Title: ACO approach with learning for preemptive scheduling of real-time tasks

Authors: Yacine Laalaoui, Habiba Drias

Addresses: National Computer Science School (ESI), 16000 Oued-Smar, Algiers, Algeria. ' LRIA Laboratory, Faculty of Computer Science, USTHB University, 16111 El-Alia, Babezzouar, Algiers, Algeria

Abstract: This paper presents an ACO algorithm to search for feasible schedules of n real-time tasks on M identical processors. Unlike existing works, the proposed algorithm addresses the problem of preemptive scheduling rather than non-preemptive scheduling. A learning technique is integrated to detect and postpone possible preemptions between tasks. The proposed learning technique is also used to develop a necessary condition for the schedulability of the input task set. Experimental results show a significant success ratio improvement of the proposed scheduling algorithm.

Keywords: ACO; ant colony optimisation; learning; hard real-time; preemptive scheduling; multiprocessor systems; feasible schedules.

DOI: 10.1504/IJBIC.2010.037018

International Journal of Bio-Inspired Computation, 2010 Vol.2 No.6, pp.383 - 394

Published online: 21 Nov 2010 *

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