Maximizing stochastic robustness of static resource allocations in a periodic sensor driven cluster

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 uncertainty 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 have designed novel techniques for maximizing the probability that a given level of QoS is achieved. These techniques feature a unique application of both path relinking and local search within a Genetic Algorithm. In addition, we define a new methodology for finding resource allocations that are guaranteed to have a non-zero probability of addressing the timing constraints of the system. We demonstrate the use of this methodology within two unique steady-state genetic algorithms designed to maximize the robustness of resource allocations. The performance results for our techniques are presented for a simulated environment that models a heterogeneous cluster-based radar data processing center.