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A Self-Organizing Neural Network for Job Scheduling in Distributed Systems

Newman, Harvey B. and Legrand, Iosif C. (2001) A Self-Organizing Neural Network for Job Scheduling in Distributed Systems. In: Advanced Computing and Analysis Techniques in Physics Research. AIP Conference Proceedings. No.583. American Institute of Physics , Melville, N.Y., pp. 113-115. ISBN 0-7354-0023-7.

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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.

Item Type:Book Section
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Newman, Harvey B.0000-0003-0964-1480
Additional Information:© 2001 American Institute of Physics. Issue Date: 20 August 2001.
Subject Keywords:computer architecture, scheduling, distributed processing, neural net architecture
Series Name:AIP Conference Proceedings
Issue or Number:583
Classification Code:PACS: 07.05.Mh; 07.05.Bx; 07.05.Kf
Record Number:CaltechAUTHORS:20111118-133238020
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Official Citation:A self-organizing neural network for job scheduling in distributed systems Harvey B. Newman and Iosif C. Legrand, AIP Conf. Proc. 583, 113 (2001), DOI:10.1063/1.1405278
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:27861
Deposited By: Ruth Sustaita
Deposited On:21 Nov 2011 00:07
Last Modified:09 Nov 2021 16:52

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