Abstract Interference between tasks running on separate cores in multi-core processors is a major challenge to predictability for real-time systems, and a source of over-estimation of worst-case execution duration bounds. This paper investigates how the multi-phase task model can be used together with static scheduling algorithms to improve the precision of the interference analysis. The paper focuses on single-period task systems (or multi-periodic systems that can be expanded over an hyperperiod). In particular, we propose an Integer Linear Programming (ILP) formulation of a generic scheduling problem as well as 3 heuristics that we evaluate on synthetic benchmarks and on 2 realistic applications. We observe that, compared to the classical 1-phase model, the multi-phase model allows to reduce the effect of interference on the worst-case makespan of the system by around 9% on average using the ILP on small systems, and up to 24% on our larger case studies. These results pave the way for future heuristics and for the adoption of the multi-phase model in multi-core context.

This paper investigates how the multi-phase representation of real-time tasks impacts their implementation and the precision of the interference analysis in a multi-core context. In classical scheduling and interference analyses, tasks are represented as a single phase with a duration equal to their Worst-Case Execution Time (WCET) in isolation, annotated with their worst-case number of accesses. We propose a general formal definition of a task model in which tasks are represented as a sequence of such phases: the multi-phase model. We then provide a set of general correction criteria for the implementation of tasks represented in the multi-phase model, which is agnostic of the analysis method applied on the tasks. We also use the multi-phase model on an avionics case-study and study its impact on the interference analysis. Finally, we define a set of efficiency criteria using a statistical study of the most efficient multi-phase shapes.

The Internet of Things (IoT) paradigm is defined as a complex large scale and distributed, and dynamic infrastructure composed of a huge number of heterogeneous devices. Identifying particular services provided by a massive number of IoT devices remains a challenging problem. The classical centralized discovery approaches are no more suitable. In our previous work, we have proposed an avatar-based Fog-Cloud architecture to support IoT object management. The avatars are defined as virtual entities of heterogeneous IoT objects. They are endowed with reasoning capabilities that make them able to coordinate with each other to accomplish an IoT application. Through this paper, we propose to extend our previous work by a new distributed mechanism for efficient discovery of IoT services relying on Social Networking (SN) and clustering methods. This is particularly interesting in large scale IoT systems since it allows to reduce the search space so that only the neighboring social avatars most apt to participate in the collaboration to accomplish an IoT application are considered. The proposed solution has been evaluated in connected vehicles context.

The Internet of Things (IoT) encompasses both large-scale deployed physical infrastructures and software layers that enable intuitive and transparent creation of applications. This highly distributed, energy-greedy environment must ensure the quality of deployed services while taking into account the heterogeneity of capabilities and protocols as well as users and objects mobility. Deployment infrastructure has been redesigned to provide the necessary features, including paradigms such as software-defined networks and Fog computing. The purpose of this article is to study IoT services placement in a Fog architecture. We propose a model of the infrastructure and IoT applications as well as a placement strategy taking into account system's energy consumption and applications delay violations minimization with a Discrete Particles Swarm Optimization algorithm (DPSO). Simulations have been done with iFogSim simulator. Results have been compared with heuristics coming from the literature: Binary Partical Swarm optimization (BPSO), Dicothomous Module Mapping (DCT), CloudOnly, IoTFogOnly, IoTCloud (IC) and FogCloud (FC) placement approaches.

The Internet of Things (IoT) continues to expand undeniably fast to reach billions of connected heterogeneous devices. This is changing the way systems are built: new applications integrating software and physical devices are emerging in different domains, such as health, smart building, and smart cities. This brings opportunities to enable new added value services. Nevertheless, building and managing such highly dynamic and heterogeneous infrastructures built upon a multitude of mobile and resource-limited devices is challenging. In this paper, we propose a semantic based autonomic management approach for service oriented IoT systems. The aim is to support building and managing highly dynamic new value added IoT services. The proposed approach relies on a semantic based model to characterize the system properties and then enables semantic reasoning, and graph grammars to enable its management and evolution. A use case is proposed to show the related features of the proposed approach and an evaluation study is presented.

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 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.

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.

Thanks to the advent of the Internet of Things (IoT) concept, several areas have been extended to offer a new category of “smart” services, such as smart home, smart cities, smart grid or smart factory. The IoT is based on communicating devices (sensors, actuators, RFID tags) that are remotely manageable by business applications. In this new context, several traditional challenges have to be reconsidered, among them the quality of service (QoS) issue to meet the application requirements. As a part of our approach for an autonomic QoS management at the Middleware level within the IoT, we first propose an analytical model based on queueing theory of a Middleware platform, aimed at guiding the monitoring and planning steps of the proposed autonomic management. This model is then instantiated for an IoT entity (here a Gateway). We finally show the benefits induced by the use of this model to guide the activation of two adaptation actions aimed at satisfying QoS constraints of critical IoT application still optimizing energy consumption of the involved IoT entity.

Low Power Wide Area Networks (LPWAN) technologies have recently triggered many research efforts and standardization activities due to the inherent possibility of both providing long range wireless communications and guaranteeing a long life for very cheap sensing devices. At the same time, the increasing interest of telco operators into such kind of networks is due to the wide range of applications that can be supported by LPWAN, including low power radiolocation. Among the available LPWAN access schemes, LoRaWAN protocols enable a hierarchical network structure over sub-gigahertz unlicensed spectra, with coordinated gateways listening for data delivered by battery-operated end-devices. A proper positioning scheme for gateways enables radiolocation duties through multilateration. However, the LoRaWAN Aloha-based capacity puts an implicit restriction on the radiolocation performances, while explicit duty cycle policies further limit the resource availability on unlicensed frequencies. To characterize the throughput of low power radiolocation applications, this contribution introduces a scalable and detailed probability model, while validating the same through a large simulation campaign. The results clearly show that the availability of multiple channel enhances the capacity of LoRaWAN networks while insuring predictable time delay between consecutive successful radiolocation events.

Typical devices of the Internet of Things are usually under-powered, and have limited RAM. This is due to energy and cost concerns. Yet, IoT applications require increasingly complex programs with increasingly large amounts of data. In principle, an application could manage the increasing data within the limited RAM by saving and loading data from the file system as needed. But managing the use of RAM in this way is both time-consuming and error-prone for the code developer. We propose instead a novel architecture in which different semantic scenes are implemented as independent operating system processes. As the need arises to switch from one scene to another, the currently running process, which represents the current scene, is checkpointed and a process representing the new scene is restarted from a checkpoint image. This solution employs checkpointing to provide a simpler framework for the end programmer, while at the same time resulting in higher performance. For example, experiments show that restarting an old process from a checkpoint image is about 25 times faster than starting a new process. When using an mmap-based optimization (deferring the paging in of virtual memory pages until runtime), restarting an old process is about 500 times faster. Overall, checkpoint and restart each execute in less than 0.2 seconds on a Raspberry Pi B.

The use of Complex Event Processing (CEP) and Stream Processing (SP) systems to process high-volume, high-velocity Big Data has renewed interest in procedures for managing these systems. In particular, self-management and adaptation of runtime platforms have been common research themes, as most of these systems run under dynamic conditions. Nevertheless, the research landscape in this area is still young and fragmented. Most research is performed in the context of specific systems, and it is difficult to generalize the results obtained to other contexts. To enable generic and reusable CEP/SP system management procedures and self-management policies, this research introduces the Attributed Graph Rewriting for Complex Event Processing Management ( AGeCEP ) formalism. AGeCEP represents queries in a language- and technology-agnostic fashion using attributed graphs. Query reconfiguration capabilities are expressed through standardized attributes, which are defined based on a novel classification of CEP query operators. By leveraging this representation, AGeCEP also proposes graph rewriting rules to define consistent reconfigurations of queries. To demonstrate AGeCEP feasibility, this research has used it to design an autonomic manager and to define a selected set of self-management policies. Finally, experiments demonstrate that AGeCEP can indeed be used to develop algorithms that can be integrated into diverse CEP systems.

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.

Summary Machine‐to‐machine (M2M) systems need to connect thousands of heterogeneous machines that are widely distributed and frequently evolve according to their environment changes. Keeping such systems alive is costly in terms of time and money. The increasing complexity of M2M communications places M2M development and research under strain. Autonomic computing paradigm is widely believed to be a promising solution to self‐manage this complexity. This paper proposes FRAMESELF, an ontology‐based framework designed for the self‐configuration of M2M communications. A variety of communication modules based on service‐oriented and event‐driven communication patterns is dynamically selected and configured in deployment plans. A generic and extensible control loop components diagram detailing the monitoring, analyzing, planning, and executing processes are provided. Ontology models describing the M2M architecture and M2M communication patterns are proposed. A smart metering use case is experimented to validate the proposed approach and to calculate the overload generated by FRAMESELF facing scalability. Copyright © 2013 John Wiley & Sons, Ltd.

Summary This paper deals with the parallel computing and computation time prediction applied to computational electromagnetics based on rigorous methods for large and complex structures, such as tunnels and airplane cabins. The main objective of this work is represented by the electromagnetic computation of an airplane cabin in order to characterize the propagation channel for a given frequency inside the structure. A well‐known three‐dimensional numerical method, transmission‐line matrix, is adopted for this work. Transmission‐line matrix modelling method is applied to volumes, using the symmetrical condensed node. The planar structures are modelled with the mode matching approach. In order to analyse the electromagnetic field at high frequencies inside large structures as an airplane cabin, the transmission‐line matrix mesh step is very small compared with the size of the physical structure. Thus, the total amount of data that need to be processed exceeds the RAM memory presented on a traditional computing machine. The implementation of the parallel transmission‐line matrix algorithm is a viable solution to this problem taking full advantage of the parallel and distributed architectures, as clusters, grids and supercomputers. The results prove the benefits of the grid computing and supercomputer environments to solve electrically large structures. The parallel hybrid approach, based on Message Passing Interface, is designed for Single Programme Multiple Data programming model. The study highlights the role of parallelization scheme, grid versus supercomputer, with respect to the size of the problem and its repartition. In order to access the computing resources available on different parallel computing platforms, users need to respect a charter containing time and resource number restrictions when doing reservations. So, a time prediction model based on the profile of the transmission‐line matrix algorithm has been developed to allow safe reservations of the computing nodes on grid environment. To have a good agreement between the measured and the predicted values for the computation time, the problem of the cache misses appearing during the simulation has been considered in the profile‐based prediction model of the transmission‐line matrix/modal hybrid application, according to the processor cache memory size. The maximum error for the estimated values of the computation times is about 6.09%. The relevance of this work is based on the fact that is given a complete tool to deal with the large electromagnetic structures: a parallel transmission‐line matrix implementation to take the advantage of the distributed architectures and a time prediction model in order to optimize the resource reservation and the simulation execution on the computing nodes. Copyright © 2014 John Wiley & Sons, Ltd.

The heterogeneous nature of distributed platforms such as computational Grids is one of the main barriers to effectively deploy tightly-coupled applications. For those applications, one common problem that appears due to the hardware heterogeneity is the load imbalance which slows down the application to the pace of the slower processor. One solution is to distribute the load adequately taking into account hardware capacities. To do so, an estimation of the hardware capacities for running the application has to be obtained. In this paper, we present a static load balancing for iterative tightly-coupled applications based on a profile prediction model. This technique is presented as a successful example of the interaction between experiment management tools and parallel applications. The experiment management tool Expo is used that enabled to: (1) provide a general, lightweight and descriptive way to capture the tuning and deployment of a parallel application in a computing infrastructure, (2) perform the tuning of the application efficiently in terms of human effort and resources needed. This paper reports the costs for carrying out the tuning of a large electromagnetic simulation based on TLM for the platform Grid'5000 and the improvements obtained on the total execution time of the application.

Complex Event Processing (CEP) is a set of tools and techniques that can be used to obtain insights from high-volume, high-velocity continuous streams of events. CEP-based systems have been adopted in many situations that require prompt establishment of system diagnostics and execution of reaction plans, such as in monitoring of complex systems. This article describes the Query Analyzer and Manager (QAM) module, a first effort toward the development of a CEP as a Service (CEPaaS) system. This module is responsible for analyzing user-defined CEP queries and for managing their execution in distributed cloud-based environments. Using a language-agnostic internal query representation, QAM has a modular design that enables its adoption by virtually any CEP system.

This paper introduces the Caging track (Collaborative and Autonomic Green computing). Topics, program committee, selected papers are explained.

With the proliferation of data, components and services on the internet, cyber-physical systems are growing up these last years. Machine-to-Machine (M2M) is an example of a cyber-physical system where mobile communication is enabled between sensors, gateways and application services. M2M is used in several domains and in particular, in critical ones such as the healthcare domain. In such domain, requirements in term of Quality of Service (QoS) and particularly time related requirements in mobile environments must be rigorously respected. It is important to define an approach to ensure and validate these systems against these requirements. In this paper, we are interested in Publish/ Subscribe M2M communication based model. Our goal is to define a formal approach that aims at designing and validating M2M systems where we consider M2M mobile sensors and time related requirements. We define a graph based model to characterize Publish/ Subscribe M2M systems and model checking mechanisms to enable verification techniques.

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.

The study of the performance of parallel applications may have different reasons. One of them is the planning of the execution architecture to guaranty a quality of service. In this case, it is also necessary to study the computer hardware, the operating system, the network and the users behavior. This paper proposes different models for those based on simulation. First results of this work show the impact of the network and the necessary precision of communication model in fine-grained parallel applications.