Greedy Approaches to Static Stochastic Robust Resource Allocation for Periodic Sensor Driven Distributed Systems

This research investigates the problem of robust resource allocation for a large class of systems operating on periodically updated data sets under an imposed quality of service (QoS) constraint. Such systems are expected to function in an environment replete with uncertainty where the workload is likely to fluctuate substantially. 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. First, this paper defines a methodology for quantifiably determining a resource allocation’s ability to satisfy QoS constraint in the midst of uncertainty in system parameters. Uncertainty in system parameters and its impact on system performance are modeled stochastically. Second, the established stochastic model is employed to develop greedy resource allocation heuristics. Finally, the utility of the proposed stochastic robustness metric and the performance of the heuristics are evaluated in a simulated environment that replicates a heterogeneous cluster-based radar system.