Parallel Job Scheduling Using Grey Wolf Optimization Algorithm for Heterogeneous Multi-Cluster Environment
|Sapinderjit Kaur1 , Kirandeep Kaur2 , Amit.Chhabra 3|
1 Dept. of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, India.
2 Dept. of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, India.
3 Dept. of Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, India.
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Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-10 , Page no. 44-53, Oct-2017
Online published on Oct 30, 2017
Copyright © Sapinderjit Kaur, Kirandeep Kaur, Amit.Chhabra . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: Sapinderjit Kaur, Kirandeep Kaur, Amit.Chhabra, “Parallel Job Scheduling Using Grey Wolf Optimization Algorithm for Heterogeneous Multi-Cluster Environment”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.44-53, 2017.
MLA Style Citation: Sapinderjit Kaur, Kirandeep Kaur, Amit.Chhabra "Parallel Job Scheduling Using Grey Wolf Optimization Algorithm for Heterogeneous Multi-Cluster Environment." International Journal of Computer Sciences and Engineering 5.10 (2017): 44-53.
APA Style Citation: Sapinderjit Kaur, Kirandeep Kaur, Amit.Chhabra, (2017). Parallel Job Scheduling Using Grey Wolf Optimization Algorithm for Heterogeneous Multi-Cluster Environment. International Journal of Computer Sciences and Engineering, 5(10), 44-53.
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|Multi-cluster environment consists of computational nodes that allow computational problems with resource requirement more than those available resources in a cluster to be treated. Scheduling jobs in heterogeneous multi-cluster environments where each cluster has varied number of processors and each computational node has a varying speed is NP hard. Thus, we always search for sub-optimal solution for scheduling jobs. Various meta-heuristics have been proposed for scheduling jobs. The literature shows that the Genetic algorithm has been employed for parallel jobs scheduling in heterogeneous multi cluster environment. But it suffers from certain limitations like slow convergence speed, local optima problem. In this research work, a Grey Wolf Optimization algorithm (GWO) has been introduced in order to minimize makespan, flowtime and mean waiting time. The proposed methodology has shown quite significant improvement over available ones.|
|Key-Words / Index Term :|
|Heterogeneous multi-cluster environment, Scheduling, Co-allocation, Grey wolf Optimization Algorithm(GWOA))|
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