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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 an environment where uncertainties in system parameters are common. In such an environment, the amount of processing required to complete a task 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 QoS is achieved - is an important area of research. The authors present two techniques for maximizing the probability that a given level of QoS is achieved. The performance results for the techniques are presented for a simulated environment that models a heterogeneous cluster-based radar data processing center.
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