Tuning struggle strategy in genetic algorithms for scheduling in computational grids

Neural Network World

Volumn 18, Issue 3, 2008, Pages 209-225

  Xhafa, Fatos ^a   Duran, Bernat ^a   Abraham, Ajith ^b   Daha, Keshav ^c  

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

^b NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Norway)

^c UNIVERSITY OF BRADFORD   (United Kingdom)

Author keywords

Genetic algorithms; Grid computing; Scheduling; Similarity measure; Struggle strategy; Tuning

Indexed keywords

ALGORITHMS; ARSENIC COMPOUNDS; COMPUTER SYSTEMS; DATA DESCRIPTION; DIESEL ENGINES; EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHMS; SCHEDULING; SOLUTIONS; TUNING;

COMPUTATIONAL GRIDS; GRID COMPUTING; SIMILARITY MEASURE; STRUGGLE STRATEGY;

SCHEDULING ALGORITHMS;

EID: 51449096450     PISSN: 12100552     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (41)

1. Abraham A., Buyya R., Nath B.: Nature's heuristics for scheduling jobs on Computational Grids. In: The 8th IEEE International Conference on Advanced Computing and Communications, India, 2000.
2. Bartschi Wall M.: A Genetic Algorithm for Resource-Constrained Scheduling PhD Thesis, Massachusetts Institute of Technology, June 1996.
3. Beynon M. D., Sussman A., Catalyurek U., Kure T., Saltz J.: Optimization for data intensive grid applications. In: Third Annual International Workshop on Active Middleware Services, California, 2001, pp. 97-106.
4. Brameier M., Banzhaf W.: Explicit control of diversity and effective variation distance in linear genetic programming. In: Genetic Programming, 5th European Conference, EuroGP 2002, Kinsale, Ireland, April 3-5, 2002, 2278 of Lecture Notes in Computer Science, Springer, 2002, pp. 37-49.
5. Braun T., Siegel H., Beck N., Boloni L., Maheswaran M., Reuther A., Robertson J., Theys M., Yao B.: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. Journal of Parallel and Distributed Computing, 61, 6, 2001, pp. 810-837.
6. Burke E. K., Gustafson S. M., Kendall G., Krasnogor N.: Advanced population diversity measures in genetic programming. In: Parallel Problem Solving from Nature - PPSN VII, 7th International Conference, Granada, Spain, 2439 of Lecture Notes in Computer Science, Springer, 2002, pp. 341-350.
7. Carretero J., Xhafa F.: Using Genetic Algorithms for Scheduling Jobs in Large Scale Grid Applications. Journal of Technological and Economic Development - A Research Journal of Vilnius Gediminas Technical University, ISSN 1392-8619, 12, 1, 2006, pp. 11-17.
8. Casanova H., Dongarra J.: Netsolve: Network enabled solvers. IEEE Computational Science and Engineering, 5, 3, 1998, pp. 57-67.
9. Di Martino V., Mililotti M.: Sub optimal scheduling in a grid using genetic algorithms. Parallel Computing, 30, 2004, pp. 553-565.
10. Ekárt A., Németh S. Z.: Maintaining the diversity of genetic programs. In: Genetic Programming, 5th European Conference, EuroGP 2002, Kinsale, Ireland, 2278 of Lecture Notes in Computer Science, Springer, 2002, pp. 162-171.
11. Freund R., Gherrity M., Ambrosius S., Campbell M., Halderman M., Hensgen D., Keith E., Kidd T., Kussow M., Lima J., Mirabile F., Moore L., Rust B., Siegel H.: Scheduling resources in multi-user, heterogeneous, computing environments with SmartNet. In: Seventh Heterogeneous Computing Workshop, 1998, pp. 184-199.
12. Gao Y., Rong H., Huang J. Z.: Adaptive Grid job scheduling with genetic algorithms. Future Gener. Comput. Syst., 21, 1 Jan. 2005, pp. 151-161.
13. Goux J. P., Kulkarni S., Linderoth J., Yoder M.: An enabling framework for master-worker applications on the computational grid. In: 9th IEEE Int. Symposium on High Performance Distributed Computing (HPDC'00), 2000.
14. Grueninger T.: Multimodal optimization using genetic algorithms. Technical report. Department of Mechanical Engineering, MIT, Cambridge, MA, 1997.
15. Hao X., Dai Y., Zhang B., Chen T., Lei Yang: QoS-Driven Grid Resource Selection Based on Novel Neural Networks. GPC 2006, pp. 456-465.
16. Holland J. H.: Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI, 1975.
17. Ishibuchi H., Narukawa K.: Recombination of Similar Parents in EMO Algorithms. In: Evolutionary Multi-Criterion Optimization, 2005, pp. 265-279.
18. Ishibuchi H., Shibata Y.: A Similarity-Based Mating Scheme for Evolutionary Multiobjective Optimization. Lecture Notes in Computer Science, Springer, 2723/2003, Genetic and Evolutionary Computation - GECCO 2003.
19. Linderoth L., Wright S.: Decomposition algorithms for stochastic programming on a Computational Grid. Computational Optimization and Applications, 24, 2003, pp. 207-250.
20. Liu Sh., Mernik M., Bryant B. R.: Entropy-driven exploration and exploitation in evolutionary algorithms. In: Bogdan Filipič and Jurij Šilc, editors, Proceedings of the 2nd International Conference on Bioinspired Optimization Methods and their Applications (BIOMA 2006), 15-24, Ljubljana, Slovenia, Jožef Stefan Institute, October 2006.
21. Moscato P.: On evolution, search, optimization, genetic algorithms and martial arts: Towards memetic algorithms. Technical report No. 826, California Institute of Technology, USA, 1989.
22. Newman H. B., Ellisman M. H., Orcutt J. A.: Data-intensive e-Science frontier research. Communications of ACM, 46, 11, 2003, pp. 68-77.
23. Page J., Naughton J.: Framework for task scheduling in heterogeneous distributed computing using genetic algorithms. AI Review, 24, 2005, pp. 415-429.
24. Paniagua C., Xhafa F., Caballé S., Daradoumis T.: A parallel grid-based implementation for real time processing of event log data in collaborative applications. In: Parallel and Distributed Processing Techniques (PDPT2005), Las Vegas, USA, 2005, pp. 1177-1183.
25. Perez J., Kégl B., Germain-Renaud C.: Reinforcement learning for utility-based Grid scheduling. At NIPS07 (Twenty-First Annual Conference on Neural Information Processing Systems) Workshops, in Vancouver, Canada, 2007.
26. Sato M., Aguirre H. E., Tanaka K.: Effects of δ-Sirnilar Elimination and Controlled Elitism in the NSGA-II Multiobjective Evolutionary Algorithm. In IEEE Congress on Evolutionary Computation, Vancouver, BC, Canada, 2006, pp. 1164-1171.
27. Vengerov D.: Adaptive Utility-Based Scheduling in Resource-Constrained Systems. In AI 2005: Advances in Artificial Intelligence, Springer Verlag, 2005, pp. 477-488.
28. Senin N., Groppetti R., Wallace D. R.: Concurrent assembly planning with genetic algorithms. Robotics and Computer-Integrated Manufacturing, 16, 1, 2000, pp. 65-72.
29. Wildman A., Parks G.: A Comparative Study of Selective Breeding Strategies in a Multiobjective Genetic Algorithm. In: C. M. Fonseca et al. (Eds.): EMO 2003, LNCS 2632, 2003, pp. 418-432.
30. Wright S.: Solving optimization problems on Computational Grids. Optima, 65, 2001.
31. Xhafa F.: A Hyper-heuristic for Adaptive Scheduling in Computational Grids, International Journal on Neural and Mass-Parallel Computing and Information Systems, 17, 6, 2007, pp. 639-656.
32. Xhafa F.: A Hybrid Evolutionary Heuristic for Job Scheduling in Computational Grids. Springer Verlag Series: Studies in Computational Intelligence , 75, 2007, Chapter 10, September 2007, ISBN: 978-3-540-73296-9.
33. Xhafa F., Alba E., Dorronsoro B., Duran B.: Efficient Batch Job Scheduling in Grids using Cellular Memetic Algorithms, Accepted, Journal of Mathematical Modelling and Algorithms, Published Online DOI: http://dx.doi.org/10.1007/s10852-008-9076-y
34. Xhafa F., Barolli L., Durresi A.: An Experimental Study On Genetic Algorithms for Resource Allocation On Grid Systems, Journal of Interconnection Networks, 8, 4, World Sci. Pub, December 2007, pp. 427-443.
35. Xhafa F., Carretero J., Abraham A.: Genetic Algorithm Based Schedulers for Grid Computing Systems. International Journal of Innovative Computing, Information and Control, 3, 5, 2007, pp. 1-19.
36. Xhafa F., Barolli L., Durresi A.: Batch Mode Schedulers for Grid Systems. International Journal of Web and Grid Services, 3, 1, 2007, pp. 19-37.
37. Xhafa F., Carretero J., Barolli L., Durresi A.: Immediate Mode Scheduling in Grid Systems. International Journal of Web and Grid Services, 3, 2, 2007, pp. 219-236.
38. Yan W., Clack Ch. D.: Behavioural GP diversity for dynamic environments: an application in hedge fund investment. In: GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation, New York, USA, 2006. ACM Press, pp. 1817-1824.
39. Yu K. M., Luo Zh. J., Chou Ch. H., Chen Ch. K., Zhou J.: A Fuzzy Neural Network Based Scheduling Algorithm for Job Assignment on Computational Grids. NBiS 2007, Lecture Notes in Computer Science, Springer Verlag, 2007, pp. 533-542.
40. Zhou J., Kun-Ming Yu K-M., Chou Ch-H., Yang L-A., Luo Zh-J.: A Dynamic Resource Broker and Fuzzy Logic Based Scheduling Algorithm in Grid Environment. ICANNGA 1, 2007, pp. 604-613.
41. Zomaya A. Y., Teh Y. H.: Observations on using genetic algorithms for dynamic load-balancing, IEEE Transactions On Parallel and Distributed Systems, 12, 9, 2001, pp. 899-911.