Adaptive Scheduling of Task Graphs with Dynamic Resilience

This paper studies a scheduling problem of task graphs on a nondedicated networked computing platform. The networked platform is characterized by a set of fully connected processors such as a multiprocessor system that can be shared by multiple tasks. Therefore, the computation and communication capacities of the computing platform dynamically fluctuate. To deal with this fluctuations for high performance task graph computing, we propose an online dynamic resilience scheduling algorithm called Adaptive Scheduling Algorithm (ASA) that bears certain distinct features compared to existing algorithms. First, the proposed algorithm deliberately assigns tasks to idle processors in multiple rounds to prevent any unfavorable decisions and also to avoid inefficient assignments of certain key tasks to slow processors. Second, the algorithm adopts task duplication as an attempt to minimize serious increase of schedule length due to unexpected processor slowdown. Finally, a look-ahead message transmission policy is applied to save communication time and further improve the overall performance. Performance evaluation results are presented to demonstrate the effectiveness and competitiveness of our approaches when compared with the existing algorithms.