Reference Point Based Multi-Objective Optimization to Workflow Grid Scheduling

Ritu Garg and Awadhesh Kumar Singh
Additional contact information
Ritu Garg: National Institute of Technology, Kurukshetra, India
Awadhesh Kumar Singh: National Institute of Technology, Kurukshetra, India

International Journal of Applied Evolutionary Computation (IJAEC), 2012, vol. 3, issue 1, 80-99

Abstract: Grid provides global computing infrastructure for users to avail the services supported by the network. The task scheduling decision is a major concern in heterogeneous grid computing environment. The scheduling being an NP-hard problem, meta-heuristic approaches are preferred option. In order to optimize the performance of workflow execution two conflicting objectives, namely makespan (execution time) and total cost, have been considered here. In this paper, reference point based multi-objective evolutionary algorithms, R-NSGA-II and R-e-MOEA, are used to solve the workflow grid scheduling problem. The algorithms provide the preferred set of solutions simultaneously, near the multiple regions of interest that are specified by the user. To improve the diversity of solutions we used the modified form of R-NSGA-II (represented as M-R-NSGA-II). From the simulation analysis it is observed that, compared to other algorithms, R-e-MOEA delivers better convergence, uniform spacing among solutions keeping the computation time limited.

Date: 2012
References: Add references at CitEc
Citations:

Downloads: (external link)
http://services.igi-global.com/resolvedoi/resolve. ... 4018/jaec.2012010105 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:igg:jaec00:v:3:y:2012:i:1:p:80-99

Access Statistics for this article

International Journal of Applied Evolutionary Computation (IJAEC) is currently edited by Sukhpal Singh Gill

More articles in International Journal of Applied Evolutionary Computation (IJAEC) from IGI Global
Bibliographic data for series maintained by Journal Editor ().