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

Ordonnancement sous contraintes de qualité de service dans les clouds

In recent years, new issues have arisen in environmental considerations, increasingly pointed out in our society. In the field of Information Technology, data centers currently consume about 1.5% of world electricity. This increasing is due to changes in many areas, especially in Cloud Computing. Besides this environmental aspect, the management of energy consumption has become an important field of Quality of Service (QoS), in the responsibility of Cloud providers. These providers propose a QoS contract called SLA (Service Level Agreement), which specify the level of QoS given to users. The level of QoS offered directly influences the quality of the users' utilization, but also the overall energy consumption and performance of computing resources, which strongly affect profits of the Cloud providers. Cloud computing is intrinsically linked to the virtualization of computing resources. A model of hardware and software architecture is proposed in order to define the characteristics of the environment considered. Then, a detailed modeling of QoS parameters in terms of performance, dependability, security and cost is proposed. Therefore, QoS metrics, associated to these parameters are defined in order to extend the possibilities for evaluating the SLA. These models represent the first contribution of this thesis. Then, it is necessary to illustrate how the use and interpretation of several QoS metrics open the possibility of a more complex and precise analysis of algorithms' insight. This multi-criteria approach, that provides useful informations about the system's status can be analyzed to manage the QoS parameters' level. Thus, four antagonists metrics, including energy consumption, are selected and used together in several scheduling algorithms which allow to show their relevance, the enrichment given to these algorithms, and how a Cloud provider can take advantage of the results of this kind of multi-objective optimization. The second contribution presents a genetic algorithm (GA) and two greedy algorithms. The analysis of the genetic algorithm behavior allows to show different interests of a multi-criteria optimization applied to QoS metrics, usually ignored in studies dedicated to Cloud Computing. The third contribution of this thesis proposes a study of the impact of the use of QoS metrics in virtual machines scheduling. The simulator CloudSim has been used and expanded to improve its energy-aware tools. The DVFS (Dynamic Voltage & Frequency Scaling), providing a highly accurate dynamic management of CPU frequencies, the virtual machines reconfiguration, and the dynamic management of events have been included. The simulations involve all of these energy tools and placement algorithms, and evaluate each selected QoS metrics. These simulations allow to see the evolution in time of these metrics, depending on the algorithms used and the behavior of the GA in different optimizations configurations. This allows to analyze from different angles the behavior of greedy algorithms, the impact of optimizations GA, and the influence of these metrics one against the others.Ces dernières années, de nouvelles problématiques sont nées au vu des considérations écologiques de plus en plus présentes dans notre société. Dans le domaine de la technologie de l'Information, les centres de calcul consomment actuellement environ 1.5% de l'électricité mondiale. Cela ne cesse d’augmenter en raison de l'évolution de nombreux domaines et particulièrement du Cloud Computing. Outre cet aspect environnemental, le contrôle de la consommation d’énergie fait désormais partie intégrante des paramètres de Qualité de Service (QoS) incombant aux fournisseurs de services de Cloud Computing. En effet, ces fournisseurs de services à la demande proposent à leurs utilisateurs un contrat de QoS, appelé SLA (Service Level Agreement), qui définit de manière précise la qualité de service qu’ils s’engagent à respecter. Le niveau de QoS proposé influence directement la qualité d’utilisation des services par les utilisateurs, mais aussi la consommation et le rendement général de l’ensemble des ressources de calcul utilisées, impactant fortement les bénéfices des fournisseurs de services.Le Cloud Computing étant intrinsèquement lié à la virtualisation des ressources de calcul, une élaboration de modèles d’architecture matérielle et logicielle est proposée afin de définir les caractéristiques de l’environnement considéré. Ensuite, une modélisation détaillée de paramètres de QoS en termes de performance, de sûreté de fonctionnement, de sécurité des données et de coûts est proposée. Des métriques associées à ces paramètres sont définies afin d’étendre les possibilités d'évaluation des SLA. Ces modélisations constituent la première contribution de cette thèse.Il convient alors de démontrer comment l’utilisation et l’interprétation de plusieurs métriques de QoS ouvrent la possibilité d'une analyse plus complexe et plus fine de la perspicacité des algorithmes de placement. Cette approche multi-critères leur apporte des informations importantes sur l’état de leur système qu’ils peuvent analyser afin de gérer le niveau de chaque paramètre de QoS. Ainsi, quatre métriques antagonistes, incluant la consommation énergétique, ont été sélectionnées et utilisées conjointement dans plusieurs algorithmes de placement de manière à montrer leur pertinence, l’enrichissement qu’elles apportent à ces algorithmes, et comment un fournisseur de service peut tirer profit des résultats d’une optimisation multi-objectifs. Cette seconde contribution présente un algorithme génétique (GA) ainsi que deux algorithmes gloutons. L’analyse du comportement de l'algorithme génétique a permis de démontrer différents intérêts d’une optimisation multi-critères appliquée à des métriques de QoS habituellement ignorées dans les études dédiées au Cloud Computing.La troisième contribution de cette thèse propose une étude de l’impact de l'utilisation des métriques de QoS sur l’ordonnancement de machines virtuelles au cours du temps. Pour cela, le simulateur CloudSim a été exploité et étendu afin d'améliorer ses fonctionnalités de gestion de consommation énergétique. Tout d’abord par l’ajout du DVFS (Dynamic Voltage & Frequency Scaling) apportant une gestion dynamique très précise des fréquences de fonctionnement CPU, puis la possibilité de reconfiguration de machines virtuelles et enfin par la gestion dynamique des évènements. Les simulations effectuées mettent en jeu l'ensemble de ces outils énergétiques ainsi que les algorithmes de placement et évaluent chacune des métriques de QoS sélectionnées. Ces simulations donnent une vision temporelle de l’évolution de celles-ci, en fonction des algorithmes utilisés et de plusieurs configurations d’optimisation du GA. Cela permet d'analyser sous différents angles le comportement des algorithmes gloutons, l'impact des optimisations du GA, et l'influence des métriques les unes par rapport aux autres.Une collaboration a pu être établie avec le laboratoire CLOUDS Laborartory de Melbourne, dirigé par Prof. Rajkumar Buyya

Grid'5000 energy-aware experiments with DVFS

National audienceIn recent years, much research has been conducted in the area of energy efficiency in distributed systems. To analyze, understand and improve their behaviour, simulators provide useful tools, to achieve energy-aware simulation like DVFS (Dynamic Voltage and Frequency Scaling). This paper presents current work on Grid'5000 to deploy a specific distributed electromagnetic application called TLM (Transmission Line Matrix), using DVFS and power measurements. The aim is to launch different set of experiments using different DVFS configurations, and then compare simulations and real experiments results

Simulation énergétique de tâches distribuées avec changements dynamiques de fréquence

National audienceCes dernières années, de nombreuses recherches ont été menées dans le domaine de la simulation des systèmes distribués, afin d'analyser et de comprendre leur comportement. Certains de ces simulateurs se focalisent sur le problème d'ordonnancement de tâches, d'autres sont spécifiquement développés pour la modélisation du réseau et seulement peu d'entre eux proposent tous les outils nécessaires pour simuler la consommation énergétique d'une application, d'une machine ou d'un centre de calcul. Cet article décrit les outils qui doivent être intégrés dans un simulateur pour être en mesure de lancer des simulations destinées à améliorer le comportement énergétique des machines. L'accent est mis davantage sur le DVFS (Dynamic Voltage and Frequency Scaling) et sa mise en oeuvre dans CloudSim, le simula-teur qui a été utilisé pour les expériences décrites dans cet article, mais aussi sur la façon de simuler et la méthodologie adoptée pour assurer la qualité des mesures et des simulations

Dynamic voltage and frequency scaling for 3D Classical Spin Glass application

International audienceThe power consumption of large-scale high performance computing (HPC) systems is becoming a crucial challenge in the context of increasing the performance regardless of energy consumption [1]. Therefore, finding ways to improve energy efficiency has become a main issue for HPC applications. Dynamic voltage and frequency scaling (DVFS) is a widely used and powerful technique for reducing energy consumption in modern processors. The present paper investigates energy efficiency of 3D Classical Spin Glass [2], [3] application using the performance, ondemand and powersave modes of DVFS method. The series of experiments show that the execution time of OnDemand and PowerSave is the same, while the OnDemand mode is better due to the power consumption and frequency balance for the system

Energy-aware simulation with DVFS

International audienceIn recent years, research has been conducted in the area of large systems models, especially distributed systems, to analyze and understand their behavior. Simulators are now commonly used in this area and are becoming more complex. Most of them provide frameworks for simulating application scheduling in various Grid infrastructures, others are specifically developed for modeling networks, but only a few of them simulate energy-efficient algorithms. This article describes which tools need to be implemented in a simulator in order to support energy-aware experimentation. The emphasis is on DVFS simulation, from its implementation in the simulator CloudSim to the whole methodology adopted to validate its functioning. In addition, a scientific application is used as a use case in both experiments and simulations, where the close relationship between DVFS efficiency and hardware architecture is highlighted. A second use case using Cloud applications represented by DAGs, which is also a new functionality of CloudSim, demonstrates that the DVFS efficiency also depends on the intrinsic middleware behavior

Internal Self-protecting for Consistency and Stability in an Autonomic Manager

International audienceIn this article we describe an approach for autonomic management of legacy software in distributed environments (cluster, grid or cloud). Our propositions have been implemented in a tool (TUNe) based on diagrams formalisms. We describe particularly the property of self-Protecting. The various mechanisms needed to maintain the consistency of the managed system, are described: autonomic creation or destruction, interruption or rollback. Finally experiments using this policy are made on the software DIET

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

Green energy efficient scheduling management

International audienceThe analysis of the energy efficiency in Cloud Computing infrastructures has become an important research domain as the utilization rate of the various on-demand services is daily higher and higher and its management is now considered as a main objective. Today, to tackle this challenging issue, Cloud providers integrate renewable energy sources to feed their infrastructure. Energy saving is part often an integral many companies goal. Unlike the classic supply of grid energy, the production of green energy is unstable and depends on nature of the weather or wind. It introduces new challenges as pervasive jobs to reduce a server consumption. In this article, studies based on the use and the storage of photovoltaic energy are exposed. We detail our design of a scheduler which uses solar energy production to make an off-line decision. This enables us to schedule virtual machines into a datacenter via different algorithms which consumes the least amount of brown energy as possible. We based our analysis through an existing workload from Google. We describe and study this workload to create one corresponding to our need. We also proposed to evaluate the storage size of a smartgrid related to the solar panel size. It is an analysis of the reliance between both storage (battery) and renewable energy production (solar panel) components sizing

Mixed integer linear programming for quality of service optimization in Clouds

International audienceThe analysis of the Quality of Service (QoS) level in a Cloud Computing environment becomes an attractive researchdomain as the utilization rate is daily higher and higher. Its management has a huge impact on the performance ofboth services and global Cloud infrastructures. Thus, in order to nd a good trade-o, a Cloud provider has to takeinto account many QoS objectives, and also the manner to optimize them during the virtual machines allocationprocess. To tackle this complex challenge, this article proposed a multiobjective optimization of four relevantCloud QoS objectives, using two different optimization methods: a Genetic Algorithm (GA) and a Mixed IntegerLinear Programming (MILP) approach. The complexity of the virtual machine allocation problem is increasedby the modeling of Dynamic Voltage and Frequency Scaling (DVFS) for energy saving on hosts. A global mixed-integer non linear programming formulation is presented and a MILP formulation is derived by linearization. Aheuristic decomposition method, which uses the MILP to optimize intermediate objectives, is proposed. Numerousexperimental results show the complementarity of the two heuristics to obtain various trade-os between the differentQoS objectives