Scheduling anomaly detection and optimization for distributed systems with preemptive task-sets

Robustness and optimality are becoming the key principles in designing efficient and reliable state-of-the-art multi-processor real-time systems. However, due to complex inter-processor dependencies, the variation of local system parameters may have unpredictable system level impact including timing anomalies. In this context, the former heuristic optimization approaches used at resource level become less suitable for distributed systems with heterogeneous components and dynamic scheduling techniques. Techniques from exploration theory can better address the optimization problems but suffer from huge search spaces in general. In this paper, we present constructive methods for pointing out those system configurations that lead to anomalous behavior of the performance metrics. These are then used to guide the exploration process and reduce the search space, thereby increasing efficiency and making the approach applicable in practice. As a result, detailed information about anomalies can be quickly obtained and heavily exploited in system optimization, which we demonstrate using comprehensible examples.