Prakash, Puneet, authorDewri, Rinku, authorAlqudah, Amin Torki Yousef, authorGovindasamy, Sudha, authorJanovy, David Leon, authorSutton, Andrew Michael, authorLadd, Joshua Samuel, authorShestak, Vladimir Vladimirov, authorRenner, Timothy, authorSiegel, Howard Jay, authorMaciejewski, Anthony A., authorBriceño, Luis Diego, authorSmith, Jay, authorIEEE, publisher2007-01-032007-01-032007Smith, 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.http://hdl.handle.net/10217/2453Heterogeneous 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.born digitalproceedings (reports)eng©2007 IEEE.Copyright 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.computational complexityBayes methodsprocessor schedulingregression analysisresource allocationstochastic processesText