Scheduling jobs on computational grids using fuzzy particle swarm algorithm
- Authors
- Abraham, Ajith; Liu, Hongbo; Zhang, Weishi; Chang, Tae-Gyu
- Issue Date
- Oct-2006
- Publisher
- SPRINGER-VERLAG BERLIN
- Citation
- KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 2, PROCEEDINGS, v.4252, pp 500 - 507
- Pages
- 8
- Journal Title
- KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 2, PROCEEDINGS
- Volume
- 4252
- Start Page
- 500
- End Page
- 507
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/53304
- DOI
- 10.1007/11893004_65
- ISSN
- 0302-9743
1611-3349
- Abstract
- Grid computing is a computing framework to meet the growing computational demands. This paper introduces a novel approach based on Particle Swarm Optimization (PSO) for scheduling jobs on computational grids. The representations of the position and velocity of the particles in the conventional PSO is extended from the real vectors to fuzzy matrices. The proposed approach is to dynamically generate an optimal schedule so as to complete the tasks within a minimum period of time as well as utilizing the resources in an efficient way. We evaluate the performance of the proposed PSO algorithm with Genetic Algorithm (GA) and Simulated Annealing (SA) approaches.
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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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