实际网格环境下基于rl的调度策略

2008 IEEE International Symposium on Parallel and Distributed Processing with Applications Pub Date : 2008-12-10 DOI:10.1109/ISPA.2008.119

B. F. Costa, I. Dutra, M. Mattoso

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引用次数: 3

摘要

在这项工作中，我们研究了在网格环境中进行作业编排时不同资源调度策略的行为。我们的经验证明，基于强化学习的调度策略是一个很好的选择，以提高网格应用程序的整体性能和资源利用率。

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RL-Based Scheduling Strategies in Actual Grid Environments

In this work, we study the behaviour of different resource scheduling strategies when doing job orchestration in grid environments. We empirically demonstrate that scheduling strategies based on reinforcement learning are a good choice to improve the overall performance of grid applications and resource utilization.

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来源期刊

2008 IEEE International Symposium on Parallel and Distributed Processing with Applications

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