By Topic

A new approach for task scheduling in distributed systems using learning automata

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Jahanshahi, M. ; Dept. of Comput. Eng., Islamic Azad Univ., Tehran, Iran ; Meybodi, M.R. ; Dehghan, M.

Tasks scheduling problem is a key factor for a distributed system in order to achieve better efficiency. The problem of tasks scheduling in a distributed system can be stated as allocating tasks to processor of each computer. The objective of this problem is minimizing Makespan and communication cost while maximizing CPU utilization. Scheduling problem is known as NP-complete. Hence, many genetic algorithms have been proposed to search optimal solutions from entire solution space. However, the existing approaches are going to scan the entire solution space without consideration to techniques that can reduce the complexity of the optimization. In other words, the main weakness of these methods is to spend much time doing scheduling and hence need to exhaustive time. In this paper we use Learning algorithm to cope with the weakness of GA based method. In fact we use the Learning automata as local search in the memetic algorithm. Experimental results prove that the proposed method outperforms the existent GA based method in terms of communication cost, CPU utilization and Makespan.

Published in:

Automation and Logistics, 2009. ICAL '09. IEEE International Conference on

Date of Conference:

5-7 Aug. 2009