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Fuzzy-logic optical optimization of mainframe CPU and memory

机译:大型机CPU和内存的模糊逻辑光学优化

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The allocation of CPU time and memory resources is a familiar problem in organizations with a large number of users and a single mainframe. Usually the amount of resources allocated to a single user is based on the user's own statistics not on the statistics of the entire organization, therefore patterns are not well identified and the allocation system is prodigal. A fuzzy-logic-based algorithm to optimize the CPU and memory distribution among users based on their history is suggested. The algorithm works on heavy and light users separately since they present different patterns to be observed. The result is a set of rules generated by the fuzzy-logic inference engine that will allow the system to use its computing ability in an optimized manner. Test results on data taken from the Faculty of Engineering of Tel Aviv University demonstrate the capabilities of the new algorithm.
机译:在具有大量用户和一个大型机的组织中,CPU时间和内存资源的分配是一个常见的问题。通常,分配给单个用户的资源量是基于用户自己的统计信息,而不是基于整个组织的统计信息,因此,无法很好地识别模式,分配系统是浪费的。提出了一种基于模糊逻辑的算法,可根据用户的历史记录优化用户之间的CPU和内存分配。该算法分别适用于重度和轻度用户,因为他们呈现出不同的待观察模式。结果是由模糊逻辑推理引擎生成的一组规则,这些规则将允许系统以优化的方式使用其计算能力。来自特拉维夫大学工程学院的数据测试结果证明了该新算法的功能。

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