Energy and Performance: Management of Virtual Machines: Provisioning, Placement, and Consolidation

Cloud computing is a new computing paradigm that offers scalable storage and compute resources to users on demand through Internet. Public cloud providers operate large-scale data centers around the world to handle a large number of users request. However, data centers consume an immense amount of electrical energy that can lead to high operating costs and carbon emissions. One of the most common and effective method in order to reduce energy consumption is Dynamic Virtual Machines Consolidation (DVMC) enabled by the virtualization technology. DVMC dynamically consolidates Virtual Machines (VMs) into the minimum number of active servers and then switches the idle servers into a power-saving mode to save energy. However, maintaining the desired level of Quality-of-Service (QoS) between data centers and their users is critical for satisfying users’ expectations concerning performance. Therefore, the main challenge is to minimize the data center energy consumption while maintaining the required QoS. This thesis address this challenge by presenting novel DVMC approaches to reduce the energy consumption of data centers and improve resource utilization under workload independent quality of service constraints. These approaches can be divided into three main categories: heuristic, meta-heuristic and machine learning. Our first contribution is a heuristic algorithm for solving the DVMC problem. The algorithm uses a linear regression-based prediction model to detect over-loaded servers based on the historical utilization data. Then it migrates some VMs from the over-loaded servers to avoid further performance degradations. Moreover, our algorithm consolidates VMs on fewer number of server for energy saving. The second and third contributions are two novel DVMC algorithms based on the Reinforcement Learning (RL) approach. RL is interesting for highly adaptive and autonomous management in dynamic environments. For this reason, we use RL to solve two main sub-problems in VM consolidation. The first sub-problem is the server power mode detection (sleep or active). The second sub-problem is to find an effective solution for server status detection (overloaded or non-overloaded). The fourth contribution of this thesis is an online optimization meta-heuristic algorithm called Ant Colony System-based Placement Optimization (ACS-PO). ACS is a suitable approach for VM consolidation due to the ease of parallelization, that it is close to the optimal solution, and its polynomial worst-case time complexity. The simulation results show that ACS-PO provides substantial improvement over other heuristic algorithms in reducing energy consumption, the number of VM migrations, and performance degradations. Our fifth contribution is a Hierarchical VM management (HiVM) architecture based on a three-tier data center topology which is very common use in data centers. HiVM has the ability to scale across many thousands of servers with energy efficiency. Our sixth contribution is a Utilization Prediction-aware Best Fit Decreasing (UP-BFD) algorithm. UP-BFD can avoid SLA violations and needless migrations by taking into consideration the current and predicted future resource requirements for allocation, consolidation, and placement of VMs. Finally, the seventh and the last contribution is a novel Self-Adaptive Resource Management System (SARMS) in data centers. To achieve scalability, SARMS uses a hierarchical architecture that is partially inspired from HiVM. Moreover, SARMS provides self-adaptive ability for resource management by dynamically adjusting the utilization thresholds for each server in data centers.Siirretty Doriast

Toward sustainable data centers: a comprehensive energy management strategy

Data centers are major contributors to the emission of carbon dioxide to the atmosphere, and this contribution is expected to increase in the following years. This has encouraged the development of techniques to reduce the energy consumption and the environmental footprint of data centers. Whereas some of these techniques have succeeded to reduce the energy consumption of the hardware equipment of data centers (including IT, cooling, and power supply systems), we claim that sustainable data centers will be only possible if the problem is faced by means of a holistic approach that includes not only the aforementioned techniques but also intelligent and unifying solutions that enable a synergistic and energy-aware management of data centers. In this paper, we propose a comprehensive strategy to reduce the carbon footprint of data centers that uses the energy as a driver of their management procedures. In addition, we present a holistic management architecture for sustainable data centers that implements the aforementioned strategy, and we propose design guidelines to accomplish each step of the proposed strategy, referring to related achievements and enumerating the main challenges that must be still solved.Peer ReviewedPostprint (author's final draft

Allocation of Virtual Machines in Cloud Data Centers - A Survey of Problem Models and Optimization Algorithms

Data centers in public, private, and hybrid cloud settings make it possible to provision virtual machines (VMs) with unprecedented flexibility. However, purchasing, operating, and maintaining the underlying physical resources incurs significant monetary costs and also environmental impact. Therefore, cloud providers must optimize the usage of physical resources by a careful allocation of VMs to hosts, continuously balancing between the conflicting requirements on performance and operational costs. In recent years, several algorithms have been proposed for this important optimization problem. Unfortunately, the proposed approaches are hardly comparable because of subtle differences in the used problem models. This paper surveys the used problem formulations and optimization algorithms, highlighting their strengths and limitations, also pointing out the areas that need further research in the future

A comparison of resource allocation process in grid and cloud technologies

Grid Computing and Cloud Computing are two different technologies that have emerged to validate the long-held dream of computing as utilities which led to an important revolution in IT industry. These technologies came with several challenges in terms of middleware, programming model, resources management and business models. These challenges are seriously considered by Distributed System research. Resources allocation is a key challenge in both technologies as it causes the possible resource wastage and service degradation. This paper is addressing a comprehensive study of the resources allocation processes in both technologies. It provides the researchers with an in-depth understanding of all resources allocation related aspects and associative challenges, including: load balancing, performance, energy consumption, scheduling algorithms, resources consolidation and migration. The comparison also contributes an informal definition of the Cloud resource allocation process. Resources in the Cloud are being shared by all users in a time and space sharing manner, in contrast to dedicated resources that governed by a queuing system in Grid resource management. Cloud Resource allocation suffers from extra challenges abbreviated by achieving good load balancing and making right consolidation decision

Multi-capacity combinatorial ordering GA in application to cloud resources allocation and efficient virtual machines consolidation

This paper describes a novel approach making use of genetic algorithms to find optimal solutions for multi-dimensional vector bin packing problems with the goal to improve cloud resource allocation and Virtual Machines (VMs) consolidation. Two algorithms, namely Combinatorial Ordering First-Fit Genetic Algorithm (COFFGA) and Combinatorial Ordering Next Fit Genetic Algorithm (CONFGA) have been developed for that and combined. The proposed hybrid algorithm targets to minimise the total number of running servers and resources wastage per server. The solutions obtained by the new algorithms are compared with latest solutions from literature. The results show that the proposed algorithm COFFGA outperforms other previous multi-dimension vector bin packing heuristics such as Permutation Pack (PP), First Fit (FF) and First Fit Decreasing (FFD) by 4%, 34%, and 39%, respectively. It also achieved better performance than the existing genetic algorithm for multi-capacity resources virtual machine consolidation (RGGA) in terms of performance and robustness. A thorough explanation for the improved performance of the newly proposed algorithm is given

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

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

GAME-SCORE: Game-based energy-aware cloud scheduler and simulator for computational clouds

Energy-awareness remains one of the main concerns for today's cloud computing (CC) operators. The optimisation of energy consumption in both cloud computational clusters and computing servers is usually related to scheduling problems. The definition of an optimal scheduling policy which does not negatively impact to system performance and task completion time is still challenging. In this work, we present a new simulation tool for cloud computing, GAME-SCORE, which implements a scheduling model based on the Stackelberg game. This game presents two main players: a) the scheduler and b) the energy-efficiency agent. We used the GAME-SCORE simulator to analyse the efficiency of the proposed game-based scheduling model. The obtained results show that the Stackelberg cloud scheduler performs better than static energy-optimisation strategies and can achieve a fair balance between low energy consumption and short makespan in a very short tim

Energy and performance-aware scheduling and shut-down models for efficient cloud-computing data centers.

This Doctoral Dissertation, presented as a set of research contributions, focuses on resource efficiency in data centers. This topic has been faced mainly by the development of several energy-efficiency, resource managing and scheduling policies, as well as the simulation tools required to test them in realistic cloud computing environments. Several models have been implemented in order to minimize energy consumption in Cloud Computing environments. Among them: a) Fifteen probabilistic and deterministic energy-policies which shut-down idle machines; b) Five energy-aware scheduling algorithms, including several genetic algorithm models; c) A Stackelberg game-based strategy which models the concurrency between opposite requirements of Cloud-Computing systems in order to dynamically apply the most optimal scheduling algorithms and energy-efficiency policies depending on the environment; and d) A productive analysis on the resource efficiency of several realistic cloud–computing environments. A novel simulation tool called SCORE, able to simulate several data-center sizes, machine heterogeneity, security levels, workload composition and patterns, scheduling strategies and energy-efficiency strategies, was developed in order to test these strategies in large-scale cloud-computing clusters. As results, more than fifty Key Performance Indicators (KPI) show that more than 20% of energy consumption can be reduced in realistic high-utilization environments when proper policies are employed.Esta Tesis Doctoral, que se presenta como compendio de artículos de investigación, se centra en la eficiencia en la utilización de los recursos en centros de datos de internet. Este problema ha sido abordado esencialmente desarrollando diferentes estrategias de eficiencia energética, gestión y distribución de recursos, así como todas las herramientas de simulación y análisis necesarias para su validación en entornos realistas de Cloud Computing. Numerosas estrategias han sido desarrolladas para minimizar el consumo energético en entornos de Cloud Computing. Entre ellos: 1. Quince políticas de eficiencia energética, tanto probabilísticas como deterministas, que apagan máquinas en estado de espera siempre que sea posible; 2. Cinco algoritmos de distribución de tareas que tienen en cuenta el consumo energético, incluyendo varios modelos de algoritmos genéticos; 3. Una estrategia basada en la teoría de juegos de Stackelberg que modela la competición entre diferentes partes de los centros de datos que tienen objetivos encontrados. Este modelo aplica dinámicamente las estrategias de distribución de tareas y las políticas de eficiencia energética dependiendo de las características del entorno; y 4. Un análisis productivo sobre la eficiencia en la utilización de recursos en numerosos escenarios de Cloud Computing. Una nueva herramienta de simulación llamada SCORE se ha desarrollado para analizar las estrategias antes mencionadas en clústers de Cloud Computing de grandes dimensiones. Los resultados obtenidos muestran que se puede conseguir un ahorro de energía superior al 20% en entornos realistas de alta utilización si se emplean las estrategias de eficiencia energética adecuadas. SCORE es open source y puede simular diferentes centros de datos con, entre otros muchos, los siguientes parámetros: Tamaño del centro de datos; heterogeneidad de los servidores; tipo, composición y patrones de carga de trabajo, estrategias de distribución de tareas y políticas de eficiencia energética, así como tres gestores de recursos centralizados: Monolítico, Two-level y Shared-state. Como resultados, esta herramienta de simulación arroja más de 50 Key Performance Indicators (KPI) de rendimiento general, de distribucin de tareas y de energía.Premio Extraordinario de Doctorado U