Stochastic robustness metric and its use for static resource allocations

  • Authors:

  • Vladimir Shestak;Jay Smith;Anthony A. Maciejewski;Howard Jay Siegel

  • Affiliations:

  • Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, United States and InfoPrint Solutions Company 6300 Diagonal Highway Boulder, CO 80301, United ...;Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, United States and Digital Globe 1601 DryCreek Rd Longmont, CO 80503, United States;Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, United States;Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, United States and Department of Computer Science, Colorado State University, Fort Collins, CO ...

  • Venue:
  • Journal of Parallel and Distributed Computing
  • Year:
  • 2008

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Abstract

This research investigates the problem of robust static resource allocation for distributed computing systems operating under imposed Quality of Service (QoS) constraints. Often, such systems are expected to function in a physical environment replete with uncertainty, which causes the amount of processing required to fluctuate substantially over time. Determining a resource allocation that accounts for this uncertainty in a way that can provide a probabilistic guarantee that a given level of QoS is achieved is an important research problem. The stochastic robustness metric proposed in this research is based on a mathematical model where the relationship between uncertainty in system parameters and its impact on system performance are described stochastically. The utility of the established metric is then exploited in the design of optimization techniques based on greedy and iterative approaches that address the problem of resource allocation in a large class of distributed systems operating on periodically updated data sets. The performance results are presented for a simulated environment that replicates a heterogeneous cluster-based radar data processing center. A mathematical performance lower bound is presented for comparison analysis of the heuristic results. The lower bound is derived based on a relaxation of the Integer Linear Programming formulation for a given resource allocation problem.