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

Task scheduling with Load balancing for computational grid using NSGA II with fuzzy mutation

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)
Salimi, R. ; Coll. of Comput. Sci., Tabari Inst. of Higher Educ., Babol, Iran ; Motameni, H. ; Omranpour, H.

The resources management in a grid computing is a complicated problem. Scheduling algorithms play important role in the parallel distributed computing systems for scheduling jobs, and dispatching them to appropriate resources. An efficient task scheduling algorithm is needed to reduce the total Time and Cost for job execution and improve the Load balancing between resources in the grid. In grid computing, load balancing is a technique to distribute workload fairly across computational resources, in order to obtain optimal resource utilization with minimum response time, and avoid overload. Load balancing is a crucial problem to grid computing. In this paper, we address scheduling problem of independent tasks in the market-based grid. In market grids, resource providers can request payment from users based on the amount of computational resource that used by them. Beside we consider Makespan and Load balancing. In this paper, NSGA II with Fuzzy Adaptive Mutation Operator is used to address independent task assignments problems in parallel distributed computing systems. Results obtained proved that our innovative algorithm converges to Pareto-optimal solutions faster and with more quality.

Published in:

Parallel Distributed and Grid Computing (PDGC), 2012 2nd IEEE International Conference on

Date of Conference:

6-8 Dec. 2012

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.