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Multi-Objective Job Scheduler using Genetic Algorithm in Grid Computing

by Pritibahen Sumanbhai Patel
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 92 - Number 14
Year of Publication: 2014
Authors: Pritibahen Sumanbhai Patel
10.5120/16079-5312

Pritibahen Sumanbhai Patel . Multi-Objective Job Scheduler using Genetic Algorithm in Grid Computing. International Journal of Computer Applications. 92, 14 ( April 2014), 34-43. DOI=10.5120/16079-5312

@article{ 10.5120/16079-5312,
author = { Pritibahen Sumanbhai Patel },
title = { Multi-Objective Job Scheduler using Genetic Algorithm in Grid Computing },
journal = { International Journal of Computer Applications },
issue_date = { April 2014 },
volume = { 92 },
number = { 14 },
month = { April },
year = { 2014 },
issn = { 0975-8887 },
pages = { 34-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume92/number14/16079-5312/ },
doi = { 10.5120/16079-5312 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:14:20.303889+05:30
%A Pritibahen Sumanbhai Patel
%T Multi-Objective Job Scheduler using Genetic Algorithm in Grid Computing
%J International Journal of Computer Applications
%@ 0975-8887
%V 92
%N 14
%P 34-43
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents multi-objective Job scheduler using Genetic Algorithm which provides efficient utilization of resources by completing the different tasks in a minimum period of time. Grid is a kind of distributed system that provides the sharing of geographically distributed independent resources dynamically at runtime depending on their availability, capability, performance and cost. Scheduling is a key problem in evolving grid computational systems. Dealing with the multiple criteria in a heterogeneous and dynamic environment like Grid is very complex and computationally hard. There are ample approaches for Job scheduling like Genetic Algorithm (GA), Simulated Annealing (SA), Ant Colony optimization (ACO) and Particle Swarm Optimization (PSO) Algorithm. This paper presents Genetic algorithm for designing efficient multi-objective job schedulers by considering multiple parameter like makespan and flow time to find optimal/nearly optimal schedule. It searches solution space in parallel and solution can be found more quickly.

References
  1. Javier Carretero, Fatos Xhafa, Ajith Abraham. Genetic algorithm based schedulers for grid computing systems. In International Journal of Innovative Computing, Information and Control ICIC International °c 2005 ISSN 1349-4198 Volume 3, Number 5, October 2007.
  2. Jing Liu, Li Chen, Yuqing Dun, Lingmin Liu, Ganggang Dong. The Research of Ant Colony and Genetic Algorithm in Grid Task Scheduling. In International Conference on MultiMedia and Information Technology 2008.
  3. S. Prabhu, V. Naveen Kumar. Optimization Based on Genetic Algorithm in Grid Scheduling. International Journal of advanced research in technology. IJART, Vol. 1 Issue 1, 2011. ISSN NO: 6602 3127 RR.
  4. Abraham, A. H. Liu, W. Zhang and T. G. Chang, Job scheduling on computational grids using fuzzy particle swarm algorithm, Proc. of the 10th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, B. Gabrys et al. (eds. ): Part II, Lecture Notes on Artificial Intelligence 4252, 500507, Springer, 2006.
  5. Jia Yu and Rajkumar Buyya and Kotagiri Ramamohanarao. Workflow Schdeduling Algorithms for Grid Computing. Grid Computing and Distributed Systems (GRIDS) Laboratory Department of Computer Science and Software Engineering, The University of Melbourne, Australia.
  6. Guangchang Ye, Ruonan Rao, Minglu Li. A Multiobjective Resources Scheduling Approach Based on Genetic Algorithms in Grid Environment. In Fifth International Conference on Grid and Cooperative Computing Workshops (GCCW'06) IEEE computer society.
  7. Taras S. Shapovalov, Alexey G. Tarasov. Genetic Algorithm Based Parallel Jobs Scheduling. In program "Research and scientific-pedagogical personnel of innovative Russia"(project No. 02-740-11-0626) and Grant of Russian Foundation for Basic Research and Far eastern branch of Russian academy of sciences No. 10-III-B- 01I-009.
  8. Wei Sun , Yuanyuan Zhang , Yanwei Wu, and Yasushi Inoguchi. Practical Task Flow Scheduling for High Throughput Computational Grid. In International Conference on Parallel Processing Workshops (ICPPW'06) 0-7695-2637-3/06, 2006,IEEE computer society.
  9. A. Abraham, R. Buyya, and B. Nath. Nature's heuristics for scheduling jobs on computational grids. In The 8th IEEE International Conference on Advanced Computing and Communications (ADCOM 2000), India, 2000.
  10. Arash Ghorbannia Delavar, Mohsen Nejadkheirallah, Mehdi Motalleb. A New Scheduling Algorithm for Dynamic Task and Fault Tolerant in Heterogeneous Grid Systems Using Genetic Algorithm. In IEEE computer society 2010.
  11. Dr. K. Vivekanandan, D. Ramyachitra A Study on Scheduling in Grid Environment Dr. K. Vivekanandan et al. / International Journal on Computer Science and Engineering (IJCSE).
  12. Javier Carretero, Fatos Xhafa. Use of Genetic algorithm for scheduling jobd in large scale grid applications. In Okio Technologies IR Ekonominis Vystymas Technological and Economic Development of Economy, ISSN 1392-8619 Volume XII, Number 1, 2006.
  13. Wael Abdulal, Omar AI Jadaan, Ahmad Jabas, S. Ramachandram. An Improved Rank-based Genetic algorithm with limited Iterations for grid Scheduling. In IEEE symposium on Industrial Electronics and Applications(ISIEA 2009), Kaula Lumpur, Malaysia, October 4-6, 2009
  14. Weizhe Zhang, Albert M. K. Cheng, Mingzeng Hu. Multisite Co-allocation Algorithms for Computational Grid. In IEEE , 2006
  15. Suchang Guo, Hong-Zhong Huang, Zhonglai Wang, Min Xie. Grid Service Reliability Modeling and Optimal Task Scheduling Considering Fault Recovery. In IEEE Transactions on reliability, VOL. 60, NO. 1, March 2011.
  16. Vijay Subramani, Rajkumar Kettimuthu, Srividya Srinivasan, P. Sadayappan. Distributed Job Scheduling on Computational Grids using Multiple Simultaneous Requests*
  17. Ajith Abraham, Hongbo Liu, Crina Grosan, Fatos Xhafa. Nature Inspired Meta-heuristics for Grid Scheduling: Single and Muti-objecive Optimization Approaches. F. Xhafa, A. Abraham(Eds. ):Meta. For Sched. In Distri. Comp. Envi. ,SCI 146, pp. 247-272, 2008, Springer-verlag berlin Heidelberg 2008.
  18. Wael Abdulal, S. Ramachandram. Reliability-Aware Genetic Scheduling Algorithm in Grid Environment. In IEEE International Conference on Communication Systems and Network Technologies DOI 10. 1109,2011,145.
  19. Carsten Ernemann, Volker Hamscher, Uwe Schwiegelshohn, Ramin Yahyapour. On Advantages of Grid Computing for Parallel Job Scheduling, In Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID. 02).
  20. S. Bhaghavathi Priya, M. Prakash, Dr. K. K. Dhawan. Fault Tolerance-Genetic Algorithm gor Grid Task Scheduling using Check Point. In IEEE the sixth international conference on grid and cooperative computing(GCC),2007.
  21. J. Monroy, J. A. Becerra, F. Bellas, R. J. Duro. Parallel Job Scheduling through Evolutionary Based Cognitive Strategies, In IEEE Congress on Evolutionary Computation Sheraton Vancouver Wall Centre Hotel, Vancouver, BC, Canada, July 16-21, 2006.
  22. Hamed Vahdat-Nej ad, Reza Monsefi, Mahmoud Naghibzadeh. A New Fuzzy Algorithm for Global Job Scheduling in Multiclusters and Grid, In IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA), Ostuni - Italy, 27-29 June 2007
  23. Pavel Fibich and Lud?ek Matyska and Hana Rudov´a. Model of Grid Scheduling Problem, American Association for Artificial Intelligence,2005
  24. Kamaljit Kaur, Amit Chhabra, Gurvinder Singh. Heuristics Based Genetic Algorithm for Scheduling Static Tasks in Homogeneous Parallel System, In International Journal of Computer Science and Security (IJCSS), Volume (4): Issue (2).
Index Terms

Computer Science
Information Sciences

Keywords

Genetic Algorithm (GA) Scheduler Makespan Minimum completion time Fitness Flow Time.

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