Repository logo
 

Measuring the robustness of resource allocations in a stochastic dynamic environment

Simple item page
dc.contributor.authorPrakash, Puneet, author
dc.contributor.authorDewri, Rinku, author
dc.contributor.authorAlqudah, Amin Torki Yousef, author
dc.contributor.authorGovindasamy, Sudha, author
dc.contributor.authorJanovy, David Leon, author
dc.contributor.authorSutton, Andrew Michael, author
dc.contributor.authorLadd, Joshua Samuel, author
dc.contributor.authorShestak, Vladimir Vladimirov, author
dc.contributor.authorRenner, Timothy, author
dc.contributor.authorSiegel, Howard Jay, author
dc.contributor.authorMaciejewski, Anthony A., author
dc.contributor.authorBriceño, Luis Diego, author
dc.contributor.authorSmith, Jay, author
dc.contributor.authorIEEE, publisher
dc.date.accessioned2007-01-03T04:37:43Z
dc.date.available2007-01-03T04:37:43Z
dc.date.issued2007
dc.description.abstractHeterogeneous 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.
dc.description.sponsorshipThis research was supported by the NSF under Contract No: CNS-0615170, by the Colorado State University Center for Robustness in Computer Systems (funded by the Colorado Commission on Higher Education Technology Advancement Group through the Colorado Institute of Technology), and by the Colorado State University George T. Abell Endowment.
dc.format.mediumborn digital
dc.format.mediumproceedings (reports)
dc.identifier.bibliographicCitationSmith, Jay, et al., Measuring the Robustness of Resource Allocations in a Stochastic Dynamic Environment, Proceedings: 21st International Parallel and Distributed Processing Symposium (IPDPS 2007 California), March 26-30, 2007, Long Beach, California: 10 p.
dc.identifier.urihttp://hdl.handle.net/10217/2453
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights©2007 IEEE.
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectcomputational complexity
dc.subjectBayes methods
dc.subjectprocessor scheduling
dc.subjectregression analysis
dc.subjectresource allocation
dc.subjectstochastic processes
dc.titleMeasuring the robustness of resource allocations in a stochastic dynamic environment
dc.typeText

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ECEaam00125.pdf
Size:
959.08 KB
Format:
Adobe Portable Document Format
Description:
Download

Collections

Publications