Adaptive Resource Management Algorithms for Periodic Tasks in Dynamic Real-Time Distributed Systems

We present adaptive resource management middleware techniques for periodic tasks in dynamic real-time distributed systems. The techniques continuously monitor the application at run-time for adherence to the desired real-time requirements, detect timing failures or trends for impending failures (due to workload fluctuations), and dynamically allocate resources by replicating subtasks of application tasks for load sharing. The objective of the techniques is to minimize (end-to-end) missed deadline ratios of the tasks. We present "predictive" resource allocation algorithms that determine the number of subtask replicas that are required for adapting the application to a given workload situation using statistical regression theory. The algorithms use regression equations that forecast subtask timeliness as a function of external load parameters such as number of sensor reports and internal resource load parameters such as CPU utilization. To evaluate the performance of the predictive algorithms, we consider algorithms that determine the number of subtask replicas using empirically determined heuristic functions. We implemented the resource management algorithms as part of a middleware infrastructure and measured the performance of the algorithms using a real-time benchmark. The experimental results indicate that the predictive algorithms outperform the heuristic strategies under the workload conditions that were studied.

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