Mutation based simulated annealing algorithm for minimizing Makespan in Grid Computing Systems

ICECT 2011 - 2011 3rd International Conference on Electronics Computer Technology

Volumn 6, Issue , 2011, Pages 90-94

  Abdulal, Wael ^a   Jabas, Ahmad ^a   Ramachandram, S ^a   Al Jadaan, Omar ^b  

^a OSMANIA UNIVERSITY   (India)

^b RAK MEDICAL AND HEALTH SCIENCES UNIVERSITY   (United Arab Emirates)

Author keywords

Genetic Algorithm (GA); Grid Computing systems; Makespan; Minimum Completion Time (MCT); Mutation Based Simulated Annealing Algorithm (MSA); Simulated Annealing (SA)

Indexed keywords

COMPUTATIONAL GRIDS; COMPUTATIONAL TASK; GRID COMPUTING SYSTEMS; GRID ENVIRONMENTS; HIGH PERFORMANCE APPLICATIONS; INDEPENDENT TASKS; MAKESPAN; MINIMIZING MAKESPAN; MINIMUM COMPLETION TIME; MUTATION BASED SIMULATED ANNEALING ALGORITHM (MSA); NOVEL ALGORITHM; QUALITY OF SOLUTION; RANDOM MINIMA; RESOURCE UTILIZATIONS; SIMULATED ANNEALING ALGORITHMS; SIMULATION RESULT; STOCHASTIC APPROACH;

COMPUTER SYSTEMS; CONVERGENCE OF NUMERICAL METHODS; GENETIC ALGORITHMS; GRID COMPUTING; HEURISTIC ALGORITHMS; NETWORK MANAGEMENT; SCHEDULING ALGORITHMS;

SIMULATED ANNEALING;

EID: 79961231498     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICECTECH.2011.5942057     Document Type: Conference Paper

References (16)

1. W. Abdulal, O. A. Jadaan, A. Jabas, and S. Ramachandram, "Rank based genetic scheduler for grid computing systems," in The International Con- ference on Computational Intelligence and Communication Networks (CICN2010). Bhopal,India: IEEE, 2010.
2. C. K. I. Foster, The Grid: Blueprint for a Future Computing Infrastructure. Morgan Kaufmann Publishers, 1998.
3. R. B. A. Abraham and B. Nath, "Nature's heuristics for scheduling jobs on computational grids," in Proceedings of 8th IEEE International Conference on Advanced Computing and Communications (ADCOM2000). IEEE, 2000, pp. 45-52.
4. B. D. B. D. F. Xhafa, E. Alba and A. Abraham, "Efficient batch job scheduling in grids using cellular memetic algorithms," Studies in Computational Intelligence, pp. 273-299, 2008.
5. Y. Gao, H. Rong, and J. Z. Huang, "Adaptive grid job scheduling with genetic algorithms," Future Generation Computer Systems, vol. 21, no. 1, pp. 151-161, January 2005.
6. V. D. Martino and M. Mililotti, "Scheduling in a grid computing environment using genetic algorithm," in the 16th International Parallel and Distributed Processing Symposium, April 2002, p. 297.
7. W. A. Alla Alhaffa, "Simulated Annealing algorithm for solving the banking ATMs location problem using convolution," in The International Conference on Machine Learning and Computing (ICMLC 2011). Singapore: IEEE, 2011.
8. S. Fidanova, "Simulated annealing for grid scheduling problem," John Vincent Atanasoff, Modern Computing, International Symposium on, vol. 0, pp. 41-45, 2006.
9. C. wook Han and J. il Park, "SA-selection-based genetic algorithm for the design of fuzzy controller," International Journal of Control, Automation, and Systems, vol. 3, pp. 236-243, 2005.
10. A. A. P. Kazem, A. M. Rahmani, and H. H. Aghdam, "A modified simulated annealing algorithm for static task scheduling in grid comput- ing," in Proceedings of the 2008 International Conference on Computer Science and Information Technology, 2008, pp. 623-627.
11. W. Abdulal, O. A. Jadaan, A. Jabas, and S. Ramachandram, "Genetic algorithm for grid scheduling using best rank power," in Nature & Biologically Inspired Computing, NaBIC 2009. IEEE, 2009, pp. 181-186.
12. A. J. Wael Abdulal, Omar Al Jadaan and S. Ramachandram, "An improved rank-based genetic algorithm with limited iterations for grid scheduling," in 2009 IEEE Symposium on Industrial Electronics and Applications (ISIEA2009), Malaysia, Kuala Lumpur, October 2009, pp.215-220.
13. "Rank-based genetic algorithm with limited iterations for grid scheduling," in First International Conference on Computational Intelligence, Communication Systems, and Networks (CICSyN2009), Indore, India, July 2009.
14. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning. New York, NY:Addison-Wesley, 1989.
15. I. Foster and C. Kesselman, "Globus: A metacomputing infrastructure toolkit," Intl J. Supercomputer Applications, vol. 11, no. 2, p. 115, 1997.
16. O. A. Jadaan, L. Rajamani, and C. Rao, "Parameterless penalty function for solving constrained evolutionary optimization," in Hybrid Intelligent Models and Applications, 2009. HIMA '09. IEEE, 2009, pp. 56-63.