Back to Search
Start Over
A Hybrid Algorithm Using Genetic Algorithm and Cuckoo Search Algorithm to Solve Job Scheduling Problem in Computational Grid Systems
- Source :
- International Journal of Applied Evolutionary Computation. 7:1-11
- Publication Year :
- 2016
- Publisher :
- IGI Global, 2016.
-
Abstract
- Job 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.
- Subjects :
- Job scheduler
Rate-monotonic scheduling
Mathematical optimization
Job shop scheduling
Computer science
Ant colony optimization algorithms
020206 networking & telecommunications
02 engineering and technology
Dynamic priority scheduling
computer.software_genre
Hybrid algorithm
Fair-share scheduling
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Cuckoo search
computer
Algorithm
Subjects
Details
- ISSN :
- 19423608 and 19423594
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- International Journal of Applied Evolutionary Computation
- Accession number :
- edsair.doi...........1e31194bb4fc970d00f5bad64c5da9f9