Cloud Computing Resource Management through a Grid Middleware: A Case Study with DIET and Eucalyptus

The Cloud phenomenon is quickly growing towards becoming the de facto standard of Internet Computing, storage and hosting both in industry and academia. The large scalability possibilities offered by Cloud platforms can be harnessed not only for services and applications hosting but also as a raw on-demand computing resource. This paper proposes the use of a Cloud system as a raw computational on-demand resource for a Grid middleware. We illustrate a proof of concept by considering the DIET-Solve Grid middleware and the EUCALYPTUS open-source Cloud platform

Scheduling and Dynamic Management of Applications over Grids

The work presented in this Thesis is about scheduling applications in computational Grids. We study how to better manage jobs in a grid middleware in order to improve the performance of the platform. Our solutions are designed to work at the middleware layer, thus allowing to keep the underlying architecture unmodified. First, we propose a reallocation mechanism to dynamically tackle errors that occur during the scheduling. Indeed, it is often necessary to provide a runtime estimation when submitting on a parallel computer so that it can compute a schedule. However, estimations are inherently inaccurate and scheduling decisions are based on incorrect data, and are therefore wrong. The reallocation mechanism we propose tackles this problem by moving waiting jobs between several parallel machines in order to reduce the scheduling errors due to inaccurate runtime estimates. Our second interest in the Thesis is the study of the scheduling of a climatology application on the Grid. To provide the best possible performances, we modeled the application as a Directed Acyclic Graph (DAG) and then proposed specific scheduling heuristics. To execute the application on the Grid, the middleware uses the knowledge of the application to find thebest schedule.Les travaux présentés dans cette thèse portent sur l'ordonnancement d'applications au sein d'un environnement de grille de calcul. Nous étudions comment mieux gérer les tâches au sein des intergiciels de grille, ceci dans l'objectif d'améliorer les performances globales de la plateforme. Les solutions que nous proposons se situent dans l'intergiciel, ce qui permet de conserver les architectures sous-jacentes sans les modifier. Dans un premier temps, nous proposons un mécanisme de réallocation permettant de prendre en compte dynamiquement les erreurs d'ordonnancement commises lors de la soumission de calculs. En effet, lors de la soumission sur une machine parallèle, il est souvent nécessaire de fournir une estimation du temps d'exécution afin que celle-ci puisse effectuer un ordonnancement. Cependant, les estimations ne sont pas précises et les décisions d'ordonnancement sont sans cesse remises en question. Le mécanisme de réallocation proposé permet de prendre en compte ces changements en déplaçant des calculs d'une machine parallèle à une autre. Le second point auquel nous nous intéressons dans cette thèse est l'ordonnancement d'une application de climatologie sur la grille. Afin de fournir les meilleures performances possibles nous avons modélisé l'application puis proposé des heuristiques spécifiques. Pour exécuter l'application sur une grille de calcul, l'intergiciel utilise ces connaissances sur l'application pour fournir le meilleur ordonnancement possible

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field

平成20年度 計算科学研究センター研究報告

1-1 素粒子分野1-2 宇宙分野2-1 計算物性分野2-2 原子核理論分野2-3 計算生命分野3-1 地球環境分野3-2 生物分野4-1 計算機アーキテクチャ研究分野5-1 計算知能分野5-2 計算メディア分

Tunable Scheduling in a GridRPC Framework

International audienceAmong existing grid middleware approaches, one simple, powerful, and flexible approach consists of using servers available in different administrative domains through the classic client-server or Remote Procedure Call (RPC) paradigm. Network Enabled Servers (NES) implement this model also called GridRPC. Clients submit computation requests to a scheduler whose goal is to find a server available on the grid using some performance metric. The aim of this paper is to give an overview of a NES middleware developed in the GRAAL team called DIET and to describe recent developments around plugin schedulers, workflow management, and tools. DIET (Distributed Interactive Engineering Toolbox) is a hierarchical set of components used for the development of applications based on computational servers on the grid