A self-organizing neural network for job scheduling in distributed systems

被引：0

作者：

Newman, HB ^{[1
]}

Legrand, IC ^{[1
]}

机构：

[1] CALTECH, Charles C Lauristsen Lab High Energy Phys, Pasadena, CA 91125 USA

来源：

ADVANCED COMPUTING AND ANALYSIS TECHNIQUES IN PHYSICS RESEARCH | 2001年 / 583卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The aim of this work is to describe a possible approach for the optimization of the job scheduling in large distributed systems, based on a self-organizing Neural Network. This dynamic scheduling system should be seen as adaptive middle layer software, aware of current available resources and making the scheduling decisions using the "past experience. It aims to optimize job specific parameters as well as the resource utilization. The scheduling system is able to dynamically learn and cluster information in a large dimensional parameter space and at the same time to explore new regions in the parameters space. This self-organizing scheduling system may offer a possible solution to provide an effective use of resources for the off-line data processing jobs for future HEP experiments.

引用

页码：113 / 115

页数：3

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