Measuring the robustness of resource allocations in a stochastic dynamic environment Smith, Jay ; Briceño, Luis Diego ; Maciejewski, Anthony A. ; Siegel, Howard Jay ; Renner, Timothy ; Shestak, Vladimir Vladimirov ; Ladd, Joshua Samuel ; Sutton, Andrew Michael ; Janovy, David Leon ; Govindasamy, Sudha ; Alqudah, Amin Torki Yousef ; Dewri, Rinku ; Prakash, Puneet "This 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." 10 p. 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. Colorado State University. Libraries 2007 text ; image application/pdf ECEaam00125.pdf FACFECEN100125ARTI eng c 2007
Measuring the robustness of resource allocations in a stochastic dynamic environment
Smith, Jay ; Briceño, Luis Diego ; Maciejewski, Anthony A. ; Siegel, Howard Jay ; Renner, Timothy ; Shestak, Vladimir Vladimirov ; Ladd, Joshua Samuel ; Sutton, Andrew Michael ; Janovy, David Leon ; Govindasamy, Sudha ; Alqudah, Amin Torki Yousef ; Dewri, Rinku ; Prakash, Puneet
"This 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."
10 p.
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.
Colorado State University. Libraries
2007
text ; image
application/pdf
ECEaam00125.pdf
FACFECEN100125ARTI
eng
c 2007