A Predictive Algorithm for Adaptive Resource Management of Periodic Tasks in Asynchronous Real-Time Distributed Systems

Real-time distributed applications such as those that are emerging for managing the entire mission of a system are characterized by significant execution-time uncertainties in the application environment and system resource state. Thus, such systems require adaptive resource management that dynamically monitor the system for adherence to the desired real-time requirements and perform run-time adaptation of the application to changing workloads when unacceptable timeliness behavior is observed. In this paper, we present a “predictive” resource management algorithm that forecasts the timeliness behavior of periodic tasks during the resource allocation process and select allocations that yield the optimal forecasted timeliness. The algorithm uses statistical regression theory-based techniques for predicting task timeliness. The algorithm uses regression equations that are based on external load parameters such as number of sensor reports and internal resource load parameters such as CPU utilization. The regression equations are determined from application profile data that is obtained by measuring the timeliness of the application for a set of external and internal load situations. The performance of the predictive algorithm is studied by comparing with a non-predictive resource management algorithm that uses heuristic rules for allocating resources. The experimental results indicate that the predictive algorithm outperforms the non-predictive algorithm when the workload shows fluctuating behavior.