Abstract
The job scheduling technology is an effective way to achieve resource sharing and to improve computational efficiency. Scheduling problem has been proved to be NP-complete problems, Particle Swarm Optimization (PSO) algorithm has demonstrated outstanding performance in solving such issues. In cognizance of the characteristics of cluster scheduling problem, a schedule strategy based on PSO was designed and implemented. Comparing with backfilling algorithm, PSO algorithm can improve the fairness of jobs better. It can avoid the problem that bigger jobs can’t be executed quickly. The speed and accuracy of strategy generation are improved significantly. The experiment results show that the scheduling strategy based on PSO algorithm can increase the utilization of the CPU and reduce average response time significantly.
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References
Liu, Z.-x., Wang, S.-m.: Research on parallel machines scheduling problem based on particle swarm optimization algorithm. Computer Integrated Manufacturing Systems 12(2), 183–185, 296 (2006)
Wu, Q.-d., Lei, W.: Research and Application of Intelligence Particle Swarm Optimization. Jiangsu Education Publishing House, Nan Jing (2005)
Zhang, L.-x., Yuan, L.-q., Xu, W.-m.: A Kind of Scheduling Strategy Based on the Type of the Job. Computer Engineering 30(13), 63–64, 115 (2004)
Yong, Y., Cai, Z.-x., Ying, F.: An Adaptive Grid Job Scheduling Method Based on Genetic Algorithm. Computer Engineering and Applications 1, 48–50, 167 (2005)
Hao, T.: Research on the Strategy of Grids Resource Management Scheduling Based on Genetic Algorithm. Journal of Wuhan University of Technology (Information & Management Engineering) 28(11), 16–19 (2006)
Liu, Z.-x.: Research and Application of Particle Swarm Optimization in Scheduling Problem. PhD thesis, Wuhan University of Technology, 46–64 (2005)
Feng, G., Chen, H.-p., Lu, B.-y.: Particle Swarm Optimization For Flexible Job Shop Scheduling. Systems Engineering 23(9), 20–23 (2005)
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© 2010 Springer-Verlag Berlin Heidelberg
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Liu, S., Wang, J., Li, X., Shuo, J., Liu, H. (2010). Design and Implement of a Scheduling Strategy Based on PSO Algorithm. In: Tan, Y., Shi, Y., Tan, K.C. (eds) Advances in Swarm Intelligence. ICSI 2010. Lecture Notes in Computer Science, vol 6146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13498-2_66
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DOI: https://doi.org/10.1007/978-3-642-13498-2_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13497-5
Online ISBN: 978-3-642-13498-2
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