使用遗传算法的模糊批处理作业调度器自动调优

18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397) Pub Date : 1999-06-10 DOI:10.1109/NAFIPS.1999.781671

A. Shaout, P. McAuliffe

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引用次数: 4

摘要

本文应用遗传算法对模糊批处理调度程序进行自动调优，以获得最大吞吐量。该遗传算法通过改变处理器上的模糊隶属函数、模糊规则和资源限制来优化分布式系统的最大作业吞吐量和负载平衡。与大多数在负载平衡和作业调度领域所做的研究不同，本文提出的算法已经在生产处理环境中进行了评估，而不仅仅是在模拟中。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Automatic tuning of a fuzzy batch job scheduler using a genetic algorithm

The paper presents the application of a genetic algorithm to automatically tune a fuzzy batch job scheduler for maximum throughput. This genetic algorithm varies fuzzy membership functions, fuzzy rules and resource limits on processors to optimize for maximum job throughput and load balancing across processors of a distributed system. Unlike most research done in the realm of load balancing and job scheduling, the paper presents an algorithm that has been evaluated in a production processing environment rather than in simulation only.

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18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397)

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