Versatile, scalable, and accurate simulation of distributed applications and platforms

The study of parallel and distributed applications and platforms, whether in the cluster, grid, peer-to-peer, volunteer, or cloud computing domain, often mandates empirical evaluation of proposed algorithmic and system solutions via simulation. Unlike direct experimentation via an application deploy...

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Vydané v:	Journal of parallel and distributed computing Ročník 74; číslo 10; s. 2899 - 2917
Hlavní autori:	Casanova, Henri, Giersch, Arnaud, Legrand, Arnaud, Quinson, Martin, Suter, Frédéric
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier Inc 01.10.2014 Elsevier
Predmet:	Applied sciences Computer Science Computer science; control theory; systems Computer systems and distributed systems. User interface Distributed, Parallel, and Cluster Computing Exact sciences and technology Scalability SimGrid Simulation Software Validation Versatility Validation SimGrid Simulation Scalability Versatility High performance Cloud computing Peer to peer Personal computer Computer simulation Grid Algorithmics Cluster Distributed computing Modeling Volunteering Simulator
ISSN:	0743-7315, 1096-0848
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Abstract	The study of parallel and distributed applications and platforms, whether in the cluster, grid, peer-to-peer, volunteer, or cloud computing domain, often mandates empirical evaluation of proposed algorithmic and system solutions via simulation. Unlike direct experimentation via an application deployment on a real-world testbed, simulation enables fully repeatable and configurable experiments for arbitrary hypothetical scenarios. Two key concerns are accuracy (so that simulation results are scientifically sound) and scalability (so that simulation experiments can be fast and memory-efficient). While the scalability of a simulator is easily measured, the accuracy of many state-of-the-art simulators is largely unknown because they have not been sufficiently validated. In this work we describe recent accuracy and scalability advances made in the context of the SimGrid simulation framework. A design goal of SimGrid is that it should be versatile, i.e., applicable across all aforementioned domains. We present quantitative results that show that SimGrid compares favorably with state-of-the-art domain-specific simulators in terms of scalability, accuracy, or the trade-off between the two. An important implication is that, contrary to popular wisdom, striving for versatility in a simulator is not an impediment but instead is conducive to improving both accuracy and scalability. •We provide a presentation of the improvements done in SimGrid in the last 10 years.•We rebut popular wisdom that specialization allows for “better” simulation.•We claim that versatility leads to better accuracy and better scalability.•We back up this claim with multiple use cases and (in)validation studies.
AbstractList	The study of parallel and distributed applications and platforms, whether in the cluster, grid, peer-to-peer, volunteer, or cloud computing domain, often mandates empirical evaluation of proposed algorithmic and system solutions via simulation. Unlike direct experimentation via an application deployment on a real-world testbed, simulation enables fully repeatable and configurable experiments for arbitrary hypothetical scenarios. Two key concerns are accuracy (so that simulation results are scientifically sound) and scalability (so that simulation experiments can be fast and memory-efficient). While the scalability of a simulator is easily measured, the accuracy of many state-of-the-art simulators is largely unknown because they have not been sufficiently validated. In this work we describe recent accuracy and scalability advances made in the context of the SimGrid simulation framework. A design goal of SimGrid is that it should be versatile, i.e., applicable across all aforementioned domains. We present quantitative results that show that SimGrid compares favorably to state-of-the-art domain-specific simulators in terms of scalability, accuracy, or the trade-off between the two. An important implication is that, contrary to popular wisdom, striving for versatility in a simulator is not an impediment but instead is conducive to improving both accuracy and scalability. The study of parallel and distributed applications and platforms, whether in the cluster, grid, peer-to-peer, volunteer, or cloud computing domain, often mandates empirical evaluation of proposed algorithmic and system solutions via simulation. Unlike direct experimentation via an application deployment on a real-world testbed, simulation enables fully repeatable and configurable experiments for arbitrary hypothetical scenarios. Two key concerns are accuracy (so that simulation results are scientifically sound) and scalability (so that simulation experiments can be fast and memory-efficient). While the scalability of a simulator is easily measured, the accuracy of many state-of-the-art simulators is largely unknown because they have not been sufficiently validated. In this work we describe recent accuracy and scalability advances made in the context of the SimGrid simulation framework. A design goal of SimGrid is that it should be versatile, i.e., applicable across all aforementioned domains. We present quantitative results that show that SimGrid compares favorably with state-of-the-art domain-specific simulators in terms of scalability, accuracy, or the trade-off between the two. An important implication is that, contrary to popular wisdom, striving for versatility in a simulator is not an impediment but instead is conducive to improving both accuracy and scalability. •We provide a presentation of the improvements done in SimGrid in the last 10 years.•We rebut popular wisdom that specialization allows for “better” simulation.•We claim that versatility leads to better accuracy and better scalability.•We back up this claim with multiple use cases and (in)validation studies.
Author	Casanova, Henri Legrand, Arnaud Suter, Frédéric Quinson, Martin Giersch, Arnaud
Author_xml	– sequence: 1 givenname: Henri surname: Casanova fullname: Casanova, Henri organization: Department of Information and Computer Sciences, University of Hawai‘i at Manoa, USA – sequence: 2 givenname: Arnaud orcidid: 0000-0002-4443-1166 surname: Giersch fullname: Giersch, Arnaud organization: FEMTO-ST, University of Franche-Comté, Belfort, France – sequence: 3 givenname: Arnaud surname: Legrand fullname: Legrand, Arnaud organization: LIG, CNRS, Grenoble University, France – sequence: 4 givenname: Martin surname: Quinson fullname: Quinson, Martin organization: LORIA, Université de Lorraine, France – sequence: 5 givenname: Frédéric surname: Suter fullname: Suter, Frédéric email: frederic.suter@cc.in2p3.fr organization: IN2P3 Computing Center, CNRS/IN2P3, Lyon-Villeurbanne, France
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Snippet	The study of parallel and distributed applications and platforms, whether in the cluster, grid, peer-to-peer, volunteer, or cloud computing domain, often...
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SubjectTerms	Applied sciences Computer Science Computer science; control theory; systems Computer systems and distributed systems. User interface Distributed, Parallel, and Cluster Computing Exact sciences and technology Scalability SimGrid Simulation Software Validation Versatility
Title	Versatile, scalable, and accurate simulation of distributed applications and platforms
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