InforLorV4, Main, Merge, bibRecord, 004184

SimGrid: a Generic Framework for Large-Scale Distributed Experiments

Identifieur interne : 004184 ( Main/Merge ); précédent : 004183; suivant : 004185

SimGrid: a Generic Framework for Large-Scale Distributed Experiments

Auteurs : Henri Casanova [États-Unis] ; Arnaud Legrand [France] ; Martin Quinson [France]

Source :

RBID : Hal:inria-00260697

Abstract

Distributed computing is a very broad and active research area comprising fields such as cluster computing, computational grids, desktop grids and peer-to-peer (P2P) systems. Unfortunately, it is often impossible to obtain theoretical or analytical results to compare the performance of algorithms targeting such systems. One possibility is to conduct large numbers of back-to-back experiments on real platforms. While this is possible on tightly-coupled platforms, it is infeasible on modern distributed platforms as experiments are labor-intensive and results typically not reproducible. Consequently, one must resort to simulations, which enable reproducible results and also make it possible to explore wide ranges of platform and application scenarios. In this paper we describe the SimGrid framework, a simulation-based framework for evaluating cluster, grid and P2P algorithms andheuristics. This paper focuses on SimGrid v3, which greatly improves on previous versions thanks to a novel and validated modular simulation engine that achieves higher simulation speed without hindering simulation accuracy. Also, two new user interfaces were added to broaden the targeted research community. After surveying existing tools and methodologies we describe the key features and benefits of SimGrid.

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https://hal.inria.fr/inria-00260697

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<front><div type="abstract" xml:lang="en">Distributed computing is a very broad and active research area comprising fields such as cluster computing, computational grids, desktop grids and peer-to-peer (P2P) systems. Unfortunately, it is often impossible to obtain theoretical or analytical results to compare the performance of algorithms targeting such systems. One possibility is to conduct large numbers of back-to-back experiments on real platforms. While this is possible on tightly-coupled platforms, it is infeasible on modern distributed platforms as experiments are labor-intensive and results typically not reproducible. Consequently, one must resort to simulations, which enable reproducible results and also make it possible to explore wide ranges of platform and application scenarios. In this paper we describe the SimGrid framework, a simulation-based framework for evaluating cluster, grid and P2P algorithms andheuristics. This paper focuses on SimGrid v3, which greatly improves on previous versions thanks to a novel and validated modular simulation engine that achieves higher simulation speed without hindering simulation accuracy. Also, two new user interfaces were added to broaden the targeted research community. After surveying existing tools and methodologies we describe the key features and benefits of SimGrid.</div>
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SimGrid: a Generic Framework for Large-Scale Distributed Experiments

SimGrid: a Generic Framework for Large-Scale Distributed Experiments

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