7 research outputs found

    Anti load-balancing for energy-aware distributed scheduling of virtual machines

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    La multiplication de l'informatique en nuage (Cloud) a abouti à la création de centres de données dans le monde entier. Le Cloud contient des milliers de nœuds de calcul. Cependant, les centres de données consomment d'énorme quantités d'énergie à travers le monde estimées à plus de 1,5 % de la consommation mondiale d'électricité et devrait continuer à croître. Une problématique habituellement étudiée dans les systèmes distribués est de répartir équitablement la charge. Mais lorsque l'objectif est de réduire la consommation électrique, ce type d'algorithmes peut mener à avoir des serveurs fortement sous chargés et donc à consommer de l'énergie inutilement. Cette thèse présente de nouvelles techniques, des algorithmes et des logiciels pour la consolidation dynamique et distribuée de machines virtuelles (VM) dans le Cloud. L'objectif principal de cette thèse est de proposer des stratégies d'ordonnancement tenant compte de l'énergie dans le Cloud pour les économies d'énergie. Pour atteindre cet objectif, nous utilisons des approches centralisées et décentralisées. Les contributions à ce niveau méthodologique sont présentées sur ces deux axes. L'objectif de notre démarche est de réduire la consommation de l'énergie totale du centre de données en contrôlant la consommation globale d'énergie des applications tout en assurant les contrats de service pour l'exécution des applications. La consommation d'énergie est réduite en désactivant et réactivant dynamiquement les nœuds physiques pour répondre à la demande des ressources. Les principales contributions sont les suivantes: - Ici on s'intéressera à la problématique contraire de l'équilibrage de charge. Il s'agit d'une technique appelée Anti Load-Balancing pour concentrer la charge sur un nombre minimal de nœuds. Le but est de pouvoir éteindre les nœuds libérés et donc de minimiser la consommation énergétique du système. - Ensuite une approche centralisée a été proposée et fonctionne en associant une valeur de crédit à chaque nœud. Le crédit d'un nœud dépend de son affinité pour ses tâches, sa charge de travail actuelle et sa façon d'effectuer ses communications. Les économies d'énergie sont atteintes par la consolidation continue des machines virtuelles en fonction de l'utilisation actuelle des ressources, les topologies de réseaux virtuels établis entre les machines virtuelles et l'état thermique de nœuds de calcul. Les résultats de l'expérience sur une extension de CloudSim (EnerSim) montrent que l'énergie consommée par les applications du Cloud et l'efficacité énergétique ont été améliorées. - Le troisième axe est consacré à l'examen d'une approche appelée "Cooperative scheduling Anti load-balancing Algorithm for cloud". Il s'agit d'une approche décentralisée permettant la coopération entre les différents sites. Pour valider cet algorithme, nous avons étendu le simulateur MaGateSim. Avec une large évaluation expérimentale d'un ensemble de données réelles, nous sommes arrivés à la conclusion que l'approche à la fois en utilisant des algorithmes centralisés et décentralisés peut réduire l'énergie consommée des centres de données.The multiplication of Cloud computing has resulted in the establishment of largescale data centers around the world containing thousands of compute nodes. However, Cloud consume huge amounts of energy. Energy consumption of data centers worldwide is estimated at more than 1.5% of the global electricity use and is expected to grow further. A problem usually studied in distributed systems is to evenly distribute the load. But when the goal is to reduce energy consumption, this type of algorithms can lead to have machines largely under-loaded and therefore consuming energy unnecessarily. This thesis presents novel techniques, algorithms, and software for distributed dynamic consolidation of Virtual Machines (VMs) in Cloud. The main objective of this thesis is to provide energy-aware scheduling strategies in cloud computing for energy saving. To achieve this goal, we use centralized and decentralized approaches. Contributions in this method are presented these two axes. The objective of our approach is to reduce data center's total energy consumed by controlling cloud applications' overall energy consumption while ensuring cloud applications' service level agreement. Energy consumption is reduced by dynamically deactivating and reactivating physical nodes to meet the current resource demand. The key contributions are: - First, we present an energy aware clouds scheduling using anti-load balancing algorithm : concentrate the load on a minimum number of severs. The goal is to turn off the machines released and therefore minimize the energy consumption of the system. - The second axis proposed an algorithm which works by associating a credit value with each node. The credit of a node depends on its affinity to its jobs, its current workload and its communication behavior. Energy savings are achieved by continuous consolidation of VMs according to current utilization of resources, virtual network topologies established between VMs, and thermal state of computing nodes. The experiment results, obtained with a simulator which extends CloudSim (EnerSim), show that the cloud application energy consumption and energy efficiency are being improved. - The third axis is dedicated to the consideration of a decentralized dynamic scheduling approach entitled Cooperative scheduling Anti-load balancing Algorithm for cloud. It is a decentralized approach that allows cooperation between different sites. To validate this algorithm, we have extended the simulator MaGateSim. With an extensive experimental evaluation with a real workload dataset, we got the conclusion that both the approach using centralized and decentralized algorithms can reduce energy consumed by data centers

    Energy aware clouds scheduling using anti-load balancing algorithm : EACAB

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    International audienceCloud computing is a highly scalable and cost-effective infrastructure for running HPC, enterprise and Web applications. However rapid growth of the demand for computational power by scientific, business and web- applications has led to the creation of large-scale data centers consuming enormous amounts of electrical power. Hence, energy-efficient solutions are required to minimize their energy consumption. The objective of our approach is to reduce data center’s total energy consumption by controlling cloud applications’ overall resource usage while guarantying service level agreement. This article presents Energy aware clouds scheduling using anti-load balancing algorithm (EACAB). The proposed algorithm works by associating a credit value with each node. The credit of a node depends on its affinity to its jobs, its current workload and its communication behavior. Energy savings are achieved by continuous consolidation of VMs according to current utilization of resources, virtual network topologies established between VMs and thermal state of computing nodes. The experiment results show that the cloud application energy consumption and energy efficiency is being improved effectively

    Cooperative Scheduling Anti-load balancing Algorithm for Cloud : CSAAC

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    In the past decade, more and more attention focuses on job scheduling strategies in a variety of scenarios. Due to the characteristics of clouds, meta-scheduling turns out to be an important scheduling pattern because it is responsible for orchestrating resources managed by independent local schedulers and bridges the gap between participating nodes. Likewise, to overcome issues such as bottleneck, overloading, under loading and impractical unique administrative management, which are normally led by conventional centralized or hierarchical schemes, the distributed scheduling scheme is emerging as a promising approach because of its capability with regards to scalability and flexibility. In this paper, we introduce a decentralized dynamic scheduling approach entitled Cooperative scheduling Anti-load balancing Algorithm for cloud (CSAAC). To validate CSAAC we used a simulator which extends the MaGateSim simulator and provides better support to energy aware scheduling algorithms. CSAAC goal is to achieve optimized scheduling performance and energy gain over the scope of overall cloud, instead of individual participating nodes. The extensive experimental evaluation with a real workload dataset shows that, when compared to the centralized scheduling scheme with Best Fit as the meta-scheduling policy, the use of CSAAC can lead to a 30%61% energy gain, and a 20%30% shorter average job execution time in a decentralized scheduling manner without requiring detailed real-time processing information from participating nodes

    A Decentralized Algorithm to Revisit the Debate of Centralization and Decentralization Approaches for Cloud Scheduling

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    International audienceIn Cloud Computing, scheduling jobs is a major and difficult issue. The main objectives of cloud services providers are the efficient use of their computing resources. Existing cloud management systems are mostly based on centralized architectures and energy management mechanisms are suffering several limitations. To address these limitations, our contribution is to design, implement, and evaluate a novel cloud management system which provides a holistic energy-efficient VM management solution by integrating advanced VM management mechanisms such as underload mitigation, VM consolidation, and power management. In this paper, we introduce a distributed task scheduling algorithm for Clouds that enables to schedule VMs cooperatively and dynamically inside a federation of clouds. We evaluated our prototype through simulations, to compare our decentralized approach with a centralized one. Our results showed that the proposed scheduler is very reactive

    Scheduling and Resource Allocation

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    International audienceChap.8 SCHEDULING AND RESOURCE ALLOCATION - 8.1 Introduction: Energy-Aware Scheduling 8.2 Use of Linear Programming in Energy-Aware Scheduling 8.2.1 Finding the Optimal Solution Using a Linear Program 8.2.2 Benefits and Limitations of LP 8.3 Heuristics in Large Instances 8.3.1 Energy-Aware Greedy Algorithms 8.3.2 Vector Packing 8.3.3 Improving Fast Algorithms 8.4 Comparing Allocation Heuristics for Energy-Aware Scheduling 8.4.1 Problem Formulation 8.4.2 Allocation Heuristics 8.4.3 Results 8.5 Energy-Aware Task Allocation in Mobile Environments 8.5.1 Reference Architecture 8.5.2 Task Allocation Strategy 8.5.3 Task Allocation Algorithm 8.5.4 Performance Results 8.6 An Energy-Aware Scheduling Strategy for Allocating Computational Tasks in a Fully Decentralized Way 8.6.1 Decentralized Resources in Cloud: Overview 8.6.2 Cooperative Scheduling Anti-Load Balancing Algorithm for Cloud (CSAAC) 8.6.3 Simulation Results 8.6.4 Evaluation 8.7 Cost-Aware Scheduling with Smart Grids 8.7.1 Cost-Aware Scheduling 8.7.2 Cost-Aware Scheduling Using DE 8.7.3 Comparison of DE with Other Approaches 8.8 Heterogeneity, Cooling, DVFS, and Migration 8.8.1 Lever Interactions 8.8.2 Infrastructures 8.8.3 Resource Allocation as a Whole 8.9 Conclusions - Reference
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