Scheduling data-intensive work-flow applications using particle swarm approaches

Authors:
Hongbo Liu;Ajith Abraham;Okkyung Choi;Seong Hwan Moon
Affiliations:
Department of Computer Science, Dalian University of Technology, Dalian, China;School of Computer Science and Engineering, Chung-Ang University, Seoul, Korea;School of Computer Science and Engineering, Chung-Ang University, Seoul, Korea and Department of Science and Technology, Education for Life, Seoul National University of Education, Seoul, Korea;Department of Science and Technology, Education for Life, Seoul National University of Education, Seoul, Korea
Venue:
ICCOM'06 Proceedings of the 10th WSEAS international conference on Communications
Year:
2006

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Abstract

Data-intensive work-flow applications increase continuously in various domains. The job scheduling problem usually has to take into account of the computational loads at each computing resource, the distributions of data required by each job, and the work-flow constraints. The scheduling problem is one of the major difficult tasks in these types of distributed computing environments. This paper formulates the scheduling problem for data-intensive work-flow applications (DFSP) and solve the problem using particle swarm optimization approaches. The details of implementation for DFSP are provided and the corresponding computational experiments are reported. The results indicate that the proposed algorithm is an efficient approach for DFSP.