Iterative Techniques for Maximizing Stochastic Robustness of a Static Resource Allocation in Periodic Sensor Driven Clusters

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 is 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. We present two techniques for maximizing the probability that a given level of QoS is achieved. The performance results for our techniques are presented for a simulated environment that models a heterogeneous clusterbased radar data processing center.