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High accuracy failure injection in parallel and distributed systems using virtualization

Published: 18 May 2009 Publication History

Abstract

Emulation sits between simulation and experimentation to complete the set of tools available for software designers to evaluate their software and predict behavior under conditions usually unachievable in a laboratory experiment. It consists in running the real application in an emulated environment. Thus, it behaves more realistically than a simulation, but under a controlled and reproducible environment, more suitable for behavior analysis.
In this paper, we propose an emulation platform for parallel and distributed systems where both the machines and the network are virtualized at a low level. We demonstrate that the use of virtual machines allows us to test highly accurate failure injection by "destroying" virtual machines. Failure accuracy is a criteria that demonstrates how realistic a fault is. The platform accuracy is evaluated using Pastry, a fault-tolerant distributed hash-table.

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cover image ACM Conferences
CF '09: Proceedings of the 6th ACM conference on Computing frontiers
May 2009
238 pages
ISBN:9781605584133
DOI:10.1145/1531743
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 18 May 2009

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Author Tags

  1. emulation
  2. fault injection
  3. large-scale applications
  4. virtualization

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CF '09
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CF '09: Computing Frontiers Conference
May 18 - 20, 2009
Ischia, Italy

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CF '09 Paper Acceptance Rate 26 of 113 submissions, 23%;
Overall Acceptance Rate 273 of 785 submissions, 35%

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