Paper Info
Title
A Hybrid Algorithm Using Genetic Algorithm and Cuckoo Search Algorithm to Solve Job Scheduling Problem in Computational Grid Systems
Abstract
AbstractJob scheduling is one of the major challenges in Grid computing systems to efficiently exploit the capabilities of dynamic, autonomous, heterogeneous and distributed resources for execution of different types of jobs. Thus optimal job scheduling is an NP-complete problem which can easily be solved by using heuristic techniques. This paper presents a hybrid algorithm for job scheduling using Genetic Algorithm GA and Cuckoo Search Algorithm CSA for efficiently allocating jobs to resources in a Grid system so that makespan and flowtime are minimized. This proposed algorithm combines the advantages of both GA and CSA. The authors' results have been compared with standard GA, CSA and Ant Colony Optimization ACO to show the importance of the proposed algorithm.
Year
DOI
Venue
2016
10.4018/IJAEC.2016040101
Periodicals
Field
DocType
Volume
Ant colony optimization algorithms,Mathematical optimization,Hybrid algorithm,Job shop scheduling,Fair-share scheduling,Computer science,Algorithm,Cuckoo search,Job scheduler,Rate-monotonic scheduling,Dynamic priority scheduling
Journal
7
Issue
ISSN
Citations 
2
1942-3594
2
PageRank 
References 
Authors
0.37
10
2
Name
Order
Citations
PageRank
Tarun Kumar Ghosh141.42
Sanjoy Das222639.18