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Multiprocessor scheduling using mean-field annealing

  • Shaharuddin Salleh
  • Albert Y. Zomaya
Workshop on Biologically Inspired Solutions to Parallel Processing Problems Albert V. Zomaya, The University of Western Australia Fikret Ercal, University of Missouri-Rolla Stephan Olariu, Old Dominion University
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1388)

Abstract

This paper presents our work on the static task scheduling model using the mean-field annealing (MFA) technique. Mean-field annealing is a technique of thermostatic annealing that takes the statistical properties of particles as its learning paradigm. It combines good features from the Hopfield neural network and simulated annealing, to overcome their weaknesses and improve on their performances. Our MFA model for task scheduling is derived from its prototype, namely, the graph partitioning problem. MFA is deterministic in nature and this gives the advantage of faster convergence to the equilibrium temperature, compared to simulated annealing. Our experimental work verifies this finding on various network and task graph sizes. Our work also includes the simulation of the MFA model on several network topologies and parameters.

Keywords

task scheduling mean-field annealing and graph partitioning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Shaharuddin Salleh
    • 1
  • Albert Y. Zomaya
    • 2
  1. 1.Dept of Mathematics Faculty of ScienceUniversity of Technology MalaysiaJohor BahruMalaysia
  2. 2.Parallel Computing Research Lab. Dept of Electrical & Electronics Eng.University of Western AustraliaPerthAustralia

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