourcing platform that guarantees a solid support for their lifetime allowing reviewing and sharing opinions regarding public and private places and architectonic barriers of a city area. Showed results confirm the effectiveness of the developed application in terms of both its viability via integration with existing and widely diffused Geographical Information Systems (GIS), and its feasibility in terms of system and user-perceived performances.

In the last years we have experienced a growing industrial interest in Mobile Cloud Networking (MCN) as the opportunity to exploit the cloud computing paradigm through Network Function Virtualization (NFV), primarily with the goal to reduce CAPEX/OPEX for future mobile networks deployment and operation. The gain from the point of view of infrastructure costs reduction is almost clear and recognized, while many technical challenges are still to be solved, especially with industry-mature solutions, due to the complexity of managing such type of infrastructures. In particular, the dynamicity and flexibility introduced by the virtualization of network functions add novel requirements on the service management and orchestration layers. In this perspective, this paper originally presents the architecture and primary implementation guidelines of the Mobile Cloud Networking framework developed within a large EU FP7 project. More specifically, it focuses on the innovative technical elements of our solution for service management and orchestration, namely i) orchestration strategies based on resource unit affinity and ii) compliance with emerging Open Cloud Computing Interface (OCCI) standards. To practically demonstrate the suitability of the proposed approach, a specific real use case has been implemented, i.e., the cloudification of the 3GPP IP Multimedia Subsystem (IMS), by reporting and analyzing the related performance results.

Recent advancements in wireless technologies and the widespread availability of smartphones equipped with several physical and virtual sensors are leading to the emergence of novel mobile healthcare scenarios where patients with critical physical/behavioral conditions can be provided with anywhere and anytime care assistance even while on the move. Crowdsensing, through the massive use of smartphone sensors further enhances the potential of supporting participatory management of emergency scenarios. This paper presents a macro process modelling of crowdsourced-based participatory emergency scenarios and, accordingly, proposes a crowdsensing-based middleware called COLLEGA that provides comprehensive management functionalities for supporting prompt assistance in case of a medical emergency. In particular, through participatory and opportunistic sensing, the COLLEGA framework allows dynamic formation of ad-hoc assistance groups formed by passing-by users capable of assisting mobile patients in need of help while waiting for professional caregivers and provides support for understanding the emergency situation and effectively planning and executing assistance actions.

The spread of projects focused on smart cities have grown in recent years. With this, the massive amount of data generated in these initiatives, creates a degree of complexity in how to manage all this information. In this paper we propose an infrastructure model for big data for a smart city projet. The goal of this model is to present the stages for the processing of data in the step of extraction, storage, processing and visualization, as well as the types of tools needed for each phase. To implement our proposed model, we used the Particip ACT Brazil a project based in smart cities. This project uses different databases to compose its big data and uses this data to seek solutions to urban problems. We observe that our model provides a structured vision of the software to be used in big data server of ParticipACT Brazil. In addition, we can also note that our model can be used in other big data servers.

One of the most interesting and promising challenges for Intelligent Transportation Systems (ITSs) relates to the traffic congestion problem. Congestion is a relevant issue for transportation because it reduces the efficiency of infrastructure and increases travel time, air pollution, and fuel consumption. Nowadays, the most promising technology in support of ITSs is found in the domain of Vehicular Ad Hoc Networks (VANETs). In this paper, we propose three protocols that are able to transmit traffic information for routes of interest on VANETs without any Road Side Unit (RSU) support. The proposed protocols adopt strategies to improve the performance of packet routing based on the density and location of vehicles; moreover, they enable an interesting comparison of the performance achievable with either reactive or proactive approaches. The extensive performance results reported show how it is possible to limit the congestion monitoring overhead along Routes of Interest (ROIs), while maintaining a sufficiently high performance in terms of traffic reporting. This may be done by employing context-aware data delivery techniques that autonomously adapt to runtime conditions. Copyright © 2016 John Wiley & Sons, Ltd.

Technical and economic opportunities of cloud computing become the focus for Internet applications and at the same time also for telco support infrastructures and network services. In fact, many telco providers are consolidating their service infrastructures towards converged and all-IP next generation networks providing typical telco services within LTE (and soon 5G) and also fixed network environments, e.g., often still adopting IP Multimedia Subsystem (IMS) architecture solutions. This telco service infrastructure evolution requires a significant upfront investment in the necessary hardware and software, thereby slowing down the adoption process significantly more than any other Internet application. Cloud computing applied to telco infrastructures can allow pay-per-use business models and significantly lower investment risks by providing telco infrastructure functionality as Virtual Network Functions (VNFs) on top of a Network Functions Virtualization (NFV) platform. For this purpose we propose a quality audit and resource brokering framework that is fully NFV-compliant. Its current and reported implementation specifically targets IMS services because of the still relevant role played by IMS in converged provisioning and the wide availability of IMS deployment testbeds to validate the proposal. In particular, the proposed solution can monitor the quality offered by VNFs and scale in/out depending on dynamic requirements; it is fully based on industrial standards and open-source reference implementations, thus enabling rapid adoption in real industrial environments.

Crowdsensing has appeared as a viable solution for data gathering in many applications with the advent of three emerging paradigms, namely Internet of Things, cloud computing, and mobile social networks. Built-in sensors in mobile devices can leverage the performance of the IoT applications in terms of energy and communication overhead savings by sending their data to the cloud servers. When crowdsensing is used for critical applications such as disaster/crisis management and/or public safety in the context of a smart city, trustworthiness of the collected data occurs as a crucial concern. In this paper, we propose using social network theory to evaluate trustworthiness of crowdsensed data, as well as the mobile devices that provide sensing services. To this end, we combine centralized reputation- based evaluation with collaborative reputation values based on votes and vote capacities. We model each participant as a node in a social network where nodes are inter-connected through their interaction values. Interaction stands for being assigned common sensing tasks. We evaluate the performance of our proposal through simulations, and show that use of social network theory-based crowdsensing with combined reputation formulation significantly improves the utility of the crowdsensing platform while dramatically reducing the manipulation probability of malicious nodes.

Efficient protocols for data packet delivery in Vehicular Ad-hoc NETworks (VANETs) are crucial to guarantee the correct forwarding of data. However, communication in VANETs is a challenging task not only for the high mobility of the vehicles, but as recent studies have shown, there is a negative impact on protocol performances caused by an obstacle such as another vehicle in the line-of-sight (LOS). Many different routing protocols were presented in the last years, but only few of them were considering the effect of non line-of-sight (NLOS) propagation. In this work, we present a new solution to improve the data packet delivery ratio considering the problems caused by a high mobility and NLOS condition. Our proposal can be integrated in every position-based routing protocol. In fact, our method creates a support context awareness of neighbors before letting the protocol choose the next hop, without interfering with the specific logic. Simulation results of our context-aware version have been compared with the original protocols and with a modified version in accordance with the principle that every violation of LOS involves a discard of the packet: our solution always perform better in terms of packet delivery ratio and delay.

Recent advances in sensor-equipped smartphones are opening brand new opportunities, such as automatically extracting Points Of Interest (POIs) and mobility habits of citizens in Smart Cities from the large amount of harvested data hotspots. At the same time, the high dynamicity and unpredictability of Smart Cities crowds, opportunistically collaborating toward these common crowdsensing tasks, introduces challenging issues due to the need for fast and continuous processing of these Big Data Streams in the backend of next generation crowdsensing platforms. This paper presents our practical experiences and lessons learnt in deploying the ParticipAct platform and living lab, an ongoing experiment at University of Bologna that involves 300 students for one year. Among all management issues addressed in ParticipAct, this article shows the integration of MongoDB in the ParticipAct backend, as a powerful NoSQL storage and processing engine to fasten the identification of POIs; the reported performance results confirm the feasibility of the approach by quantifying its advantages for city managers.

Cloud businesses need comprehensive visibility on hardware and software components, their utilization and their configuration. In addition, they need to integrate such information with their asset management systems and publicly available information such as hardware specifications. In this paper, we present an approach for cloud management and monitoring based on a semantic layer that unifies different interfaces and representations, and makes all relevant information accessible from a single point. We show a proof-of-concept based on OpenStack and Linked Data technologies and evaluate it in terms of overhead, query execution times, and effectiveness in the data gathering phase. Our findings indicate that a semantics-based approach is indeed feasible and advantageous for providing uniform access across different cloud environments and levels.

Mobile Crowd Sensing (MCS) aims to coordinate and activate the participation of volunteers willing to use their smartphones to harvest large quantities of data as they move in urban areas. One of the most important requirements in MCS is maximizing the effectiveness of the data gathering campaign. In fact, also due to the initial low penetration rate of MCS apps and to avoid making the MCS process cumbersome to users, only a small portion of the whole citizenship can be involved in such campaign, while most citizens are not part of the process. This paper proposes a novel approach that combines participatory and opportunistic techniques to amplify the amount of data harvested from the crowd. The core idea is that people with similar interests, such as employees of the same company, students or friends tend to meet more frequently with respect to people with different interests. Accordingly, it is possible to opportunistically involve in the crowd sensing loop people in the volunteers' neighborhood. Following that main design guideline, our work assesses the SOcial amplification FActor (SOFA) that allows to increase the number of samples retrievable during a crowd sensing campaign. We show the benefits of SOFA by using the ParticipAct MCS platform and we analyze three different application scenarios. Highly realistic simulation results, based on ParticipAct mobility traces, show the advantages of SOFA, with an amplification factor increasing up to 4.45.

Novel sensor-equipped smartphones have enabled the possibility of harvesting large quantities of data in urban areas by opportunistically involving citizens and their portable devices, as mobile sensors widely available and distributed over Smart Cities areas, typically defined as Mobile Crowd Sensing (MCS). Although some existing efforts have already tackled some of the several MCS issues, to the best of our knowledge, active experiments addressing the challenging issue of the assignment of MCS data collection campaigns to users, namely, MCS scheduling, in a large-scale crowdsensing real-world experiment are still missing. This paper presents the ParticipAct platform and living lab, an ongoing crowdsensing experiment at University of Bologna that involves 300 students for one year. In particular, this article focus on ParticipAct intelligent MCS scheduling of future crowdsensing campaigns based on user mobility history and powered by NoSQL technologies for fast processing of the large amount of mobility traces in the ParticipAct backend. Showed results confirm the feasibility of the proposed approach and quantify its cost.

In the last few years, we witnessed a growing interest in interoperability and portability topics within the Cloud Computing area. In fact, most heterogeneous PaaS solutions have been developed with no standard APIs, and with different models and levels of service; in this scenario, vendor lock-in becomes an issue. Cloud4SOA project aims to design and develop a Reference Architecture that could solve interoperability and portability problems within the Cloud domain. This paper describes our work on Cloud4SOA Semantic and SOA layer presenting a common knowledge base framework and a standardized set of harmonized APIs to overcome diversities among PaaS solution and report interesting experimental results collected for two real-world PaaS deployments.

The ever increasing diffusion of smartphones today equipped with several physical and virtual sensors allow to directly collect information about surrounding physical and logical context that range from monitoring current social pulse of individuals and entire communities to detecting user current physical activity. Enabling those advanced sensing capabilities requires complex signal processing, machine learning, and resource management algorithms that are often beyond the skills of many mobile app developers. This paper describes the relevance of these facilities for mobile crowdsensing applications in Smart City scenarios and presents our solution for activity detection, comparing it with the reference implementations provided by Google as part of the Google Play Services library.

Platform as a Service (PaaS) systems fully exploit the potential of elastic Cloud computing Infrastructure as a Service (IaaS) layer, by providing computational platforms for the developers characterized by a set of frameworks and runtimes. In these scenarios, the developers could focusing only on the implementation side of web applications without having to deal with configuration of the environment the web apps require for running properly. Services represent a central point of PaaS systems, providing external features for web applications such as SQL databases, messaging systems, and any kind of external software required by the developer. The monitoring of the availability and the performances of these Services plays an essential role in PaaS environments. The paper tackles above issues focusing on the real use case of the Cloud Foundry PaaS; collected results assess the effectiveness of the proposed monitoring function and confirm its feasibility and low overhead.

Open Data initiatives push public administrations to publish an increasing number of raw datasets, achieving a more transparent and open governance. The availability of new data raises opportunities for the development of new services, but also open new challenging issues such as the increasing volume of datasets publication available and their integration in large-scale public data ecosystems. The last decade has witnessed the spreading and the consolidation of the Semantic Web technologies that have been proposed as an opportunity to ease data integration through new semantic representation and interrogation languages. By using these technologies, we propose an analysis of the different phases that bring from raw data to mashups over these data through a deployment of standard Linked Data inside a triplestore and by exploiting a Geografic Information System tool for creating mashups with cartographic data, focusing on the real case of Bologna Open Data. We also report experimental results that point out the performances of different triplestores with the goal of supporting informed choices in this emerging new area.

Reducing traffic congestion and improving the efficiency of urban vehicular mobility are widely recognized as central objectives for smart cities. In particular, given the economic/time costs of developing an infrastructure for traffic monitoring and surveillance, there is growing interest in the exploitation of V2V communication technologies to foster new forms of peer-to-peer cooperation and to achieve even coarse-grained estimations of vehicular traffic, also with no need for communication towards global data collection centers. In addition, in realistic scenarios for the next years, it is necessary that such solutions can reasonably work with limited penetration rates of vehicles equipped with V2X traffic surveillance capabilities. In this perspective and within the framework of the ongoing EU FP7 COLOMBO project, we have investigated, developed, and thoroughly evaluated some innovative locality-based cooperation protocols for the determination of traffic characteristics in proximity of intersections, with the goal of offering concise monitoring indicators to optimize traffic light management. The reported results (obtained through realistic simulations based on real traffic traces and the real road topology of the city of Bologna) show that it is possible to achieve reasonable estimations of vehicular traffic, suitable for traffic light control optimization, even if with limited penetration rates of our solution.

Context awareness, intended as providing the current execution environment at the service level, is a fundamental capability in future mobile systems. Unfortunately, the real-world realization of such scenarios is currently undermined by inefficient context data delivery mechanisms, which introduce excessive overhead over bandwidth-constrained wireless fixed infrastructures. To efficiently offload context access from fixed infrastructures to mobile nodes, this paper presents a new data caching algorithm that exploits peculiar aspects of context distribution, mainly limited data lifetime and interests similarity between nodes in physical proximity, to properly select the data to evict when necessary. Our solution considers a history over past data accesses and information over data replication to better exploit the limited available space. Extensive simulation results, collected in NS2 simulator, support our assumptions and demonstrate that our caching solution improves system scalability while adding a limited management overhead.

Data distributions systems with guaranteed Quality of Service (QoS) levels, such as the data-centric Data Distribution Service (DDS) standard specification, have gained more and more success in the last decade. These systems represent suitable solutions for effective and high-performance data communication for challenging application scenarios with real-time requirements, such as air traffic management, industrial automation, smart grids, and, more recently, financial applications. Notwithstanding the last decade has witnessed the diffusion and consolidation of some major implementations, only a very few, in some sense obsolete, performance analysis studies are available in the literature. To fill that gap and to facilitate future IT decision processes, we propose a thorough analysis of the DDS implementations proposed by the two main stakeholders in the DDS market, namely, PrismTech and Real-Time Innovations (RTI). The reported experimental results point out the pros and cons of both solutions in terms of data delivery performance, also by precisely evaluating bottlenecks and overhead, for instance in terms of CPU and memory resource usage.

Despite its great promises, current Cloud offering is still typically rather static and does not support very dynamic execution patterns. In fact, while dynamic resource allocation is typically required to ensure efficient and effective usage of the Cloud resources, Cloud providers have to deal with complex services, usually treated as black-boxes; hence, the estimation of the maximum number of resources that could improve service execution is a big challenge. This paper proposes and explores a novel automatic service rescaling approach to solve the deployment scaling problem. The proposed scheme, called Dynamic Cloud Infrastructure (DCI), has been designed and verified as a new architecture for the IBM Cloud infrastructure. DCI provides hints useful to understand if a particular service could take full advantage from additional resources enabling automatic discovery of the deployment configuration that jointly addresses high service scalability and low resource consumption. We detail the lessons learnt from the application of the proposed solution in the IBM Smart Bay project and present experimental results that demonstrate our approach as a viable first step toward run-time service scaling.

ABSTRACT In the last years, context awareness, namely the provisioning of the current execution context to the application level, has received an increasing attention up to becoming a core capability in next generation mobile scenarios. Context awareness intrinsically forces a continuous delivery of context data to resource‐constrained mobile devices, such as mobile phones and personal digital assistants, to allow application adaptation, and that can become too severe a constraint even for modern platforms (Android, iOS, etc.). This paper focuses on the realization of a context data distribution support for Android‐based mobile phones with guaranteed quality levels on the context data delivery time. Android, notwithstanding its great potential, puts harsh constraints on the implementation of specific context distribution primitives, thus preventing the realization of a wide set of significant deployment scenarios. At this stage, we face that a large campaign of tests are necessary. We have collected main experimental results in a real testbed to highlight noteworthy details on the runtime performance obtainable with a real Android deployment. Copyright © 2012 John Wiley & Sons, Ltd.

IP multimedia subsystem (IMS) and IMS-based services are increasingly providing interoperable session control and mobility for next-generation all-IP networks. However, clear design guidelines and techniques for the support of scalable IMS-based deployment, especially for data-intensive services such as mobility management, presence, and instant messaging, are still missing. That could block or at least relevantly slow down IMS acceptance by network operators and application providers. To address these challenges, this paper thoroughly analyzes IMS scalability with special attention to intradomain deployment issues. Then, it proposes a novel solution with three core original contributions toward intradomain scalability: 1) data-centric dissemination of session state with limited overhead; 2) service-aware routing for fast intradomain load balancing; 3) service-aware load monitoring and component de-/activation for long-term intradomain load partitioning at both service and infrastructure levels. The reported experimental results point out that our solution can significantly increase intradomain scalability with very limited costs.

Virtual Machine (VM) placement has to carefully consider the aggregated resource consumption of co-located VMs in order to obey service level agreements at lower possible cost. In this paper, we focus on satisfying the traffic demands of the VMs in addition to CPU and memory requirements. This is a much more complex problem both due to its quadratic nature (being the communication between a pair of VMs) and since it involves many factors beyond the physical host, like the network topologies and the routing scheme. Moreover, traffic patterns may vary over time and predicting the resulting effect on the actual available bandwidth between hosts within the data center is extremely difficult. We address this problem by trying to allocate a placement that not only satisfies the predicted communication demand but is also resilient to demand time-variations. This gives rise to a new optimization problem that we call the Min Cut Ratio-aware VM Placement (MCRVMP). The general MCRVMP problem is NP-Hard, hence, we introduce several heuristics to solve it in reasonable time. We present extensive experimental results, associated with both placement computation and run-time performance under time-varying traffic demands, to show that our heuristics provide good results (compared to the optimal solution) for medium size data centers.

Context awareness, namely the provisioning of the current execution context at the application level, forces the continuous delivery of context data to resource-constrained mobile devices, and that can become too severe a constraint even for modern support (Android, iOS, etc.). This article focuses on the realization of a context data distribution infrastructure for Android-based mobile phones, and highlights important details on the implementation of specific context distribution primitives. Finally, we present new experimental results to assess the runtime performances obtainable with a real Android deployment.

Cloud computing management supports are becoming more and more important not only in the fields of IT infrastructures for Internet applications and services, but steadily also in the field of telecommunication services and infrastructures. More and more telecommunication service providers have adopted IP Multimedia Subsystems (IMS) to consolidate their service infrastructures towards converged, all-IP, access network independent Next Generation Networks (NGNs). Although modern NGN service environments have the potential to greatly reduce new telecommunication services time-to-market, until now significant upfront investments into computational resources are required, that is still often a risk for the enterprise, with unsure return on invest. The application of cloud computing technologies to IMS-based service infrastructures enables new pay-per-use cost models, so IMS service providers may be charged for what they use only, significantly lowering the risk of bad investments. This work presents the design and implementation of a cloud brokering system for IMS services, capable of simultaneously interworking with multiple cloud infrastructures. We developed a Cloud Broker Engine (CBE) able to dynamically up/down-scale cloud resources across multiple cloud platforms. Our CBE is capable of coping with dynamic load situations and QoS requirements optimizing resource utilization across different cloud infrastructures: it enables QoS assurance and optimizes resource consumption across multiple cloud providers.

Software as a Service (SaaS) applications fully-exploiting the potential of elastic Cloud computing infrastructures naturally are enabling new ubiquitous access scenarios for nomadic users, such as market salesmen and home healthcare medical assistants. SaaS applications typically require to transfer data and resources to the Cloud infrastructure site; that raises several challenging issues spanning from access control to resources to privacy protection, ownership, and security of the data of the final SaaS users. However, although encryption of personal and enterprise data is strongly recommended by existing Cloud infrastructures, such as Amazon Web Services (AWS), typically they do not provide yet adequate encryption and key management support. This paper presents a real use case of Vitaever, a home healthcare SaaS application deployed on Amazon AWS, and discusses the challenges and changes needed to add cryptography and key management capabilities to the standard AWS Web/database offer so to enable SaaS data protection. We also show experimental results that benchmark the new security functions over Amazon, demonstrating their applicability to SaaS production deployments.

Cloud computing has become an essential technology not only for web provisioning, but also in mobile scenarios. Mobile devices are usually resource constrained due to processing and power limitations, so typical applications are not easy portable. Battery draining and application performance (resource shortage) have a big impact on the experienced quality, so shifting applications and services to the Cloud may improve mobile user's satisfaction. However, available Cloud solutions are mostly focused on scenarios with slowly changing provisioning, which are unable to support and promptly react to short-term provisioning requests. To address the new scenario, this paper proposes a novel Cloud monitoring and management architecture based on the data-centric publish-subscribe Data Distribution Service (DDS) standard. We present not only an architecture proposal, but also a real prototype that we have deployed in our experimental testbed. The experimental results show that our architecture is able to support the scheduling of highly dynamic tasks in the Cloud while maintaining low overheads.

The increasing request for converged multimedia services have motivated relevant standardization efforts, such as the Session Initiation Protocol (SIP) to support session control, mobility, and interoperability in all-IP next generation wireless networks. Notwithstanding the central role of SIP in novel converged multimedia, the potential of SIP-based service composition for the development of new classes of Web 2.0 services able to interoperate with existing HTTP-based services is still widely unexplored. The paper proposes an original solution to improve online user experience by integrating a SIP stack into the Web browser, thus enabling the execution of novel SIP-based applications directly at the client endpoint. In particular, our browser extension coordinates with our novel SIP-based Converged Application Server to enable session mobility and prevent abuses of the services available in the client. Experimental results show that our SIP-based solution is feasible in most common Internet deployment scenarios and enables session mobility with limited management cost.

On one hand, the convergence of the Internet, Telecommunication and Broadcasting is generating new services over the Internet, and on the other hand, the online experience is getting improved via the introduction of user profile mobility and web session mobility. While different architectural schemes for HTTP session mobility have been proposed and implemented in the academia, the industry has introduced user profile mobility solutions, such as Mozilla Weave and Google Browser Sync. This paper presents our effort to improve the web browsing experience via a SIP-based web session mobility service. It discusses the client and proxy features of our session mobility system. The system architecture is service-oriented and easy to integrate in any existing SIP-based deployment environment. Experimental results about CPU usage, memory consumption, and number of requests processed at the proxy side confirm the feasibility of our solution from a service provider perspective in all most common Internet deployment scenarios.

Context-aware scenarios require the distribution of large amounts of context data over fixed wireless infrastructures. The opportunistic usage of mobile nodes as data carriers has received a great deal of interests in recent years in order to increase system scalability. In this paper, we focus on the overhead introduced by context distribution on mobile devices, and we highlight the need of resource-aware solutions to trade off run-time overhead with available resources. Next, we propose a novel algorithm to dynamically adapt data distribution task processing load at mobile devices. Our experimental results, obtained using a real testbed, demonstrate that our approach ensures correct resource management under a wide range of different working conditions.

Disaster area scenarios are the consequence of sudden and unexpected disasters due to either human or natural causes, such as terrorist attacks and earthquakes. Towards the main goal of saving as many human lives as possible, context-aware services are emerging as standard-de-facto solutions to improve the coordination of involved rescue teams. Unfortunately, the scarce resources (communication bandwidth, memory, etc.) lead to very tough deployment scenarios to deal with. This paper presents our real-world quality-based context data distribution infrastructure for context-aware services in disaster areas. The main contribution is a quality-based caching solution that self-adapts by using quality requirements associated to close neighbors. Collected experimental results confirm that our proposal improves both system scalability and data availability.

Advances in wireless communications have motivated the development of Wireless Sensor Networks (WSNs) for low-cost and easy-deployable physical and environmental monitoring. At the same time, the evolution of portable mobile devices is enabling novel fully integrated internetwork computing scenarios by making viable to opportunistically exploit Mobile Ad-hoc NETwork (MANET) devices traversing the WSN and equipped with a WSN radio to relay part of the WSN traffic over the MANET. The paper proposes an original solution to enable reliable WSN communications between mobile MANET devices and fixed WSN sensors; the primary design guideline is to exploit a local beaconing mechanism together with low-level WSN link monitoring techniques and data retransmissions so to grant reliable communications notwithstanding MANET node mobility. The reported experimental results, collected over a real testbed, confirm the effectiveness of the proposed solution, even under challenging channel contention and device mobility conditions.

In the recent years, Cloud computing is emerging as the next big revolution of both computer networks and web provisioning. Due to its enormous promises, several vendors, such as Amazon and IBM, started designing, developing, and deploying Cloud solutions to optimize the usage of their own data centers. Unfortunately, several management issues of the Cloud are still open and deserve additional research. Among them, and fuelled by the emerging Green Computing research, Cloud architectures have to consolidate virtual machines in the minimal number of physical servers to reduce the run-time power consumption. In this paper, we present a project on power saving through server consolidation conducted at the IBM Innovation Centre in Dublin. Our experimental results, collected on a real testbed, show that server consolidation can effectively save energy, while introducing minimum performance degradation.

Ubiquitous smart environments equipped with low-cost and easily-deployable wireless sensor networks (WSNs) and ever-increasing widespread Mobile Ad hoc NETworks (MANETs) are opening brand new opportunities in environmental monitoring. This paper proposes an original solution for WSN/MANET integration based on the primary design guideline of opportunistically exploiting MANET overlays impromptu formed over the WSN to improve and boost the data collection task of a typical WSN. On the one hand, we adopt a cross-layer approach that exploits MANET connections to differentiate and fasten the delivery of sensed urgent data by pushing them over low-latency MANET paths. On the other hand, we take advantage of local cross-layer visibility of the WSN data collection procedures and protocols to carefully control and limit WSN–MANET coordination overhead. We claim that our proposed solution can obtain significant quality of service improvements via differentiation, by granting faster delivery times to urgent data with a very limited cost in most common execution scenarios.

The possibility of distributing data to all interested entities connected to a mobile system is a fundamental core function in future mobile ubiquitous class based applications. Unfortunately, its realization poses many problems especially due to the typically scarce resources. With the goal of enhancing scalability, this paper presents a novel data distribution infrastructure for wireless mobile environments. After the introduction of quality-based indicators useful to manage data distribution, we present an adaptive data batching technique that exploits load information to reduce wireless channel congestion. Our results indicate that our data distribution infrastructure scales well also under high loads.

Recent advances in wireless communications have motivated the development of Wireless Sensor Networks (WSNs) for low-cost and easy-deployable physical and environmental monitoring. WSNs were typically accessible only through special WSN nodes acting as WSN data sinks and gateways towards standard IP-based networks. The recent diffusion of low-power IP network protocol implementations has (potentially) made all WSN nodes directly reachable over IP. However, the wide variety of communication platforms makes it difficult to glue together different access types. We propose an original solution for WSN gateway-/IP-based access integration based on the primary design guideline of exploiting a proxy component to facilitate WSN access management. Our proposal is fully compliant with latest communication standards and adopts a novel (translucent) approach to enable either fully-transparent or fully-aware WSN access control. Our experimental results show good data delivery performances with different WSN access types and evaluate the cost of our management support.

Recent trends in data-centric systems have motivated significant standardization efforts, such as the Data Distribution Service (DDS) to enable data dissemination with guaranteed Quality of Service (QoS). However, clear design guidelines and techniques for the support of reliable and scalable DDS-based deployments, especially for (mobile) data intensive services, such as Internet-wide information dissemination for breaking news or financial analysis services, are still missing. After an analysis of main DDS fault-tolerance and scalability deployment issues, this paper proposes a novel solution with two core original contributions: i) DDS-compliant routing substrate to facilitate reliable data dissemination between mobile devices; ii) relay-based DDS support infrastructure with limited overhead to enable scalable Internet-wide data dissemination. Our solution, especially tailored for mobile computing scenarios, is lightweight and requires neither persistency nor heavy operations at mobile device side. Reported experimental results confirm that our proposal can guarantee desired scalability requirements with a limited network, CPU, and memory resource overhead.

Disaster areas typical to deal with unexpected and sudden disasters, such as earthquakes and terrorist attacks, can take advantage of context-awareness, namely the capability of providing applications with full awareness of current execution context, to increase the possibility of saving human lives. Unfortunately, context-aware services in disaster areas require efficient and reliable context data distribution. Towards this direction, we present our quality-based context data distribution infrastructure, and we introduce two optimizations useful to improve both distribution efficiency and reliability. Experimental results, obtained by means of simulations, support our solutions.

Recent trends in data-centric systems have motivated significant standardization efforts, such as the Data Distribution Service (DDS) to support data dissemination with guaranteed Quality of Service (QoS) in heterogeneous Internet environments. Notwithstanding the central relevance of DDS in that scenario, DDS-based pub/sub solutions still exhibit limited support for reliability, by omitting advanced techniques to reduce/eliminate QoS-degradations and data losses due to possible network and DDS system faults. We propose an original solution for fault- tolerance and prompt recovery of DDS-based pub/sub systems based on a DDS-compliant P2P routing substrate that continuously achieves a guaranteed data delivery with expected QoS-levels. In contrast with similar solutions in the field, our proposal neither requires support for data persistency nor implies heavy client-side operations. We exploit a DDS-compliant data dispatching infrastructure to reliably disseminate events and to balance data distribution load. The reported experimental results point out that our solution can guarantee desired requirements together with a limited overhead: the paper reports also performance indicators for our proposal CPU and memory resource usage.

The provisioning of multimedia streaming towards wireless mobile devices, even while they change their point/technology to access the Internet, is of growing relevance in the converged mobile world. Several technical challenges have to be faced for seamlessly supporting horizontal/vertical handoffs without interrupting on-going service sessions and without endangering service continuity. The paper specifically focuses on innovative techniques to rapidly take proper content adaptation decisions in case of vertical handoffs. To avoid service interruptions, we originally adopt a combination of context-aware proactive management based on vertical handoff predictions and of innovative multiple-criteria decisions that effectively exploit user/device profiling and multimedia adaptor descriptions. These original techniques are implemented in a support facility integrated in our mobile multimedia middleware and available for download. The reported experimental results, collected in a real testbed at our university, demonstrate the suitability of the proposed approach.

Recent advances in portable client devices are enabling new scenarios where mobile users assume to continuously access to various services, such as email, printing, and social computing applications, by opportunistically exploiting any computing resource and any wireless connectivity possibility encountered during their roam. That requirement calls for suitable context-aware middlewares capable of retrieving and using context data to personalize service provisioning. However, existing solutions still exhibit very limited support for context data management and distribution, especially in wide-scale and highly heterogeneous systems. The paper proposes a novel context-aware middleware to achieve scalability in context data dissemination. The primary design guideline is to reduce context data traffic by using a hierarchical distributed architecture and pervasive physical/logical caching techniques. To validate our design choices, we exploited our context-aware middleware to realize an advertisements service and a service discovery facility in our university campus. Obtained performance results demonstrate that our solution positively affects system scalability and average context dissemination time.

The increasing request for mobile multimedia services have motivated relevant standardization efforts, such as the IP multimedia system (IMS) to support session control, mobility, and interoperability in all-IP next generation wireless networks. Notwithstanding their increasing diffusion, IMS solutions still exhibit limited support for service scalability, especially for data intensive services such as mobility management, presence, and instant messaging, by omitting clear design guidelines and techniques to (re-)distribute incoming load dynamically. The contribution of this paper is twofold. First, it thoroughly analyzes the state-of-the-art literature in the field to clarify all main IMS scalability issues. Second, it proposes a novel and widely applicable architecture of solution based on three original guidelines: data-centric session management; differentiated management of intra-/inter-domain communications; service-aware load-balancing at both infrastructure and service levels. Preliminary performance results collected in the IMS-enabled wireless infrastructure at our campus demonstrate the effectiveness of the proposal.

Self-adaptive management and quality adaptation of multimedia services are open challenges in the heterogeneous wireless Internet, where different wireless access points potentially enable anywhere anytime Internet connectivity. One of the most challenging issues is to guarantee streaming continuity with maximum quality, despite possible handoffs at multimedia provisioning time. To enable handoff management to self-adapt to specific application requirements with minimum resource consumption, this paper offers three main contributions. First, it proposes a simple way to specify handoff-related service-level objectives that are focused on quality metrics and tolerable delay. Second, it presents how to automatically derive from these objectives a set of parameters to guide system-level configuration about handoff strategies and dynamic buffer tuning. Third, it describes the design and implementation of a novel handoff management infrastructure for maximizing streaming quality while minimizing resource consumption. Our infrastructure exploits i) experimentally evaluated tuning diagrams for resource management and ii) handoff prediction/awareness. The reported results show the effectiveness of our approach, which permits to achieve the desired quality-delay tradeoff in common Internet deployment environments, even in presence of vertical handoffs.

Recent advances in wireless client devices and multimedia communications have motivated relevant standardization efforts, such as the IP Multimedia Subsystem (IMS) to support session control, mobility, and interoperability in all-IP next generation networks. Notwithstanding the central relevance of IMS for novel mobile multimedia services, IMS-based solutions still exhibit limited support for service continuity during handoffs, by omitting advanced techniques to reduce/eliminate handoff delays and quality degradations, especially during vertical handoffs. We propose an original solution for service continuity and dynamic multimedia content tailoring based on the primary design guideline of exploiting terminal-based decentralized handoff predictions to proactively activate application-level management operations (flow quality downscaling). The proposal is fully compliant with standard IMS and exploits infrastructure media gateways to execute content tailoring actions, without requiring heavy client-side adaptation operations. The reported experimental results point out that our solution can avoid streaming playout interruptions and significantly increase user-perceived service quality, without negative effects on handoff delay.

New challenging deployment scenarios are accommodating limited and heterogeneous portable devices that roam among wireless access localities during service provisioning with session maintenance and continuity requirements, such as in multimedia streaming. That calls for novel middlewares able to dynamically personalize service quality, with no interruptions while clients move in wired-wireless integrated networks at provision time. The paper proposes a middleware-level proactive buffering solution for streaming continuity based on mobile proxies. Mobile proxies execute in the wired network locally to their wireless clients and proactively migrate to maintain colocality with associated roaming devices. In addition, apart from traditional client-side buffering, mobile proxies proactively manage pre-fetching buffers of multimedia contents by dynamically adapting buffer size to the current context such as handoff probability, client/streaming characteristics, and user service class. Experimental results show that, notwithstanding portable Java-based implementation, our context-aware proactive and adaptive buffering does not experience streaming interruptions in most common wireless Internet deployment scenarios

New challenging deployment scenarios are accommodating portable devices with limited and heterogeneous capabilities that roam among wireless access localities during service provisioning with session continuity requirements, such as in multimedia streaming. The paper proposes an original two-level buffering strategy to maintain streaming continuity independently of client roaming at provision time. In particular, it focuses on a specific component of the proposed support infrastructure, i.e., the pure Java buffering component, which has been shown to outperform the standard Java Media Framework in both streaming initialization time and imposed overhead.

Advances in wireless solutions and portable devices are enabling new challenging service scenarios where mobile users are willing to access ubiquitous and continuous services. This calls for novel middleware capable of tailoring service contents to client characteristics and of following client movements at provision time. The paper proposes MUM, a dynamic and flexible middleware to support continuous services to mobile users in ubiquitous scenarios. MUM performs service configuration by dynamically distributing middleware components to intermediate nodes along the client-server path and provides service session continuity by automatically migrating the session state in response to user movements during service provisioning. MUM exploits mobile agents to move both middleware components and session state, where and when needed, while it allows service developers to continue using the traditional client/server model for MUM-based application components. In addition, The work presents the implementation of a Video-on-Demand service on top of MUM, with the goal of verifying the feasibility of our approach when applied to the challenging multimedia application area. First experimental results show that, notwithstanding the application-level approach, the MUM configuration/session migration times are compatible even with the strict requirements imposed by multimedia distribution over the best-effort Internet.