Dewri, Rinku, authorAlqudah, Amin, authorGovindasamy, Sudha, authorJanovy, David, authorSutton, Andrew, authorLadd, Joshua, authorPrakash, Puneet, authorRenner, Timothy, authorSiegel, Howard Jay, authorMaciejewski, Anthony A., authorBriceño, Luis D., authorSmith, Jay, authorShestak, Vladimir, author2007-01-032007-01-032006http://hdl.handle.net/10217/89372Heterogeneous distributed computing systems often must function in an environment where system parameters are subject to variations during operation. 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 vary within predictable ranges and tasks arrive randomly. The methodology is evaluated through measuring the robustness of three different resource allocation heuristics within the context of the stochastically modeled 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 digitalStudent workspostersengCopyright 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.stochastic robustness metricheterogeneous distributed computingheuristicsText