• Categorization of existing service orchestration strategies that improve energy-efficiency in Edge computing. • Identification and analysis of novel approaches introduced by integrating renewable energy sources into Edge service orchestration. • Open research challenges and case studies to future sustainable orchestration. Edge computing is an emerging paradigm of decentralized computing. It extends the Cloud infrastructure to the Edge of the network and closer to users. It allows the deployment of low-latency services, better privacy and extended availability. Orchestration, based on softwarization and virtualization of services, plays a crucial role in performance optimization and dynamic adaptation to the evolving context. It constitutes the basis of the operation of Edge computing. In this systematic literature review we study the problem of energy efficiency in the orchestration of Edge services. Orchestration represents both a challenge and a lever for the energy management of future systems of this type that will be deployed on a global scale. Faced with current environmental concerns and the growing and plethora of heterogeneous and scattered resources involved in these systems, it is imperative to review the design of orchestration methods from the additional perspective of sustainability and efficiency. We provide a comprehensive classification framework specifically designed for energy-efficient Edge service orchestration, analyzing 63 research papers across traditional and renewable energy contexts. Our analysis reveals three distinct categories of traditional strategies and identifies three emerging orchestration paradigms unique to renewable energy integration. We explore the research landscape, identifying strategies that consider renewable energy, energy storage and renewable energy management, lacking in the current survey literature. We also discuss eleven potential future directions and provide three case studies. This review strengthens the critical importance of addressing sustainability concerns and demonstrates how virtualization and service orchestration constitute fundamental foundations for future energy-efficient distributed ICT infrastructures.

Deep Reinforcement Learning (DRL) has proven effective across numerous fields, making it a promising approach for optimizing network slice placement in 5G mobile networks. Many studies have highlighted the advantages of DRL agents in autonomously and efficiently placing network slices, aligning well with the vision of major operators and organizations for next-generation networks. However, the challenge of stable convergence in DRL agents, particularly when their action space becomes large, has limited their application to small-and medium-sized infrastructures. In this paper, we address this limitation by introducing P-DDQN, a novel pointer-based DRL algorithm that uses a fixed set of navigation actions to enable spatial exploration of the infrastructure. P-DDQN is capable of exploring very large-scale networks while maintaining a constant number of actions. We evaluated it on small, medium, and large infrastructures, and simulation results demonstrate that it outperforms traditional DRL agents, such as Double Deep Q-Network (DDQN) and Deep Q-Network (DQN).

Traditional routing protocols and analytical routing optimization models face limitations in adapting to dynamic and complex environments such as SDN. Deep Reinforcement Learning (DRL) offers promise for addressing these challenges, but its intensive training phase hinders practical implementation. This paper presents a distributed DRL-based routing optimization solution in SDN, enhanced with Generative Adversarial Networks (GAN) to expedite agent training. Our approach, evaluated on a Containernet and OpenAI Gym-based testbed, effectively optimizes network traffic routes for diverse traffic classes, maximizing throughput. Activation of the GAN module significantly reduces training times, enhancing the feasibility of our solution for real-world deployment.

With the emergence of the Internet of Things (IoT) services, meeting multiple and diverse Quality of Service (QoS) requirements in networks has become a crucial issue. In the new 5G networks, network slicing is presented as the solution to provide a tailored QoS for different network services. This new technology offers better prospects for IoT services and applications. In fact, in modern IoT systems, the number of IoT devices increases, and these systems evolve to be autonomous IoT systems. QoS management must be done without human intervention, making conventional QoS management mechanisms unsuitable. In this paper, we introduce an oneshot Deep Reinforcement Learning (DRL) agent capable of autonomously receiving requests for slices and proposing a placement on the physical infrastructure that maximizes the total number of accepted requests while guaranteeing load balancing at the infrastructure resources level. By adopting a new paradigm located at the crossroads between the single DRL agent and the multi-agent DRL, our agent manages to generate the placement decision of a slice request in one step, which makes it compatible with the European Telecommunications Standards Institute (ETSI) standard. Numerous simulations and comparisons with six other algorithms allowed us to validate its effectiveness in real-time scenarios where learning from previous placements is required to improve future slice provisioning.

Safety applications are the cornerstone of the envisioned Vehicle Ad Hoc Network (VANET). Early transmission of alert messages following car accidents can avoid further potential crashes and save lives. However, the limited terrestrial coverage on highways, particularly in rural areas with low traffic density, hinders the deployment of this service. One promising solution involves integrating Unmanned Aerial Vehicles (UAVs), commonly known as drones, into VANETs to serve as flying relays. These UAVs can re-broadcast alert or warning messages between vehicles, bridging communication gaps. In this paper, we propose a unified UAVs-VANET architecture where UAVs relay messages among vehicles on rural highways. To evaluate our approach, we consider a case study involving a road accident on a Tunisian highway (Tunis - Bou Salem 75 km). We use the SUMO simulator in conjunction with NS3. The obtained results showed that 100% of vehicles are alerted within 3.85 seconds. The study also investigates how the number of deployed UAVs impacts the number of alerted vehicles.

The routing challenges in Flying Ad Hoc Networks (FANETs), characterized by high-speed Unmanned Aerial Vehicles (UAVs), limited UAV battery life, intermittent links, network partitioning, and dynamic topologies, have led to the development of specialized routing protocols based on Reinforcement Learning (RL). In this context, the Q-Learning algorithm is the most commonly used RL algorithm. It relies on two primary hyperparameters: the learning rate and discount factor. The protocol's efficiency hinges on the selection of these parameters. To tackle this challenge, numerous adaptive Q-Learning routing protocols introduce novel functions to dynamically adjust the learning parameters. Therefore, this paper delves into an examination of these parameters and introduces a novel taxonomy categorizing them into three distinct classes: linear function-based adjustment, exponential function-based adjustment, and grid search-based adjustment. This paper highlights that the prevailing adjustment function for the learning rate follows a decreasing exponential pattern, while the discount factor adheres to a linear function. This equilibrium facilitates swift adaptation to changes while ensuring a stable transition between short-term and long-term rewards. Such balance is essential for efficient and effective routing in FANETs.

Identifying self-admitted technical debt (SATD) plays an important role in maintaining software stability and improving software quality. Although existing methods can detect SATD and researchers have identified design debt and requirement debt, an approach to realize multiple classification of SATD, including defect, test, and documentation, is still lacking. In this paper, we combine text generation oversampling and the Convolutional Neural Networks-Gated Recurrent Unit (CNNGRU) model, and propose an approach called SCGRU to classify multiple debt, including defect, test, documentation, design, and requirement. First, SeqGAN-based text generation is employed to generate new samples by learning the original SATD data, thereby increasing the number of SATD samples such as defect debt and reducing data imbalance. Then, we apply the CNNGRU model to refine SATD into multiple classes. An experiment with cross-project identification of 10 projects shows that our approach is more effective than existing methods such as CNN and text mining. The proposed SCGRU approach has strong advantages especially in cases of flawed debt with very unbalanced data such as test debt and documention debt.

Solving the problem of connectivity in remote areas is vital for providing important services (e.g., telemedicine, virtual education, etc.) to communities of these regions. However, managing these communication networks is complex and requires permanent human intervention in such inaccessible and highly variable environments. We propose a network self-management architecture based on SDN and DRL that self-adapts to operational conditions to meet the QoS demands of network services. We use a case study of QoS-aware routing optimization in a rural scenario to test our architecture.

Summary Modeling and specifying correct software systems is a challenging task that can be supported by providing appropriate modeling abstractions. This article proposes an approach for graphical multiscale modeling of such systems using model transformation techniques. The approach is founded on a guided rule‐based iterative modeling process ensuring controlled transition from a coarse‐grained description to a fine‐grained description. It provides also user‐friendly graphical descriptions by extension of UML notations, hence preserving the common practices from software architectures design. The iterative design process is supported by a set of model transformation rules. The rules manage the refinement process (by adding or removing subsystems or by adding or removing details on a given subsystem) as a model transformation. Our approach is supported by a rule‐based generator that implements the automatic transformation of UML diagrams into Event‐B specifications allowing formal verification of their correctness properties, and relieving software architects of mastering formal techniques. To experiment and validate our approach, we consider a case study dedicated to the smart cities.

Multi-domain federations support shared network services. Many orchestrators manage the service’s lifecycle. For the shared VNF Forwarding Graph (VNF-FG) reconfiguration, orchestrators update the graph’s logical information, ensuring a consistent behavior for replicas. Only one work in the literature considers sharing the VNF-FG. However, it offers weak consistency guarantees, without considering the VNF-FG’s non-functional dependencies. In case of a conflict, while updating the VNF-FG, the orchestrators solve consensus. However, this adds latency, undermining the goal of Network Function Virtualization. This paper introduces the first coordination-free multi-domain orchestration algorithm for consistent shared VNF-FG reconfiguration. Unlike the current state of the art, the proposed algorithm skips the coordination phase, offers strong eventual consistency, and supports non-functional dependencies. We present two variants: the preventive, where transient inconsistent states are prevented; the corrective, where intermediary inconsistent states, during the updating process, are tolerated. We prove the correctness of our algorithm and evaluate it. The variants, unlike the state of the art, reconfigure consistently the shared VNF-FGs without solving consensus. They offer stronger guarantees compared to the literature, like allowing orchestrators to reject ongoing reconfigurations without a high impact on performance.

In this paper, we consider the problem of identifying multiple bottlenecks (a.k.a bottleneck analysis) in IoT Service Platforms. For QoS-constrained applications, IoT Platforms have grown in complexity with non-stationary workloads and inter-task dependencies created by data flows crossing the platform’s nodes. These factors create multiple simultaneous “bottlenecks” (a bottleneck expresses overload in terms of request processing time on a given node, and contributes to QoS degradation). Multi-bottlenecks are non-trivial to analyze since they may escape typical assumptions made in classic performance analysis, such as analysis based on queuing theory models. Solving this analysis problem requires real-time collection and analysis of data that can be massive, and as a result, induce negative impacts on the performance of the NFV-based IoT Platform (NIP) (e.g., use of bandwidth, computing resource, and storage resource). Therefore, it needs to be adapted to the strict minimum allowing effective analysis. We build an adaptive performance analysis method that optimizes bottlenecks’ identification for a monitoring overhead budget associated with the different available metrics. Instead of systematically collecting all the NIP metrics, the proposed process determines the best subset of metrics to consider for the efficiency of the performance analysis. The conducted experiments on a practical use case show that the proposed method exhibited high performances of the bottleneck analysis process, in the presence of different bottleneck types and durations, with very few false positives and false negatives.

Network Function Virtualization (NFV) enables running Virtualized Network Functions (VNF) on top of any generic, commercial off-the-shelf hardware. VNFs rely on the VNF-Forwarding Graph (VNF-FG) concept to describe and implement network topologies. VNF-FGs are provisioned and managed by appropriate orchestrators. In the multidomain approach, and for complex and sophisticated network topologies, VNF-FGs are managed by multiple orchestrators. Despite being a key task in the lifecycle of network services, the reconfiguration of VNF-FGs under multi-domain orchestration has not been thoroughly explored in the literature, focused mostly on the initial VNF-FG placement and embedding. Reconfiguration enables the providers to answer to unforeseen changes in the environment. However, such distributed orchestration might lead to inconsistencies, and thus partial or total failure of network services. In turn, such inconsistencies increase the cost for providers. This paper proposes the use of causal dependencies among orchestrators to provision consistent VNF-FG reconfiguration in distributed multi-domain environments under asynchronous communication channels. The proposed model is implemented and compared with the current ETSI VNF-FG reconfiguration algorithm. Results indicate that, with this proposal, the inconsistencies are reduced compared to the reconfiguration algorithm according to the ETSI standard for VNF-FGs in multi-domain orchestration.

The European Telecommunications Standards Institute (ETSI) established the key reference architectural framework for Network Function Virtualization (NFV). The NFV framework supports several phases (e.g., design, deploy, and manage) that make up the life-cycle of Virtual Network Functions (VNFs) along with their connectivity through the Network Service (NS) concept. Furthermore, coupled with Software-Defined Networking (SDN), NFV provides a dynamic reshaping of the network control plane. For a proper NS provisioning, network providers should anticipate and handle all the possible and practical VNFs composition scenarios, even those that are either rarely or never used. This results in costly, tedious, and time-consuming procedures when designing, deploying, and managing NSs. To overcome these limitations, NSs can be further improved by using techniques that have not been deeply explored yet. In this paper, we advocate for routing VNFs with re-configurable wiring capabilities as a novel concept that would allow agile and dynamic VNFs wiring from design-time to run-time. To validate this work, we developed a proof-of-concept as a plugin on top of ETSI Open Source MANO, as well as, a running vehicle platooning prototype. The performed experiments show that our approach: (i) performs better than the SDN-based solution in terms of latency and Mean Time-To-Operation (MTTO), and (ii) shows that the associated cost, with regard to SDN-based solution. remains acceptable.

Abstract This paper focuses on the problem of the hierarchical multi‐label classification of research papers, which is the task of assigning the set of relevant labels for a paper from a hierarchy, using reduced amounts of labelled training data. Specifically, we study leveraging unlabelled data, which are usually plentiful and easy to collect, in addition to the few available labelled ones in a semi‐supervised learning framework for achieving better performance results. Thus, in this paper, we propose a semi‐supervised approach for the hierarchical multi‐label classification task of research papers based on the well‐known Co‐training algorithm, which exploit content and bibliographic coupling information as two distinct papers' views. In our approach, two hierarchical multi‐label classifiers, are learnt on different views of the labelled data, and iteratively select their most confident unlabelled samples, which are further added to the labelled set. The success of our suggested Co‐training‐based approach lies in two main components. The first is the use of two suggested selection criteria (i.e., Maximum Agreement and Labels Cardinality Consistency) that enforce selecting confident unlabelled samples. The second is the appliance of an oversampling method that rebalances the labels distribution of the initial labelled set, which reduces the reinforcement of the label imbalance issue during the Co‐training learning. The proposed approach is evaluated using a collection of scientific papers extracted from the ACM digital library. Performed experiments show the effectiveness of our approach with regards to several baseline methods.

Abstract The emergence of the Internet of Things (IoT) in the medical field has led to the massive deployment of a myriad of medical connected objects (MCOs). These MCOs are being developed and implemented for remote healthcare monitoring purposes including elderly patients with chronic diseases, pregnant women, and patients with disabilities. Accordingly, different associated challenges are emerging and include the heterogeneity of the gathered health data from these MCOs with ever‐changing contexts. These contexts are relative to the continuous change of constraints and requirements of the MCOs deployment (time, location, state). Other contexts are related to the patient (medical record, state, age, sex, etc.) that should be taken into account to ensure a more precise and appropriate treatment of the patient. These challenges are difficult to address due to the absence of a reference model for describing the health data and their sources and linking these data with their contexts. This article addresses this problem and introduces a semantic‐based context‐aware system (IoT Medicare system) for patient monitoring with MCOs. This system is based on a core domain ontology (HealthIoT‐O), that is, designed to describe the semantic of heterogeneous MCOs and their data. Moreover, an efficient interpretation and management of this knowledge in diverse contexts are ensured through SWRL rules such as the verification of the proper functioning of the MCOs and the analysis of the health data for diagnosis and treatment purposes. A case study of gestational diabetes disease management is proposed to evaluate the effectiveness of the implemented IoT Medicare system. An evaluation phase is provided and focuses on the quality of the elaborated semantic model and the performance of the system.

The systematic scaling approach is used in the literature as the only option to meet Quality of Service (QoS) requirements in response to the traffic load increase in IoT platforms. Such a solution reduces the load on the congested nodes and leads to an increase in the provisioning costs that, when measured at the scale of billions of devices, may hinder acceptability. Our objective is to enhance the existing approaches using and extending the emerging concept of virtualized network functions (VNFs), promoting End-to-End IoT traffic control. We start from the observation that, within Cloud-enabled platforms, the allocated resources are not fully used, making it possible to deploy traffic control network functions (NFs). Instead of systematically scaling a congested node, we dynamically deploy, on under-loaded nodes, additional NFs that exploit the available computing resources to differentiate the traffic processing level and to apply a QoS-oriented policy. The considered NFs extend the notion of VNF defined within NF virtualization (NFV), to take advantage of component-based software design. To enable a possible use of the implemented functions, we formulate a multiobjective optimization problem for the efficient planning of adequate NFs and scaling actions, according to the considered multiconstrained context. The planner, called QoS4NIP (QoS for NFV-enabled IoT Platforms), is based on a genetic algorithm (GA). The GA metaheuristic relies on biologically inspired operators such as mutation, crossover, and selection. QoS4NIP uses the GA to generate solutions to the identified multiobjective optimization problem by making a series of improvements in an iterative process. The experiments conducted on a realistic case study show that QoS4NIP outperforms autoscaling-based approaches (65% financial cost saved).

Industrial cyber-physical systems (ICPS) are heterogeneous inter-operating parts that can be physical, technical, networking, and even social like agent operators. Incrementally, they perform a central role in critical and industrial infrastructures, governmental, and personal daily life. Especially with the Industry 4.0 revolution, they became more dependent on the connectivity by supporting novel communication and distance control functionalities, which expand their attack surfaces that result in a high risk for cyber-attacks. Furthermore, regarding physical and social constraints, they may push up new classes of security breaches that might result in serious economic damages. Thus, designing a secure ICPS is a complex task, since this needs to guarantee security and harmonize the functionalities between the various parts that interact with different technologies. This article highlights the significance of cyber-security infrastructure and shows how to evaluate, prevent, and mitigate ICPS-based cyber-attacks. We carried out this objective by establishing an adequate semantics for ICPS’s entities and their composition, which includes social actors that act differently than mobile robots and automated processes. This article also provides the feasible attacks generated by a reinforcement learning mechanism based on multiple criteria that selects both appropriate actions for each ICPS component and the possible countermeasures for mitigation. To efficiently analyze ICPS’s security, we proposed a model-checking-based framework that relies on a set of predefined attacks from where the security requirements are used to assess how well the model is secure. Finally, to show the effectiveness of the proposed solution, we model, analyze, and evaluate the ICPS security on two real use cases.

Several works in the literature have proposed migration mechanisms for VNFs, however they only consider the migration of isolated Virtual Network Functions (VNFs) instead of the migration under a shared and chained scenario. Reconfiguration of VNFs, like in the case of migration, is necessary to handle dynamic requirements for the service. However, it is not a straightforward operation. On one hand, it is necessary to coordinate VNFs to achieve migration while maintaining the end-to-end service availability. On the other hand, the new deployment can disrupt the chain and violate predefined services constraints. Moreover if there is no access to global references, migration can introduce inconsistent services due to a lack of knowledge from orchestrators. This paper focuses on the problem of coordinating orchestrators in a NFV federation to achieve migration of shared VNFs with only local domain information. It introduces a novel coordination algorithm that relies on ETSI/MANO specifications. The proposed algorithm is implemented and evaluated for validation purposes. Results show that migration satisfies the constraints for seamless migration and the algorithm obtains better performance compared to state of the art solution with a slight overhead cost when considering the shared VNFs instead of isolated VNFS.

Smart Systems are composed of interconnected nodes (devices, services, components, etc.) that can be intelligent, mobile, embedded, cloud-based, or/and adaptive. These nodes interact together through new communication protocols. Architectures of such system needs to adapt its behavior and deals with change in the environment. An adaptive and reconfigurable architecture can repair itself if any execution problems occur, in order to successfully complete its own execution. In the design of an adaptive and reconfigurable software system, several aspects have to be considered. Also other emerging areas such as Big data can also be used to solve traditional problems of Smart Systems, like scalability and performance. This special issue addresses these issues and is composed of seven papers. The paper of1 entitled “Efficient monitoring for intrusion detection in wireless sensor networks” addresses the design of a reliable and energy-efficient monitoring system that resists internal security attacks based on a distributed smart monitoring protocol. The paper of2 entitled “Cloud SLA negotiation and re-negotiation: An ontology-based context-aware approach” defines a context-aware system that automates the negotiation of dynamically generated and adapted SLAs using reasoning techniques and semantic mapping between client requests and provider offers. The paper of3 entitled “Cloud service composition using minimal unsatisfiability and genetic algorithm” targets the run-time phase of adaptive systems. It addresses the dynamic properties of service-oriented adaptive systems by provide a hybrid approach for automatic WS composition based on formal methods and genetic algorithms. The paper of4 entitled “A knowledge-based approach to manage configurable business processes” targets both run-time and design-time as well as structural and behavioral properties of enterprise systems modelled and enacted as Business Processes (BP). It proposes a knowledge-based approach for capturing the different versions of a BP into a single configurable BP and splitting this process into fragments as an alternative of handling multiple BP versions. The paper of5 entitled “Avoiding resource misallocations in business processes” presents an approach for avoiding resource misallocations, considered as an improper combination of resources, in Business Processes (BPs). The paper of6 entitled “Simulating systems of systems using situation/reaction paradigm” targets the designs-time phase of adaptive systems. It considers the paradigm of Systems of Systems that can be applied to different smart and adaptive systems. The paper sees these systems from the mission point of view to characterise their evolving rules with formal modelling and simulation. The paper of7 entitled “A comprehensive survey on modeling of cyber-physical systems” provides a survey of existing approaches to modeling CPS. It aims to study and to classify the CPS properties and to discuss their importance in different application domains. We would like to thank CCPE Editor-in-Chief Dr. Geoffrey C. Fox for his strong support. A special thank goes to all authors for their valuable contributions to this special issue. Special thanks go to all the guest eidtorial board members for their comments that helped in enhancing the quality of the papers.

This article deals with anomaly detection for microservices-based applications during elastic treatment. In elastic treatment, scaling-up resources is based on threshold. Many studies consider that threshold exceeding is caused by the increase in requests number. However, this exceeding may be caused by many problems such as specific requests requiring a lot of resources or issues related to VMs and containers. That's why, when thresholds are exceeded we propose to apply an analysis treatment that detects and identifies the root cause of the threshold exceeding, either it's caused by a problem such as specific request, VM issue, container issue or it's caused by a normal increase in request's number. This paper presents "DOCKERANALYZER" a software module that detects and identifies execution problems in microservices context. Experimental measurements have been conducted on an IOT platform as a real use-case presenting realistic problems and demonstrating the effectiveness of our proposed solution.

The adoption of the Internet of Things (IoT) drastically witnesses an increase in different domains and contributes to the fast digitalization of the universe. Henceforth, next generation of IoT-based systems are set to become more complex to design and manage. Collecting real-time IoT-generated data unleashes a new wave of opportunities for business to take more precise and accurate decisions at the right time. However, a set of challenges, including the design complexity of IoT-based systems and the management of the ensuing heterogeneous big data as well as the system scalability, need to be addressed for the development of flexible smart IoT-based systems. Consequently, we proposed a set of design patterns that diminish the system design complexity through selecting the appropriate combination of patterns based on the system requirements. These patterns identify four maturity levels for the design and development of smart IoT-based systems. In this article, we are mainly dealing with the system design complexity to manage the context changeability at runtime. Thus, we delineate the autonomic cognitive management pattern, which is at the most mature level. Based on the autonomic computing, this pattern identifies a combination of management processes able to continuously detect and manage the context changes. These processes are coordinated based on cognitive mechanisms that allow the system perceiving and understanding the meaning of the received data to make business decisions, as well as dynamically discovering new processes that meet the requirements evolution at runtime. We demonstrated the use of the proposed pattern with a use case from the healthcare domain; more precisely, the patient comorbidity management based on wearables.

Wireless Sensor Networks (WSNs) have been recognized as a promising communication technology for the Internet of Things (IoT). In particular, smart grid applications rely on WSNs for enabling pervasive monitoring and control of the electric grid. However, these applications are commonly deployed in harsh environments that adversely impact the reliability of low-power wireless links in WSNs. Efficient link quality estimation has been shown as a prerequisite to overcome link unreliability. Several WSN Link Quality Estimators (LQEs) have been proposed in the literature. However, there is a lack of real world experimentations that investigate their adequacy to assess low-power links in smart grid environments. To fill this gap, this paper presents a thorough experimental study of representative LQEs in a smart grid distribution substation. Both single and composite LQEs are evaluated in terms of reliability, stability and reactivity, by analyzing their statistical behavior. This study would help system designers choose the most appropriate estimators for smart grid environments. Especially, it shows that composite LQEs, such as Opt-FLQE, F-LQE, and four-bit, are more reliable than single LQEs, including PRR, WMEWMA, and RNP. Further, experimental results show that Opt-FLQE is found to be the most reliable estimator, F-LQE, PRR, and WMEWMA are the most stable estimators, while Opt-FLQE, RNP, and four-bit are the most reactive LQEs.

Mobile healthcare applications highly depend on healthcare data, which is collected from wearable or implantable sensors. However, sensor readings may be inaccurate due to resource-constrained devices, sensor misplacement, patient with smearing, and other environmental related causes. Analyzing healthcare data is of paramount importance to provide high quality-care services and reduce false medical diagnosis. In this paper, we propose an online approach to detect inaccurate measurements and to raise alerts only when patients seem to be in emergency situations. The proposed approach is based on robust principal component analysis and adaptive threshold for multivariate anomaly detection, and on contribution plots for univariate anomaly diagnosis. We apply our proposed approach on real medical dataset. Our experimental results prove the effectiveness of our approach in detecting and diagnosing anomalous physiological measurements. The reduced time and space complexities of our approach make it useful and efficient for real time mobile health applications.

The development of the Semantic Web of Things (SWoT) is challenged by the nature of IoT deployment architectures, where constrained devices collect data processed remotely by powerful Cloud servers. Such a deployment pattern introduces bottlenecks constituting a hurdle for scalability, and increases response time. This hinders the development of a number of critical and time-sensitive applications. Enabling the deployment of the Semantic Web stack closer to the constrained devices of the IoT may foster the development of time-sensitive interoperable applications, while reducing forwarding the user data to remote third party Cloud servers. The approach we develop in this paper is a contribution towards this direction, and aims to enable rule-based reasoning closer to sensors producing IoT data. For this purpose, we define a distributed scalable semantic processing algorithm by dynamically propagating deduction rules on Fog nodes. Our goal is to shorten the time needed to deliver high level information deduced from the collected data. This approach is evaluated on a smart building use case where both distribution and scalability have been considered.

The goal of this track is to bring together researchers and practitioners both from the Academia and from the Industry working in the areas of the adaptation and reconfiguration of distributed systems. Different investigation topics are involved, such as: CBSE, Web service, cloud applications, mobile applications, Functional and Non-Functional requirements (QoS, performance, resilience), monitoring, diagnosis, decision and execution of adaptation and reconfiguration. Different research areas are covered: concepts, methods, techniques, and tools to design, develop, deploy and manage adaptive and reconfigurable software systems.

The Information-Centric Networking (ICN) paradigm is shaping the foreseen future Internet architecture by focusing on the data itself rather than its hosting location. It is a shift from a host-centric communication model to a content-centric model supporting among others unique and location-independent content names, in-network caching and name-based routing. By leveraging the easy data access, and reducing both the retrieval delay and the load on the data producer, the ICN can be a viable framework to support the Internet of Things (IoT), interconnecting billions of heterogeneous constrained objects. Among several ICN architectures, the Named Data Networking (NDN) is considered as a suitable ICN architecture for IoT systems. However, its default caching approach lacks a data freshness mechanism, while IoT data are transient and frequently updated by the producer which imposes stringent requirements in terms of information freshness. Furthermore, IoT devices are usually resource-constrained with harsh limitations on energy, memory and processing power. We propose in this paper a caching strategy and a novel cache freshness mechanism to monitor the validity of cached contents in an IoT environment while minimizing the caching process cost. We compared our solution to several relevant schemes using the ccnSim simulator. Our solution exhibits the best system performances in terms of hop reduction ratio, server hit reduction ratio and response latency, yet it provides the lowest cache cost and significantly improves the content validity.

This article describes how the named data networking (NDN) has recently received a lot of attention as a potential information-centric networking (ICN) architecture for the future Internet. The NDN paradigm has a great potential to efficiently address and solve the current seminal IP-based IoT architecture issues and requirements. NDN can be used with different sets of caching algorithms and caching replacement policies. The authors investigate the most suitable combination of these two features to be implemented in an IoT environment. For this purpose, the authors first reviewed the current research and development progress in ICN, then they conduct a qualitative comparative study of the relevant ICN proposals and discuss the suitability of the NDN as a promising architecture for IoT. Finally, they evaluate the performance of NDN in an IoT environment with different caching algorithms and replacement policies. The obtained results show that the consumer-cache caching algorithm used with the Random Replacement (RR) policy significantly improve NDN content validity in an IoT environment.

System of Systems (SoS) is a new class of complex software systems resulting from the integration of several independent systems working together. Within a SoS, many participant systems may be integrated and deleted operationally over the time. Each system has an Architecture Model modeled at design time. Thus, the SoS' software architecture description is represented by an aggregated Architecture Model. This aggregated Architecture Model represents participant systems but not necessarily their interactions and communications over the time. In literature, several research studies addressed issues related to SoS. However, we noticed a lack of studies that address the problem of how to describe the whole SoS' software architecture for each change of a participant system over the time. Moreover, studies dealing with checking conformity between the whole SoS' software architecture description and the aggregated Architecture Model, are still lacking. This paper presents an approach for the discovery of SoS' software architecture description from execution traces. For this purpose, the proposed approach records execution traces of all participant systems belonging to the SoS, their interactions and communications in a data base. Then, our approach relies on mining techniques to extract software architecture from the data base and describes it via a model called Architecture Model. In addition, this paper offers a solution for checking conformity between the aggregated Architecture Model and the Discovered Model. The diagnosis results may suggest new rules/constraints to enhance the aggregated Architecture Model.

Information-Centric Networking (ICN) is an emerging network paradigm based on name-identified data objects and in-network caching. Therefore, ICN contents are distributed in a scalable and cost-efficient manner. With the rapid growth of IoT traffic, ICN is intended to be a suitable architecture to support IoT networks. In fact, ICN provides unique persistent naming, in-network caching and multicast communications which reduce the data producer load and the response latency. Using ICN in an IoT environment requires a study of caching policies in terms of cache placement strategies and cache replacement policies. To this end, we address, in this paper, caching challenges with the aim to identify which caching policies are suitable for IoT networks. Simulation findings show that the combination of the consumer-cache caching strategy and the RR cache replacement policy is the most convenient in IoT environments in terms of hop reduction ratio, server hit reduction and response latency.

Our objectives involve the development of correct by design models and operational solutions to discover, compose and manage, by automated procedures, the properties of adaptability essential for autonomous reconfiguration. These properties are critical for highly dynamic complex systems such as massive data collection, transport and processing systems, and associated IoT applications. This research direction is of interest for many applications that, on the one hand, have strong reconfiguration requirements to manage mobility, ubiquity, adaptation and self-healing, which are essential for autonomy, and that, on the other hand, are subject to non-functional constraints of quality of service, robustness and reliability. We propose, here, to develop multi-level reconfiguration solutions (VM clusters, application containers, message partitions), in order to manage simultaneously and consistently the adaptation in the different architectural levels. This is necessary for service continuity and end-to-end quality we implemented by coordinating the adaptation actions to satisfy the evolving of the context.

Scientific research teams have immense valuable knowledge that need to be managed. Organizing scientific contributions of team members constitutes a major challenge for the monitoring of knowledge evolution, team member’s competences discovery, and facilitating information retrieval processes. However, performing manual annotations is often time consuming and labor-intensive task, especially in case of complex annotation schemas. Currently, existing knowledge management systems focus on ensuring the scientific knowledge creation, sharing, organization and evaluation but don’t provide a way for helping researchers in the classification task. In this paper, we introduce a knowledge management system that offers an annotation service for researchers’ contributions by including some natural language processing techniques. The provided service process comprises four phases: (1) the semantic enrichment of domain ontology based on the extraction of background data from Babelnet knowledge base, (2) the automatic generation of candidate categories using the enriched domain ontology, (3) the forwarding of the pre-annotated papers to our web-based system to interact with researchers, and finally (4) the human revision of the generated annotations. Evaluation results show its advantage not only in reducing human effort and time consumption during the annotation task but also in improving annotations quality.

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

Wireless Sensor Networks (WSNs) have been recognized as a promising communication technology for smart grid monitoring and control applications. Unfortunately, environmental conditions in smart grids are complex and harsh (electromagnetic interference, obstructions, fading, etc.), which turns radio links extremely unreliable. Routing protocols play a crucial role to overcome low-power link unreliability in smart grid environments. Especially, RPL (IPv6 Routing Protocol for Low Power and Lossy Networks) is an IETF standard that is supposed to meet the requirements of WSN-based smart grid communications. RPL routing metric relies on link quality estimation through ETX (Expected Transmission Count) assessment, to identify high quality links for data delivery. However, ETX is not sufficiently accurate as it assesses a particular link aspect, number of packet retransmissions over the link, and ignores other important aspects such as channel quality. Consequently, as confirmed by recent experimental studies, RPL can fail to identify routing paths with high quality links leading to high packet loss rates. In this paper, we propose an alternative routing metric for RPL based on holistic link quality estimation, where several link metrics are combined. Based on COOJA simulations, we demonstrate that our proposed routing metric improves RPL performance over traditional routing metrics, including the RPL default metric, mainly in terms of packet loss ratio, end-to-end delay, energy efficiency, and topology stability.

Advances in the Web and healthcare data capture technologies have far-reaching benefits for the development of new clinical decision support systems that accelerate decisionmaking and generate personalized treatments.However, the diversity of healthcare data formats, the lack of computer interpretable representation of medical interventions, and the distribution of reliable medical knowledge sources constitute important barriers to better support the medical decision process.To deal with these issues, we propose the Treatment Plan Ontology (TPO) that formalizes medical interventions, and allows medical systems sharing and reasoning over them.This knowledge together with the acquired patient data are then reused by the autonomic processes that we have developed in order to timely detect anomalies and support the physicians in personalizing the patient treatment at the right time.We demonstrate the system efficiency through a use case for managing hyperglycemia in type 2 diabetes.

AROSA Track Report

This Editorial [1] was originally published bearing the title “Towards the optimal synchronization granularity for dynamic scheduling of pipelined computations on heterogeneous computing systems” which was taken from another article's title [2] due to a production error. The title is now corrected above, and in the Editorial itself. We apologize for this error.

Software Design Patterns provide architects and developers with reusable software elements helping them to master building complex software systems. Nevertheless, presented in an informal way, software design patterns may give rise to ambiguity and may lead to their incorrect usage as well as incorrect compositions. In this paper we focus on SOA design patterns composition and we propose a rigorous composition method based on two steps. Firstly, we outline a precise definition of the composition process with the semi-formal SoaML standard language. Secondly, we outline a formal composition process using Event-B method. Our approach covers both structural and behavioral features of composed patterns.

Systems-of-Systems (SoS) refer to a new class of software-intensive systems, where their constituent systems work cooperatively in order to fulfill specific missions. Characterized by managerial and operational independence, geographic distribution, evolutionary development, and emergent behavior, SoS bring substantial challenges to the software engineering area. SESoS 2015, held in Florence, Italy, on May 17, 2015, as a joint workshop of the 37th International Conference on Software Engineering (ICSE), provided a forum to exchange ideas and experiences, analyze current research and development issues, discuss promising solutions, and to explore inspiring visions for the future of Software Engineering (SE) for SoS.

Several Link Quality Estimators (LQEs) have been proposed for Wireless Sensor Networks. However, their adequacy to smart grid environments has not been properly investigated. This paper addresses the problem of efficient low-power link quality estimation for smart grid environments. The first part of this paper presents a performance study of representative LQEs, namely ETX, four-bit and F-LQE, in three typical smart grid environments. These LQEs are evaluated in terms of reliability, stability and reactivity, by analyzing their statistical behavior. This study shows that F-LQE is more reliable and more stable than ETX and four-bit. However, it is not the most efficient for smart grid due to the lack of reactivity and also its higher complexity. Hence, the second part of this paper introduces Opt-FLQE, an optimized version of F-LQE that overcomes its limitations. The performance analysis of Opt-FLQE shows that it is more reactive than F-LQE while still being more reliable.

Information-Centric Networking (ICN) is a new paradigm proposing a shift in the main Internet architecture from a host-centric communication model to a content-centric model. ICN architectures target to meet user demands for accessing the information regardless of its location. A major building block of ICNs concerns caching strategies. Concomitantly, Machine-to-Machine (M2M) technologies are considered the main pattern for the Internet of Things (IoT). Unifying M2M and ICN into a single framework raises the challenge of cache coherence. In this paper, we propose a novel cache coherence mechanism to check the validity of cache contents. We also propose a caching strategy suitable to M2M environment. Extensive experimentations are conducted to evaluate the performance of our proposals. They show that the combination of our two proposed schemes results in a notable improvement in content validity at the expenses of a certain degradation in both server hit and hop reduction ratios.

Methodologies for correct by construction reconfigurations can efficiently solve consistency issues in dynamic software architecture. Graph-based models are appropriate for designing such architectures and methods. At the same time, they may be unfit to characterize a system from a non functional perspective. This stems from efficiency and applicability limitations in handling time-varying characteristics and their related dependencies. In order to lift these restrictions, an extension to graph rewriting systems is proposed herein. The suitability of this approach, as well as the restraints of currently available ones, are illustrated, analysed and experimentally evaluated with reference to a concrete example. This investigation demonstrates that the conceived solution can: (i) express any kind of algebraic dependencies between evolving requirements and properties; (ii) significantly ameliorate the efficiency and scalability of system modifications with respect to classic methodologies; (iii) provide an efficient access to attribute values; (iv) be fruitfully exploited in software management systems; (v) guarantee theoretical properties of a grammar, like its termination.

Organizational studies and analyses of distributed engineering processes and other cooperative activities in the areas of training, expertise or distance education show business needs into solutions for organizing their activities around a workspace that can be shared or distributed and virtual. In this era of global competitiveness, the economic space reflects a constantly reorganizing movement, in which companies are globalized and internationalized 1. And this leads in both cases to an organization in teams, units or task forces increasingly geographically distributed. The procedures for design and engineering are being redefined to fit the new organization by anticipating solutions to avoid the disadvantages of this new organization, on one hand, and to benefit from its advantages on the other hand. This resulted in a need for research on a new organization of group activities, new generic software used in various fields of activities and new architectures for software specific to a particular area of activity. Different research areas are affected by this issue and are classified under the multidisciplinary theme of collaborative enterprises. Several research teams from the academic and industrial worlds have examined the emerging problems of the distribution of traditional engineering activities and the creation of new collaborative work procedures. Context-awareness in collaborative systems is being investigated for emergent technologies such as machine to machine (M2M) technologies 2. The multiplicity of requirements, both from the organizational and software point of views, for these studies makes them a sought after multidisciplinary field in which we can distinguish different disciplines, different applications and different views for each discipline and for each application. Autonomic computing, and cloud computing 3, service-oriented architecture and Web services for both real-time 4 and standard applications 5, as well as aspect-oriented programming 6 are becoming key technologies for designing and implementing collaborative systems. In an effort to disseminate the recent advances in this field and to stimulate discussion on the future research directions while enabling research interests toward the development and applications of collaborative systems, a special issue of Wiley Concurrency and Computation: Practice and Experience has been dedicated to recent developments in collaborative systems. This special issue presents a total of nine papers in the most active areas of research in collaborative systems. The paper by Nesrine Khabou et al. 7 titled ‘Threshold-based Context Analysis Approach For Ubiquitous Systems’ proposes an analysis approach for collaborative ubiquitous systems. The approach aims at analyzing context information and detecting significant abnormal changes using thresholds for tracking down context changes. The paper by Fatma Krichen et al. 8 titled ‘Development of Reconfigurable Distributed Embedded Systems with a Model-Driven Approach’ presents a methodology to design reconfigurable distributed real-time embedded systems with temporal and resources constraints. A model-driven engineering approach is adopted to handle the complexity of the design and implementation process. The paper by Mahdi Ben Alaya et al. 9 titled ‘FRAMESELF: An ontology-based framework for the self-management of M2M systems’ proposes a framework implementing the different steps of autonomic management for M2M networks. The approach uses representation based on ontologies and graphs to describe the M2M concepts and relationship. The paper by Mohamed Sellami et al. 10 titled ‘A Decentralized and Service-Based Solution for Data Mediation: The case for Data Providing Service Compositions’ deals with Web services and semantic mediation and addresses the problem of data heterogeneity in service composition. The authors propose a service-based approach for automatically inserting appropriate mediation services in Data-as-a-Service compositions to resolve structural heterogeneities in their data flow. The paper by Kevin Brown et al. 11 titled ‘Fine-grained filtering to provide access control for data providing services within collaborative environments’ deals with Web services for data retrieval and semantic annotation using ontology-based descriptions. The defined approach relies on a filtering ontology allowing to extend extensible access control markup language policies to reference filtering classes. The paper by Antonio Cuomo et al. 12 titled ‘Planting Parallel Program Simulation on the Cloud’ introduces a cloud-based concurrent simulation framework for efficient performance prediction of parallel programs. The accuracy and the validity are proven through a case study. The paper by Magdalena Punceva et al. 13 titled ‘Incentivising Resource Sharing in Social Clouds’ addresses cloud applications for social networking. An incentive approach is elaborated for resources sharing. The approach has a significant potential for improving resource utilization and making available additional capacity. The paper by Emiliano Tramontana et al. 14 titled ‘Providing QoS strategies and Cloud-integration to Web Servers by means of Aspects’ proposes an aspect-oriented approach for handling of QoS parameters. The approach is applied to improve the performances of Web servers. The paper by Nicola Capodieci et al. 15 titled ‘Context-awareness in the Deregulated Electric Energy Market: an Agent-based Approach’ proposes a context-aware agent-based approach for simulation. The case of energy market is studied for the future smart grids. Different context parameters are considered including weather forecasting. The guest editors of this special issue would like to thank all referees for providing thoughtful and knowledgeable reviews and for their substantial contributions to the final revised versions appearing in this special issue. They also would like to thank the Wiley Concurrency and Computation: Practice and Experience editor in chief, Professor Dr. Geoffrey Fox, for his valuable suggestions and all the authors who have submitted to this special issue.

Sensing coverage in wireless sensor networks is one of the most fundamental issues, which have been extensively addressed in the literature. It is viewed as one of the critical performance measures in large-scale sensor networks. In this context, researchers have designed several coverage protocols. Throughout the variety of research works in this topic, most interests focused purely on the coverage problem under the restrictive assumption that each deployed node is equipped with a GPS receiver that provides a sensor node with its accurate location. However, in some wireless sensor network applications GPS service may be inaccessible, unpractical and very expensive. Faced to this challenge, several GPS-less localization algorithms for wireless sensor networks have been proposed. Such localization algorithms enable sensor nodes to locate themselves with some degree of accuracy. In this paper, we address the issue of maintaining coverage from the perspective of a GPS-less localization. We particularly integrate two well-known coverage and GPS-less localization solutions, namely CCP and AT-Dist. This integration yields key insights for handling coverage and GPS-less localization in a unified framework in contrast to several existing approaches that address the two issues in isolation. To the best of our knowledge, this will be the first work that integrates those two solutions. Extensive simulations show the effectiveness of this integrated framework to provide guaranteed coverage and localization.

The goal of the AROSA track is to bring together researchers and practitioners both from the Academia and from the Industry working in the areas of Service-oriented and component-based software applications and architectures and addressing adaptation and reconfiguration issues.

With the development of Internet technologies, distributed and multimedia applications are more and more used. Because these applications have Quality of Service (QoS) expectations (bounded delay, guaranteed bandwidth, etc.), a set of QoS oriented protocols aimed at aiding end-to-end communications are proposed. Each one of these protocols offers one or several mechanisms. Selecting the best protocol with the best mechanism(s) according to the application requirements and network contexts is a challenge if we want to provide enough QoS to the final users. Also because contexts can evolve, for example when network characteristics change, the previous selected protocol can become obsolete. In this context, an adaptation is mandatory. In this paper, we show how the OSI transport layer can become autonomic in order to perform dynamically reconfiguration actions when it is needed. Our approach consists in including at the transport layer the MAPE loop of the autonomic computing proposal to monitor and analyze the communication in order to predict or to detect a QoS degradation induced by changes of network context, and to plan and execute dynamically new reconfiguration actions in order to keep the communication at the expected QoS level. To implement and evaluate our approach, we extend the new Multipath-TCP (MPTCP) protocol, which offers an extensible application oriented layer. This layer gives opportunity to deploy QoS-aware mechanisms. The test results show that the consumption time of autonomic selection and composition of these mechanisms at run time does not prevent enhancing the QoS, and encourage us to continue working in this approach.

The HIP-Based M2M Overlay Network (HBMON) is a virtual, self-organized and secure M2M network built on the top of Internet, composed of scattered mobile devices. A fundamental requirements of this overlay network is to ensure session survivability upon end-host movement. The Host Identity Protocol (HIP) provides a regular mobility support in our M2M Overlay network. However, HIP is not able to handle the simultaneous mobility case, where both communicating end-points simultaneously acquires a new topologically correct IP address. We propose in this paper a novel solution to manage the simultaneous mobility (also known as double jump) of M2M devices within our overlay. For this purpose we enhance the HIP rendez-vous server in order to fully manage the double jump case. We analytically evaluate the signaling cost of our solution. Then, we implement our double jump solution within the OMNeT++ network simulator. Finally, we evaluate the application recovery time of an M2M device experiencing a double jump situation.

Correction added

The Internet of things consists of a high amount of heterogeneous objects that are widely distributed and evolve frequently according to their context changes. Management of such a complex environment is costly in terms of time and money. Designing a context aware autonomic framework with capability of self-management is a challenge. This paper proposes FRAMESELF, a generic and extensible autonomic framework to self-manage ubiquitous environments based on ontologies, graph models, and reasoning rules. A smart metering use case is experimented to illustrate the proposed solution.

In this paper, we propose an analytical framework for monitoring and analyzing QoS of publish/subscribe systems on MANET. Our framework copes with intermittent connectivity frequently occurring in MANET and provides statistical methods allowing detecting QoS degradations affecting links between brokers at the middleware layer. Besides, our analytical framework identifies QoS degradation source which enables to repair the system. The proposed framework is extensively evaluated with simulation experiments. Simulations results show its efficiency.

To adapt communication in order to maintain the connectivity and the quality of communications in group-wide collaborative activities becomes challenging when considering mobile entities in a wireless environment, requiring responsiveness and availability of the communication system, and designing self-adaptable communication architecture remains a complex task. To simplify this task, semantic presents a very attractive alternative to support self-adaptation and context awareness. This paper proposes three ontologies to model different aspects related to communication (organizational, collaborative and contextual) and to forecast future problems in accordance with priorities. Adaptation actions are also proposed for self-adaptation in communicating architecture. These actions deal with different changing context parameters such as resource constraints and architecture evolution requirements. To illustrate the proposed ontologies and some adaptation actions, we use ERCMS (Emergency Response and Crisis Management Systems) scenarios of the ROSACE project as a case study from the more general group communication systems.

Abstract Web Services (WS) can be considered as the most influent enabling technology for the next generation of web applications. WS‐based application providers will face challenging features related to nonfunctional properties in general and to performance and QoS in particular. Moreover, WS‐based developers have to provide solutions to extend such applications with self‐healing (SH) mechanisms as required for autonomic computing to face the complexity of interactions and to improve availability. Such solutions should be applicable when the components implementing SH mechanisms are deployed on both or only one platform on the WS providers and requesters sides depending on the deployment constraints. Associating application‐specific performance requirements and monitoring‐specific constraints will lead to complex configurations where fine tuning is needed to provide SH solutions. To contribute to enhancing the design and the assessment of such solutions for WS technology, we designed and implemented a monitoring and measurement framework, which is part of a larger Self‐Healing Architectures (SHA) developed during the European WS‐DIAMOND project. We implemented the Conference Management System (CMS), a real WS‐based complex application. We achieved a large‐scale experimentation campaign by deploying CMS on top of SHA on the French grid Grid5000. We experienced the problem as if we were a service provider who has to tune reconfiguration strategies. Our results are available on the web in a structured database for external use by the WS community. Copyright © 2010 John Wiley & Sons, Ltd.

Service provisioning is a challenging research area for the design and implementation of autonomic service- oriented software systems. It includes automated QoS management for such systems and their applications. Monitoring and Measurement are two key features of QoS management. They are addressed in this paper as elements of a main step in provisioning of self-healing web services. In a previous work, we defined and implemented a generic architecture applicable for different services within different business activities. Our approach is based on meta-level communications defined as extensions of the SOAP envelope of the exchanged messages, and implemented within handlers provided by existing web service containers. Using the web services technology, we implemented a complete prototype of a service-oriented Conference Management System (CMS). We experienced our monitoring and measurement architecture using the implemented application and assessed successfully the scalability of our approach under the French grid5000. In this paper, experimental results are analyzed and concluding remarks are given.

In this paper we propose a UML based approach for describing and checking dynamic software architectures. In fact, the UML profile enables us to model dynamic software architectures by three aspects: the first one defines the architectural style of an application, the second describes a set of reconfiguration operations and the last one specifies the coordination among the reconfiguration operations defined in the above aspect. The built models are automatically checked through a set of syntactical rules. These models are automatically transformed into formal Z specifications in order to prove architectural properties such as consistency and invariants preservation against reconfiguration.

Maintaining the Quality of Service (QoS) is important for self-healing web service-based distributed interactive applications. It requires the ability to deal with permanently changing constraints both at the communication and the execution levels. Preventing or repairing QoS degradation also requires the capacity of identifying its possible or actual sources and the capacity of reconfiguration decision and enforcement. Dealing with these issues is especially challenging for web services since the self-healing solution has to preserve the dynamic composition property and to be seamless for the service requesters, while being always usable under the different deployment constraints. In this paper, we present a self-healing middleware framework able to provide the self-healing properties for QoS management in web service-based distributed interactive applications. The framework implementation has been achieved in the context of the WS-DIAMOND project. It covers the whole cycle of adaptation management including monitoring and analysis of QoS values, and substitution-based reconfiguration.

Failures during Web service execution may depend on a wide variety of causes, such as network faults, server crashes, or application-related errors, such as unavailability of a requested Web service, errors in the orchestration or choreography of applications, missing data or parameters in an execution flow, or low quality of service (QoS). Providing efficient solutions requires handling of adaptability not only at the behavioral level, but also at the architectural level. Most of existing standard solutions focus on the behavioral level using reconfiguration mechanisms. In this paper, we propose a model-based strategy providing adaptability actions at the architectural level. Typical architectural reconfiguration actions, such as duplication of services or substitution of a faulty service are introduced. An scenario is illustrated and discussed with respect to a running example involving coordinated Web services.

Architectural style constitutes a mean to put in practice design approaches based on pattern refinement, adaptation and reuse. Our objective is to support such approaches. In this work, we focus on real-life architectural styles and more particularly on the event-based style. We provide a set of basic styles and we describe a formal approach to specify them and to compose them to generate more complex patterns. In this way, we help the architect to design correct and elaborated architectural styles that can serve as a starting point for a more correct and successful refinement process.

The design of wireless cooperative mobile service-oriented applications with machine-to-machine communication capabilities requires providing solutions that include service and Quality of Service (QoS) provisioning as key features. Self-adaptability and self-organization are properties that help managing services and associated QoS for communication, vertically and horizontally. This paper addresses self-adaptability as a key feature for model-oriented and architecture-driven QoS management. We study the different abstraction levels at which the adaptability can be managed. Such decomposition allows mastering the complex handling of self-adaptive group-wide QoS for cooperative activities supported by wireless and mobile ad-hoc communications. A case study based on cooperative group-activity support is developed to illustrate the proposed concepts.

This paper presents an analysis of the main requirements to support synchronous collaborative activities. Then, a collaborative platform, PLATINE, composed of a complete set of tools for synchronous group work support and collaboration session management, is presented. Focus is made on two important components: videoconference and session manager. Afterwards, a set of experiments is described. A co-design scenario in the electronic domain is detailed.

This paper presents a design framework and a comparative survey of the tools, environments and approaches for collaborative authoring. The defined framework identifies three interaction levels: cooperation, coordination and communication. For each level, different points of views are distinguished to specify interaction and architecture styles. This paper presents, as an application of this framework, SEdit a distributed collaborative authoring tool allowing distributed group editing and reviewing of shared Web documents.

This paper defines a coordination model allowing different collaboration modalities to be managed by maintaining consistency of the distributed management actions at the user and collaborative tool levels. The model was achieved to manage multi-tools and multi-user collaboration sessions during the DSE project for design review activities in space domain. We have identified and formalized three coordination relations that guarantee maintaining the consistency. Our model is used to implement a software component for collaborative session management. We proceed by control of user-level management actions and by automated execution of tool-level management actions.

This paper presents algorithms for distributed coin tossing. Distributed coin tossing furnishes a symmetrical way to break the symmetry in a distributed system. They allow the conflicts arising from competing remote sites to be overcome in a fair (equiprobable) manner: any site has an equal chance of being selected. It as also shown how these algorithms may be used to obtain a total order between competing sites of a system. Moreover, in each case the outputs of our algorithms are totally unpredictable.>

A testability assessment approach for multicomponent systems is proposed within the framework of formal description techniques. The approach relies on the formal definition of implementation conformance with respect to a given specification. It provides measures of user confidence and guidelines for an automatic design analysis. Both items are based on test requirements. The proposed technique for testability assessment formalizes the common-sense statement that testability of a component is degraded when this component cannot be tested in isolation. Furthermore, it gives a precise answer to the question: What are the limits of a test taking into consideration the constraints of a given environment?.>