Allocation et réallocation de services pour les économies d'énergie dans les clusters et les clouds

L'informatique dans les nuages (cloud computing) est devenu durant les dernières années un paradigme dominant dans le paysage informatique. Son principe est de fournir des services décentralisés à la demande. La demande croissante pour ce type de service amène les fournisseurs de clouds à augmenter la taille de leurs infrastructures à tel point que les consommations d'énergie ainsi que les coûts associés deviennent très importants. Chaque fournisseur de service cloud doit répondre à des demandes différentes. C'est pourquoi au cours de cette thèse, nous nous sommes intéressés à la gestion des ressources efficace en énergie dans les clouds. Nous avons tout d'abord modélisé et étudié le problème de l'allocation de ressources initiale en fonction des services, en calculant des solutions approchées via des heuristiques, puis en les comparant à la solution optimale. Nous avons ensuite étendu notre modèle de ressources pour nous permettre d'avoir une solution plus globale, en y intégrant de l'hétérogénéité entre les machines et des infrastructures de refroidissement. Nous avons enfin validé notre modèle par simulation. Les services doivent faire face à différentes phases de charge, ainsi qu'à des pics d'utilisation. C'est pourquoi, nous avons étendu le modèle d'allocation de ressources pour y intégrer la dynamicité des requêtes et de l'utilisation des ressources. Nous avons mis en œuvre une infrastructure de cloud simulée, visant à contrôler l'exécution des différents services ainsi que le placement de ceux-ci. Ainsi notre approche permet de réduire la consommation d'énergie globale de l'infrastructure, ainsi que de limiter autant que possible les dégradations de performance.Cloud computing has become over the last years an important paradigm in the computing landscape. Its principle is to provide decentralized services and allows client to consume resources on a pay-as-you-go model. The increasing need for this type of service brings the service providers to increase the size of their infrastructures, to the extent that energy consumptions as well as operating costs are becoming important. Each cloud service provider has to provide for different types of requests. Infrastructure manager then have to host all the types of services together. That's why during this thesis, we tackled energy efficient resource management in the clouds. In order to do so, we first modeled and studied the initial service allocation problem, by computing approximated solutions given by heuristics, then comparing it to the optimal solution computed with a linear program solver. We then extended the model of resources to allow us to have a more global approach, by integrating the inherent heterogeneity of clusters and the cooling infrastructures. We then validated our model via simulation. Usually, the services must face different stages of workload, as well as utilization spikes. That's why we extended the model to include dynamicity of requests and resource usage, as well as the concept of powering on or off servers, or the cost of migrating a service from one host to another. We implemented a simulated cloud infrastructure, aiming at controlling the execution of the services as well as their placement. Thus, our approach enables the reduction of the global energy consumption of the infrastructure, and limits as much as possible degrading the performances

Fiscal competition in a transition economy

Endast sammandrag. Inbundna avhandlingar kan sökas i Helka-databasen (http://www.helsinki.fi/helka). Elektroniska kopior av avhandlingar finns antingen öppet på nätet eller endast tillgängliga i bibliotekets avhandlingsterminaler.Only abstract. Paper copies of master’s theses are listed in the Helka database (http://www.helsinki.fi/helka). Electronic copies of master’s theses are either available as open access or only on thesis terminals in the Helsinki University Library.Vain tiivistelmä. Sidottujen gradujen saatavuuden voit tarkistaa Helka-tietokannasta (http://www.helsinki.fi/helka). Digitaaliset gradut voivat olla luettavissa avoimesti verkossa tai rajoitetusti kirjaston opinnäytekioskeilla.The paper analyses effects of fiscal competition for mobile capital between identical regions in a transition economy. I add two features characteristic of transition economies into the familiar model of fiscal competition by Keen-Marchand (1997). Firstly, the economy is seen to consist of two sectors with differing productivities. Even though both sectors use same inputs, the new sector is more productive than the old one. Secondly, decision-makers are assumed to be only partially benevolent. They maximise a weighted average of consumer's utility and their private benefit that originates in the old sector production. The primary interest centers on the effects of fiscal competition on the overall level and on the composition of public goods provision when the economy is characterised by the above-mentioned transition features. Two specifications for decision-maker's private benefit will be used. The basic case corresponds closely to that in Keen-Marchand (1997) producing results largely in line with theirs. The level of public goods provision is proved to be too low in a competitive equilibrium. Additionally, the composition of public goods will be distorted towards too much infrastructure and too little social public good. A common increase in capital tax rates or a common change in the composition of public goods would unambigously increase consumer's welfare, but the welfare change is proven to be smaller than it would be in pure Keen-Marchand (1997) model. The alternative specification of decision-maker's private benefit may be seen as a special case of the one used in Qian-Roland (1998). As it is assumed that politicans own state sector rents, the results change radically. It is no longer self-evident that too little public goods is provided in a competitive equilibrium and a common policy change may, in fact, be welfare-deterioring for the consumers. Specifically, when the relative share of old sector production in a region is large, a common increase in tax on mobile capital may decrease consumer's welfare. The opposite is proven to hold if the production structure of the transition economy (i.e. the relative share of old sector production) is very close to a standard one-sector economy

Hybrid approach for energy aware management of multi-cloud architecture integrating user machines

International audienceThe arrival and development of remotely accessible services via the cloud has transfigured computer technology. However, its impact on personal computing remains limited to cloud-based applications. Meanwhile, acceptance and usage of telephony and smartphones have exploded. Their sparse administration needs and general user friendliness allows all people, regardless of technology literacy, to access, install and use a large variety of applications.We propose in this paper a model and a platform to offer personal computing a simple and transparent usage similar to modern telephony. In this model, user machines are integrated within the classical cloud model, consequently expanding available resources and management targets. In particular, we defined and implemented a modular architecture including resource managers at different levels that take into account energy and QoS concerns. We also propose simulation tools to design and size the underlying infrastructure to cope with the explosion of usage. Functionalities of the resulting platform are validated and demonstrated through various utilization scenarios. The internal scheduler managing resource usage is experimentally evaluated and compared with classical method-ologies, showing a significant reduction of energy consumption with almost no QoS degradation

Hybrid approach for energy aware management of multi-cloud architecture integrating user machines

The arrival and development of remotely accessible services via the cloud has transfigured computer technology. However, its impact on personal computing remains limited to cloud-based applications. Meanwhile, acceptance and usage of telephony and smartphones have exploded. Their sparse administration needs and general user friendliness allows all people, regardless of technology literacy, to access, install and use a large variety of applications. We propose in this paper a model and a platform to offer personal computing a simple and transparent usage similar to modern telephony. In this model, user machines are integrated within the classical cloud model, consequently expanding available resources and management targets. In particular, we defined and implemented a modular architecture including resource managers at different levels that take into account energy and QoS concerns. We also propose simulation tools to design and size the underlying infrastructure to cope with the explosion of usage. Functionalities of the resulting platform are validated and demonstrated through various utilization scenarios. The internal scheduler managing resource usage is experimentally evaluated and compared with classical methodologies, showing a significant reduction of energy consumption with almost no QoS degradation

Host management policy for energy efficient dynamic allocation

International audienceNowadays, reducing the energy consumption of large scale and distributed infrastructures has truly become a challenge for both industry and academia. Dynamic job allocation and resources provisioning is important to fit users requirements. The aim is to minimize the number of hosts utilized in order to reduce the energy consumption while maintaining a correct level of quality of service for the users. Green leverages like migration and on/off actions have cost overheads. Switching on and off a host has a cost: it takes time and it consumes power. These actions have to be optimized and should anticipate load variations. In this paper we investigate host management linked with the allocation and reallocation algorithm in order to optimize the number of powered on hosts and to reduce the overheads. We propose an original host management algorithm based on a genetic algorithm. Our approach has been implemented in DCWoRMS simulator and compared with other heuristics

Energy-Aware Resource Allocation

International audienceThis paper deals with the reduction of energy consumption in large scale systems, especially by taking into account the impact of energy consumption for server consolidation. Decreasing the number of physical hosts used while ensuring a certain level of quality of services is the goal of our approach. We introduce a metric called energetic yield which represents the quality of a task placement on a subset of machines, while taking into account quality of service and energy efficiency aspects. It measures the difference between resources required by a job and what the system allocates ultimately, while trying to save energy. Our work aims at minimizing this difference. We propose placement heuristics that are compared to the optimal solution and to a related system. In this paper, we present a set of experiments showing the relevance of this metric in order to reduce significantly energy consumption

Energy-aware service allocation

International audienceIn this paper we study the problem of energy-aware resource allocation for hosting long-term services or on-demand computing jobs in clusters, e.g., deployed as part of computing infrastructures. We formalize the problem as three constrained optimization problems: maximize job performance under power consumption constraints, minimize power consumption under job performance constraints, and optimize a linear combination of power consumption and job performance. These problems are NP-hard but, given an instance, a bound on the optimal solution can be computed via a rational linear program. We propose polynomial heuristics for all three problems. Simulation experiments show that in all three cases some heuristics can achieve results close to optimal, i.e., lead to good job performance while conserving energy

Réduction de la consommation énergétique dans les centres de serveurs

National audienceCet article traite de la réduction d'énergie dans les systèmes à large échelle, plus particulièrement l'impact de la prise en compte de la consommation énergétique sur la consolidation de serveurs. Diminuer le nombre de machines physiques utilisées tout en garantissant une certaine qualité de services est au coeur de notre approche. Nous introduisons une métrique appelée le yield énergétique qui représente la qualité du placement de tâches sur un ensemble de machines, en prenant en compte l'économie d'énergie et la qualité de services. Elle est matérialisée par la différence entre ce qui est demandé par une tâche et ce que le système lui alloue in fine en prenant aussi en compte le facteur économie d'énergie. Nous cherchons bien entendu à minimiser cette différence. Nous proposons des heuristiques de placement que nous comparons avec l'existant et avec l'optimal. Nous présentons dans cet article un ensemble d'expérimentations qui montrent la pertinence de cette métrique pour réduire de manière significative la consommation énergétique

Energy-Aware Service Allocation

In this paper we study the problem of energy-aware resource allocation for hosting long-term services or on-demand compute jobs in clusters, e.g., deployed as part of computing infrastructures. We formalize the problem as three constrained optimization problems: maximize job performance under power consumption constraints, minimize power consumption under job performance constraints, and optimize a linear combination of power consumption and job performance. These problems are NP-hard but, given an instance, a bound on the optimal solution can be computed via a rational linear program. We propose polynomial heuristics for all three problems. Simulation experiments show that in all three cases some heuristics can achieve results close to optimal, i.e., lead to good job performance while conserving energy. 1

Scheduling and Resource Allocation

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