Abstract
Grid computing has been evolved as a high performance computing to fulfill the demand of computational resources among the geographically dispersed virtual organizations. Grid is used to provide solutions to the complex computational intensive problems. Scheduling of user applications on the distributed resources is an indispensable issue in Grid environment. In this paper, a speed-constrained multi-objective particle swarm optimization (SMPSO) technique-based scheduler is proposed to find efficient schedules that minimizes makespan, flowtime, resource usage cost and maximizes resource utilization in Grid environment. The work is integrated in ALEA 3.0 Grid Scheduling simulator. The results of the proposed approach have been contrasted with Grid’s conventional scheduling algorithms like FCFS, EDF, MinMin, and other multi-objective algorithms like NSGA-II and SPEA2. The results discussed in the paper shows that SMPSO outperforms over other scheduling techniques.
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Mandeep Kaur (2017). Multi-objective Evolution-Based Scheduling of Computational Intensive Applications in Grid Environment. In: Satapathy, S., Bhateja, V., Joshi, A. (eds) Proceedings of the International Conference on Data Engineering and Communication Technology. Advances in Intelligent Systems and Computing, vol 469. Springer, Singapore. https://doi.org/10.1007/978-981-10-1678-3_44
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DOI: https://doi.org/10.1007/978-981-10-1678-3_44
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