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

  • Authors:
  • Yacine Laalaoui;Habiba Drias

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

  • Venue:
  • International Journal of Bio-Inspired Computation
  • Year:
  • 2010

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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.