Autonimic energy-aware task scheduling

International audienceThe increasing processing capability of data-centers increases considerably their energy consumption which leads to important losses for companies. Energy-aware task scheduling is a new challenge to optimize the use of the computation power provided by multiple resources. In the context of Cloud resources usage depends on users requests which are generally unpredictable. Autonomic computing paradigm provides systems with self-managing capabilities helping to react to unstable situation. This article proposes an autonomic approach to provide energy-aware scheduling tasks. The generic autonomic computing framework FrameSelf coupled with the CloudSim energy-aware simulator is presented. The proposed solution enables to detect critical schedule situations and simulate new placements for tasks on DVFS enabled hosts in order to improve the global energy efficiency

Sémantique et Internet des objets : d'un état de l'art à une ontologie modulaire

National audienceLa notion d'Internet des Objets désigne un réseau d'objets connectés entre eux et communiquant de manière automatique. Les notions de sémantiques y ont une place croissante, car plus que jamais elles ap-paraissent comme une solution aux problèmes d'interopérabilité et d'interprétation des données et des services par des machines. La diversité des applications possibles à l'intersection de l'internet des objets et du web sé-mantique a poussé de nombreuses équipes de recherche à travailler à l'interface entre ces deux disciplines. Nous souhaitons dans ce papier faire un inventaire de leurs propositions. Nous cherchons également à contribuer à l'évolution de ce domaine de recherche en proposant une ontologie pour décrire les réseaux d'objets connectés

Harnessing XMPP for Machine-to-Machine Communications & Pervasive Applications

An ever increasing number of interconnected embedded devices, or Machine-to-Machine (M2M) systems, are changing the way we live, work and play. M2M systems as a whole are typically characterized by the diversity in both the type of device and type of network access technology employed, and such systems are often still today task-specific and built for just one specific application. Smart lighting, remote monitoring and control of all kinds of consumer devices and industrial equipment, safety and security monitoring devices and smart health and fitness products, exemplify this revolution of intercommunicating machines. However, the differences in communication technologies and data formats among such devices and systems are leading to a huge complexity explosion problem and a strongly fragmented market, with no true interoperability. Due to these problems, the full potential of M2M technology has yet to be fulfilled. In this paper, we examine the suitability of the Extensible Messaging and Presence Protocol (XMPP) and experiment with its potential to rise to the challenge of machine-to-machine communications and meet the needs of modern pervasive applications. Experimental implementations and some proof-of-concept solutions are also presented

Vers l'interopérabilité, l'autogestion, et la scalabilité des systèmes Machine-to-Machine

Machine-to-Machine (M2M) is one of the main features of Internet of Things (IoT). It is a phenomenon that has been proceeding quietly in the background, and it is coming into the surface, where explosion of usage scenarios in businesses will happen. Sensors, actuators, tags, vehicles, and intelligent things all have the ability to communicate. The number of M2M connections is continuously increasing, and it has been predicted to see billions of machines interconnected in a near future. M2M applications provide advantages in various domains from smart cities, factories of the future, connected cars, home automation, e-health to precision agriculture. This fast-growing ecosystem is leading M2M towards a promising future. However, M2M market expansion opportunities are not straightforward. A set of challenges should be overcome to enable M2M mass-scale deployment across various industries including interoperability, complexity, and scalability issues. Currently, the M2M market is suffering from a high vertical fragmentation affecting the majority of business sectors. In fact, various vendor-specific M2M solutions have been designed independently for specific applications, which led to serious interoperability issues. To address this challenge, we designed, implemented, and experimented with the OM2M platform offering a flexible and extensible operational architecture for M2M interoperability compliant with the SmartM2M standard. To support constrained environments, we proposed an efficient naming convention relying on a non-hierarchical resource structure to reduce the payload size. To reduce the semantic gap between applications and machines, we proposed the IoT-O ontology for an effective semantic interoperability. IoT-O consists of five main parts, which are sensor, actuator, observation, actuation and service models and aims to quickly converge to a common IoT vocabulary. An interoperable M2M service platform enables one to interconnect heterogeneous devices that are widely distributed and frequently evolving according to their environment changes. Keeping M2M systems alive is costly in terms of time and money. To address this challenge, we designed, implemented, and integrated the FRAMESELF framework to retrofit self-management capabilities in M2M systems based on the autonomic computing paradigm. Extending the MAPE-K reference architecture model, FRAMESELF enables one to dynamically adapt the OM2M system behavior according to high level policies how the environment changes. We defined a set of semantic rules for reasoning about the IoT-O ontology as a knowledge model. Our goal is to enable automatic discovery of machines and applications through dynamic reconfiguration of resource architectures. Interoperability and self-management pave the way to mass-scale deployment of M2M devices. However, current M2M systems rely on current internet infrastructure, which was never designed to address such requirements, thus raising new requirements in term of scalability. To address this challenge, we designed, simulated and validated the OSCL overlay approach, a new M2M meshed network topology as an alternative to the current centralized approach. OSCL relies on the Named Data Networking (NDN) technique and supports multi-hop communication and distributed caching 5 to optimize networking and enhance data dissemination. We developed the OSCLsim simulator to validate the proposed approach. Finally, a theoretical model based on random graphs is formulated to describe the evolution and robustness of the proposed system.La communication Machine-to-Machine (M2M) est l'un des principaux fondements de l'Internet des Objets (IoT). C'est un phénomène qui a évolué discrètement au cours du temps et vient d’émerger à la surface pour do! nner naissance à une explosion de nouveaux usages et services. Capteurs, actionneurs, tags, véhicules et objets intelligents ont tous la possibilité de communiquer. Le nombre de connexions M2M est en constante augmentation et il est prévu de voir des milliards d’objets connectés dans un futur proche. Les applications M2M offrent des avantages dans divers domaines à savoir les villes intelligentes, les voitures connectées, les usines du futures, l’agriculture de précision, l’environnement, la santé, etc. La croissance rapide de cet écosystème est entrain de conduire le M2M vers un avenir prometteur. Cependant, les opportunités d'expansion des marchés M2M ne sont pas évidentes. En effet, un ensemble de challenges doivent être surmontés afin de permettre un déploiement à grande échelle dans des domaines diverses et variés à savoir les défis d’interopérabilité, de complexité et de scalabilité. Actuellement, le marché du M2M souffre d'une fragmentation verticale importante touchant la majorité des domaines industriels. En effet, diverses solutions propriétaires ont été conçues pour répondre à des applications spécifiques engendrant ainsi un sérieux problème d''interopérabilité. Pour adresser ce challenge, nous avons conçu, développer et expérimenté la plateforme OM2M offrant une architecture opérationnelle, flexible et extensible pour l'interopérabilité M2M conforme à la norme SmartM2M. Pour supporter les environnements contraints, nous avons proposé une nouvelle convention de nommage basée sur une structure de ressources non-hiérarchique permettant d’optimiser la taille des messages échangés. Pour assurer l’interopérabilité sémantique entre les applications et les machines, nous avons proposé l'ontologie IoT-O. Cette dernière est composée de cinq modèles de base représentant les capteurs, les actionneurs, les observations, les actuations et les web ! services pour permettre de converger rapidement vers un vocabulaire commun pour l'IoT. Une plateforme M2M horizontale permet d'interconnecter des machines hétérogènes largement distribués et qui évoluent fréquemment en fonction des changements de l’environnement. Maintenir ces systèmes complexes en vie est coûteux en termes de temps et d'argent. Pour adresser ce challenge, nous avons conçu, développé et intégré le framework FRAMESELF afin d'ajouter des capacités d'autogestion aux systèmes M2M basées sur le paradigme de l'informatique autonome. En étendant le modèle d'architecture de référence MAPE-K, notre solution permet d'adapter dynamiquement le comportement de la plateforme OM2M par en fonctions des changements du contexte et des politiques haut niveaux. Nous avons défini un ensemble de règles sémantiques pour faire du raisonnement sur l'ontologie IoT-O en tant que modèle de connaissance. Notre objectif est de permettre la découverte automatique entre les machines et les applications à travers un appariement sémantique et une reconfiguration dynam! ique de l'architecture des ressources. L’interopérabilité et l’autogestion ouvrent la voie à un déploiement de masse des systèmes M2M. Par contre, ces derniers se basent sur l'infrastructure actuelle d'internet qui n'a jamais été conçu pour ce genre de d'utilisation ce qui pose de nouvelles exigences en termes de scalabilité. Pour adresser ce challenge, nous avons conçu, simulé et validé l'approche OSCL proposant une nouvelle topologie de réseau maillé M2M comme alternative à l'approche centralisée actuelle. OSCL s'appuie sur les techniques de routage centrées sur l'information favorisant les communications à sauts multiples et un cache distribué pour une meilleure dissémination des données. Nous avons développé le simulateur OSCLsim pour valider l'approche proposée.[...

Vers l'interopérabilité, l'autogestion, et la scalabilité des systèmes Machine-to-Machine

Machine-to-Machine (M2M) is one of the main features of Internet of Things (IoT). It is a phenomenon that has been proceeding quietly in the background, and it is coming into the surface, where explosion of usage scenarios in businesses will happen. Sensors, actuators, tags, vehicles, and intelligent things all have the ability to communicate. The number of M2M connections is continuously increasing, and it has been predicted to see billions of machines interconnected in a near future. M2M applications provide advantages in various domains from smart cities, factories of the future, connected cars, home automation, e-health to precision agriculture. This fast-growing ecosystem is leading M2M towards a promising future. However, M2M market expansion opportunities are not straightforward. A set of challenges should be overcome to enable M2M mass-scale deployment across various industries including interoperability, complexity, and scalability issues. Currently, the M2M market is suffering from a high vertical fragmentation affecting the majority of business sectors. In fact, various vendor-specific M2M solutions have been designed independently for specific applications, which led to serious interoperability issues. To address this challenge, we designed, implemented, and experimented with the OM2M platform offering a flexible and extensible operational architecture for M2M interoperability compliant with the SmartM2M standard. To support constrained environments, we proposed an efficient naming convention relying on a non-hierarchical resource structure to reduce the payload size. To reduce the semantic gap between applications and machines, we proposed the IoT-O ontology for an effective semantic interoperability. IoT-O consists of five main parts, which are sensor, actuator, observation, actuation and service models and aims to quickly converge to a common IoT vocabulary. An interoperable M2M service platform enables one to interconnect heterogeneous devices that are widely distributed and frequently evolving according to their environment changes. Keeping M2M systems alive is costly in terms of time and money. To address this challenge, we designed, implemented, and integrated the FRAMESELF framework to retrofit self-management capabilities in M2M systems based on the autonomic computing paradigm. Extending the MAPE-K reference architecture model, FRAMESELF enables one to dynamically adapt the OM2M system behavior according to high level policies how the environment changes. We defined a set of semantic rules for reasoning about the IoT-O ontology as a knowledge model. Our goal is to enable automatic discovery of machines and applications through dynamic reconfiguration of resource architectures. Interoperability and self-management pave the way to mass-scale deployment of M2M devices. However, current M2M systems rely on current internet infrastructure, which was never designed to address such requirements, thus raising new requirements in term of scalability. To address this challenge, we designed, simulated and validated the OSCL overlay approach, a new M2M meshed network topology as an alternative to the current centralized approach. OSCL relies on the Named Data Networking (NDN) technique and supports multi-hop communication and distributed caching 5 to optimize networking and enhance data dissemination. We developed the OSCLsim simulator to validate the proposed approach. Finally, a theoretical model based on random graphs is formulated to describe the evolution and robustness of the proposed system.La communication Machine-to-Machine (M2M) est l'un des principaux fondements de l'Internet des Objets (IoT). C'est un phénomène qui a évolué discrètement au cours du temps et vient d’émerger à la surface pour do! nner naissance à une explosion de nouveaux usages et services. Capteurs, actionneurs, tags, véhicules et objets intelligents ont tous la possibilité de communiquer. Le nombre de connexions M2M est en constante augmentation et il est prévu de voir des milliards d’objets connectés dans un futur proche. Les applications M2M offrent des avantages dans divers domaines à savoir les villes intelligentes, les voitures connectées, les usines du futures, l’agriculture de précision, l’environnement, la santé, etc. La croissance rapide de cet écosystème est entrain de conduire le M2M vers un avenir prometteur. Cependant, les opportunités d'expansion des marchés M2M ne sont pas évidentes. En effet, un ensemble de challenges doivent être surmontés afin de permettre un déploiement à grande échelle dans des domaines diverses et variés à savoir les défis d’interopérabilité, de complexité et de scalabilité. Actuellement, le marché du M2M souffre d'une fragmentation verticale importante touchant la majorité des domaines industriels. En effet, diverses solutions propriétaires ont été conçues pour répondre à des applications spécifiques engendrant ainsi un sérieux problème d''interopérabilité. Pour adresser ce challenge, nous avons conçu, développer et expérimenté la plateforme OM2M offrant une architecture opérationnelle, flexible et extensible pour l'interopérabilité M2M conforme à la norme SmartM2M. Pour supporter les environnements contraints, nous avons proposé une nouvelle convention de nommage basée sur une structure de ressources non-hiérarchique permettant d’optimiser la taille des messages échangés. Pour assurer l’interopérabilité sémantique entre les applications et les machines, nous avons proposé l'ontologie IoT-O. Cette dernière est composée de cinq modèles de base représentant les capteurs, les actionneurs, les observations, les actuations et les web ! services pour permettre de converger rapidement vers un vocabulaire commun pour l'IoT. Une plateforme M2M horizontale permet d'interconnecter des machines hétérogènes largement distribués et qui évoluent fréquemment en fonction des changements de l’environnement. Maintenir ces systèmes complexes en vie est coûteux en termes de temps et d'argent. Pour adresser ce challenge, nous avons conçu, développé et intégré le framework FRAMESELF afin d'ajouter des capacités d'autogestion aux systèmes M2M basées sur le paradigme de l'informatique autonome. En étendant le modèle d'architecture de référence MAPE-K, notre solution permet d'adapter dynamiquement le comportement de la plateforme OM2M par en fonctions des changements du contexte et des politiques haut niveaux. Nous avons défini un ensemble de règles sémantiques pour faire du raisonnement sur l'ontologie IoT-O en tant que modèle de connaissance. Notre objectif est de permettre la découverte automatique entre les machines et les applications à travers un appariement sémantique et une reconfiguration dynam! ique de l'architecture des ressources. L’interopérabilité et l’autogestion ouvrent la voie à un déploiement de masse des systèmes M2M. Par contre, ces derniers se basent sur l'infrastructure actuelle d'internet qui n'a jamais été conçu pour ce genre de d'utilisation ce qui pose de nouvelles exigences en termes de scalabilité. Pour adresser ce challenge, nous avons conçu, simulé et validé l'approche OSCL proposant une nouvelle topologie de réseau maillé M2M comme alternative à l'approche centralisée actuelle. OSCL s'appuie sur les techniques de routage centrées sur l'information favorisant les communications à sauts multiples et un cache distribué pour une meilleure dissémination des données. Nous avons développé le simulateur OSCLsim pour valider l'approche proposée.[...

FRAMESELF: A generic autonomic framework for self-management of distributed systems -Application on the self-configuration of M2M architecture using semantic and ontology

International audienceM2M systems need to connect thousands of various fix and mobile machines that are widely distributed and evolve frequently according to their profile and context changes. The increasing complexity of managing current distributed systems needs new solutions. Designing autonomic systems, which are self-managing, and context aware is a solution and also a challenge. This paper proposes the FRAMESELF framework, a generic autonomic architecture based on decision models. The proposed solution capabilities are used for the self-deployment and self-configuration of machine-to-machine (M2M) systems. Components diagrams are illustrated to describe FRAMESELF modules and to show how they are connected together. FRAMESELF implements Devices Profile for Web Services (DPWS) protocol to describe and to discover managed resources. It implements multi models representation based on ontologies and graphs to describe the M2M concepts and relationships on a multi-level knowledge base. Two communication patterns modules based on service-oriented and event-driven communications are dynamically selected and configured in deployment plans. Finally, a smart metering scenario is experimented to validate this approach and to calculate the overload that FRAMESELF generates facing scalability. INTRODUCTION Over the last years, Internet of Things (IoT) has evolved at an exceptional speed. Things can both correspond to physical things (sensors, actuators, smartphones, machines, etc.) or immaterial ones (applications, multimedia content providers, directories, etc.). Such environments consist of a high amount of heterogeneous entities having mutual interactions and delivering high-level services that could be discovered, monitored, composed, and executed. One of the main concepts behind IoT is Machine-to-Machine (M2M) communication. M2M provides seamless integration of the heterogeneous participating machine domains overcoming the interoperability issues raised between them. The growing number of interconnected machines and the diversity of communication technologies and protocols as well as the increasing volume of exchanged data mirrored the increase of complexity in M2M systems. Deployment, configuration and maintenance of M2M platforms are costly in term of time and money and require a permanent presence of high skilled administrator. For this purpose, it is paramount to provide efficient M2M systems with capability of self-management to increase the autonomic potential of M2M systems. On the first hand, autonomic computing paradigm was introduced by IBM to deal with system complexity which is inspired by the human autonomic nervous system that handles complexity and uncertainties, and aims at realizing computing systems and applications capable of managing themselves with minimum human intervention. In theory, Autonomic computing encompasses four self-management capabilities including self-configuring, self-healing, self-optimizing and self-protecting. Up until the present time, available autonomic systems are restricted to specific problems and are not applied in M2M systems due to the vertical fragmentation of M2M application domains. On the other hand, ETSI define

Autonomic framework based on decision models for self-management of ubiquitous systems

International audienceInternet 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 complex environment is costly in terms of time and money. Designing context aware autonomic framework with capability of self-management is a challenge. This paper proposes FRAMESELF, a generic and extensible autonomic framework based on ontology and graph models, and reasoning rules to self-manage ubiquitous environment. A smart metering use case is experimented to illustrate the proposed solution

A Framework to Create Multi-domains Autonomic Middleware

International audienceThis paper proposes an enumeration and a classification of the services or functionality needed in the autonomic middleware. This allows to propose a second time the foundation for a framework that will be able to generate different middleware implementing autonomic loop and adapted to areas with different constraints and different needs. An illustration in the field of " Machine to Machine " and more particularly of smart metering is given

Towards Semantic Data Interoperability in oneM2M Standard

International audienceOneM2M standard is a global initiative led jointly by major standardization organizations around the world in order to come up with a unique standard for M2M communications. Prior standards, but also oneM2M, while focusing on achieving interoperability at the communication level, fail or lack to achieve full interoperability at the semantic data level. An expressive ontology for IoT called IoT-O has been defined making best use of already defined ontologies in specific domains such as sensor, observation, service, quantity kind, units, or time. IoT-O defines also some missing concepts relevant for IoT such as thing, actuator, actuation, or manager. The extension of the oneM2M standard to support semantic data interoperability based on IoT-O is discussed. Finally, through comprehensive real use cases, benefits of the augmented standard are demonstrated ranging from heterogeneous devices interoperability to autonomic behavior achieved by automated reasoning

Diameter constrained overlays with faulty links: equilibrium, stability, and upper bounds

International audienceIn network overlays, virtual links among remote processes are usually established to circumvent the limitations of underlying protocols. The resulting dynamics have been recently studied, based on a novel random graph model that assumes that no link failure can occur. In that model, the case of faulty links has been only marginally stated to stimulate future research activities. Unfortunately, network overlays are very prone to faulty links, which are caused by any possible reasons, that force a node to loose its connectivity. To bridge this gap, this brief deepens the implications of faulty links in diameter-constrained overlays and demonstrates the following: 1) the resulting system has a unique globally stable equilibrium point; 2) the number of links composing the network is upper bounded in closed form; and 3) the speed of convergence to the equilibrium point is upper bounded in closed form, too. These outcomes grant for a stable regime and serve for estimating the overhead incurred by network nodes and sizing them adequately. Finally, to characterize the application bounds of the model, a stochastic analysis of its accuracy has been proposed, along with an extensive simulation campaign that encompasses a wide range of scenarios