MULTIPROCESSOR SCHEDULING BY MEAN FIELD THEORY

Zeeman Z. Zhang, Nirwan Ansari, Edwin Hou, Pei Ken Yi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we develop an optimization scheme based on Mean Field Theory (MFT) to solve the Task Scheduling Problem. The algorithm combines characteristics of the Simulated Annealing (SA) algorithm and the Hopfield neural network. The temperature behavior of MFT for the task scheduling problem is shown to possess a critical temperature (Tc) below which an optimal solution may be achieved. The algorithm has been applied to various task graphs, and promising results have been obtained.

Original languageEnglish (US)
Title of host publicationProceedings - 1992 International Joint Conference on Neural Networks, IJCNN 1992
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages582-587
Number of pages6
ISBN (Electronic)0780305590
DOIs
StatePublished - 1992
Event1992 International Joint Conference on Neural Networks, IJCNN 1992 - Baltimore, United States
Duration: Jun 7 1992Jun 11 1992

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume4

Conference

Conference1992 International Joint Conference on Neural Networks, IJCNN 1992
Country/TerritoryUnited States
CityBaltimore
Period6/7/926/11/92

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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