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Submitted
Aug 3, 2020
Published
Mar 25, 2015
USING THE DISCRETE FOREST OPTIMIZATION ALGORITHM FOR THE PROBLEM OF SCHEDULING INDEPENDENT JOBS ON COMPUTATIONAL GRIDS
Corresponding Author(s) : Do Vinh Truc
dvtruc@hcmiu.edu.vn
UED Journal of Social Sciences, Humanities and Education,
Vol. 5 No. 1 (2015): UED JOURNAL OF SOCIAL SCIENCES, HUMANITIES AND EDUCATION
Abstract
The Computational Grid (CG) is a new problem which has appeared recently. The scheduling of independent jobs on CG for the purpose of minimizing makespan is difficult but fascinating. To solve this problem as well as problems in the field of optimization, there has been the latest contribution to the group of well-known evolutionary algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Forest Optimization Algorithm (FOA) [1],... This paper introduces a revised FOA and applies it to the solution of the problem of independent job scheduling on CG with the goal of minimizingmakespan. The results show that FOA is also a good algorithm for solving the above optimization problem.
Keywords
FOA; computational grid; independent job; scheduling; makespan.
Do Vinh Truc. (2015). USING THE DISCRETE FOREST OPTIMIZATION ALGORITHM FOR THE PROBLEM OF SCHEDULING INDEPENDENT JOBS ON COMPUTATIONAL GRIDS. UED Journal of Social Sciences, Humanities and Education, 5(1), 15-20. https://doi.org/10.47393/jshe.v5i1.506
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References
-
[1] M. Ghaemi and M.-R. Feizi-Derakhshi (2014), “Forest Optimization Algorithm”, Expert Systems with Applications, vol. 41, no. 15, pp. 6676–6687, Nov. 2014.
[2] H. Liu, A. Abrahamc, and A. E. Hassanien (2010), “Scheduling jobs on computational grids using a fuzzy particle swarm optimisation algorithm”, Future Generation Computer Systems, vol. 26, pp. 1336–1343.
[3] F. Xhafa, J. Carretero, B. Dorronsoro, and E. Alba (2009), “A tabu search algorithm for scheduling independent jobs in computational grids”, Computing and informatics, vol. 28, no. 2, pp. 237–250.
[4] I. Foster, C. Kesselman, and S. Tuecke (2001), “The anatomy of the grid,” Berman et al.[2], pp. 171–197.
[5] F. Dong and S. G. Akl (2006), “Scheduling algorithms for grid computing: State of the art and open problems”, Technical report.
[6] I. Foster and C. Kesselman (2003), The Grid 2: Blueprint for a New Computing Infrastructure. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
[7] E.-G. Talbi and A. Y. Zomaya, Eds. (2007), “Wiley Series on Bioinformatics: Computational Techniques and Engineering”, in Grid Computing for Bioinformatics and Computational Biology, John Wiley & Sons, Inc, pp. 393–393.
[8] S. B. Nguyen, M. Zhang, and others (2014), “A hybrid discrete particle swarm optimisation method for grid computation scheduling”, in Evolutionary Computation (CEC), 2014 IEEE Congress on, pp. 483–490.
[9] Howard Jay Siegel Tracy D. Braun and N. Beck (2001), “A Comparison of Eleven Static Heuristics for Mapping a Class of Independent Tasks onto Heterogeneous Distributed Computing Systems”, Journal of Parallel and Distributed Computing, vol. 61, pp. 810–837.
[10] A. J. Page and T. J. Naughton (2005), “Framework for Task Scheduling in Heterogeneous Distributed Computing Using Genetic Algorithms”, Artif Intell Rev, vol. 24, no. 3–4, pp. 415–429.
[11] G. Ritchie and J. Levine (2003), “A hybrid ant algorithm for scheduling independent jobs in heterogeneous computing environments”.
[12] J. L. Graham Ritchie, “A fast, effective local search for scheduling independent jobs” in heterogeneous computing environments.
[13] F. Xhafa, E. Alba, and B. Dorronsoro (2007), “Efficient Batch Job Scheduling in Grids using Cellular Memetic Algorithms,” in Parallel and Distributed Processing Symposium, 2007. IPDPS 2007. IEEE International, pp. 1–8.
[14] A. YarKhan and J. J. Dongarra (2002), “Experiments with Scheduling Using Simulated Annealing in a Grid Environment”, in Grid Computing — GRID 2002, M. Parashar, Ed. Springer Berlin Heidelberg, pp. 232–242.
[15] H. Izakian and A. Abraham, Performance Comparison of Six Efficient Pure Heuristics for Scheduling Meta-Tasks on Heterogeneous Distributed Environments.
[16] A. Abraham, R. Buyya, and B. Nath (2000), “Nature’s Heuristics for Scheduling Jobs on Computational Grids”, in ieee international conference on advanced computing and communications, pp. 45–52.
[17] H. Izakian, B. T. Ladani, A. Abraham, and V´aclav Sn´aˇsel (2010), “A Discrete Particle Swarm Optimization Approach for Grid Job Scheduling,” International Journal of Innovative Computing, Information and Control, vol. 6, no. 9.