This research investigates the problem of robust dynamic resource allocation for heterogeneous distributed computing systems operating under imposed constraints. Often, such systems are expected to function in an environment where uncertainty in system parameters is common. In such an environment, the amount of processing required to complete an application may fluctuate substantially. Determining a resource allocation that accounts for this uncertainty---in a way that can provide a probability that a given level of service is achieved---is an important area of research. We define a mathematical model of stochastic robustness appropriate for a dynamic environment that can be used during resource allocation to aid heuristic decision making. In addition, we design a novel technique for maximizing stochastic robustness in this environment. Our performance results for this technique are compared with several well known resource allocation techniques in a simulated environment that models a heterogeneous distributed computing system.
['Jay Smith', 'Edwin K. P. Chong', 'Anthony A. Maciejewski', 'Howard Jay Siegel']
Stochastic-Based Robust Dynamic Resource Allocation in a Heterogeneous Computing System