Measuring the robustness of resource allocations in a stochastic dynamic environment

Jay Smith, Luis D. Briceño, Anthony A. Maciejewski, Howard Jay Siegel, Timothy Renner, Vladimir Shestak, Joshua Ladd, Andrew Sutton, David Janovy, Sudha Govindasamy, Amin Alqudah, Rinku Dewri, Puneet Prakash

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Heterogeneous distributed computing systems often must operate in an environment where system parameters are subject to uncertainty. Robustness can be defined as the degree to which a system can function correctly in the presence of parameter values different from those assumed. We present a methodology for quantifying the robustness of resource allocations in a dynamic environment where task execution times are stochastic. The methodology is evaluated through measuring the robustness of three different resource allocation heuristics within the context of a stochastic dynamic environment. A Bayesian regression model is fit to the combined results of the three heuristics to demonstrate the correlation between the stochastic robustness metric and the presented performance metric. The correlation results demonstrated the significant potential of the stochastic robustness metric to predict the relative performance of the three heuristics given a common objective function.

Original languageEnglish (US)
Title of host publicationProceedings - 21st International Parallel and Distributed Processing Symposium, IPDPS 2007; Abstracts and CD-ROM
DOIs
StatePublished - 2007
Event21st International Parallel and Distributed Processing Symposium, IPDPS 2007 - Long Beach, CA, United States
Duration: Mar 26 2007Mar 30 2007

Publication series

NameProceedings - 21st International Parallel and Distributed Processing Symposium, IPDPS 2007; Abstracts and CD-ROM

Other

Other21st International Parallel and Distributed Processing Symposium, IPDPS 2007
CountryUnited States
CityLong Beach, CA
Period3/26/073/30/07

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