W. Sun, Y. Inoguchi, and Y. Zhang (Japan)
Grid computing, task scheduling, genetic algorithm, high throughput, and utilization
Grid computing inevitably evolves to an infrastructure that satisfies various kinds of requirements, thus demanding more complex resource management. The scheduler introduced here is designed to serve a local resource domain in complex resource management. It uses MCT (Minimum Completion Time) for the immediate mode scheduling and an improved GA (Genetic Algorithm) for the batch mode scheduling to obtain the highest possible throughput and utilization. We created a simulation and compared our scheduler with three other benchmark algorithms. The experimental results indicate that our scheduler can achieve higher throughput and utilization than the benchmark algorithms subject to varying task flow, except when the arrival rate of tasks is very low. The dynamic scheduling cycle and batch size make the scheduler more adaptive for task flow varying, and decrease the number of tasks missing deadlines when resources fluctuate. The improved genetic algorithm is the key to obtain better results in a busy task flow.
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