Cart (Loading....) | Create Account
Close category search window

Improved Genetic Algorithms and List Scheduling Techniques for Independent Task Scheduling in Distributed Systems

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)
Loukopoulos, T. ; Univ. of Thessaly, Volos ; Lampsas, P. ; Sigalas, P.

Given a set of tasks with certain characteristics, e.g., data size, estimated execution time and a set of processing nodes with their own parameters, the goal of task scheduling is to allocate tasks at nodes so that the total makespan is minimized. The problem has been studied under various assumptions concerning task and node parameters with the resulting problem statements usually being NP-complete. List scheduling (LS) heuristics such as MaxMin and MinMin together with genetic algorithms (GAs) were applied in the past to find solutions. In this paper we investigate new heuristics for both the LS and the GA paradigm with the specific aim of improving the performance of the standard algorithms when task computations involve large data transfers. Experimental results under various environment assumptions illustrate the merits of the new algorithms.

Published in:

Parallel and Distributed Computing, Applications and Technologies, 2007. PDCAT '07. Eighth International Conference on

Date of Conference:

3-6 Dec. 2007

Need Help?

IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.