Grid Task Scheduling Algorithm R3Q for Evolving Artificial Neural Networks
Task scheduling algorithms for evolving artificial neural networks (EANNs) in grid computing environments is discussed. In this paper, list scheduling with round-robin order replication (RR) is adopted to reduce waiting times due to synchronization. However, RR is suitable for coarse-grained tasks. For EANNs as medium-grained tasks, we propose a new technique to reduce the communication overhead, called the remote work queue (RWQ) method. We then define round-robin replication remote work queue (R3Q) as RWQ with RR. Our results show that R3Q can reduce both the synchronous waiting time and communication time %, and provides efficient forced termination of tasks compared to other methods.
KeywordsSchedule Algorithm Recurrent Neural Network Grid Environment Total Execution Time List Schedule
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