Tuning struggle strategy in genetic algorithms for scheduling in computational grids

Proceedings - 7th Computer Information Systems and Industrial Management Applications, CISIM 2008

Volumn , Issue , 2008, Pages 275-280

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

^a UNIVERSITAT POLITÈCNICA DE CATALUNYA   (Spain)

^b NORWEGIAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Norway)

^c UNIVERSITY OF BRADFORD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPLICATIONS; ARSENIC COMPOUNDS; COMPUTER NETWORKS; COMPUTER SYSTEMS; DATA DESCRIPTION; DIESEL ENGINES; EVOLUTIONARY ALGORITHMS; INDUSTRIAL ENGINEERING; INDUSTRIAL MANAGEMENT; INFORMATION MANAGEMENT; INFORMATION SCIENCE; INFORMATION SYSTEMS; PROJECT MANAGEMENT; SCHEDULING; SOLUTIONS; TUNING;

COMPUTATIONAL GRIDS; MANAGEMENT APPLICATIONS;

SCHEDULING ALGORITHMS;

EID: 51749110897     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CISIM.2008.54     Document Type: Conference Paper

References (16)

1. Casanova, H. and Dongarra, J.: Netsolve: Network enabled solvers. IEEE Computational Science and Engineering, 5(3):57-67, 1998.
2. Goux, J.P., Kulkarni, S., Linderoth, J. and Yoder, M.: An enabling framework for master-worker applications on the computational grid. In 9th IEEE International Sympo-sium on High Performance Distributed Computing (HPDC'00). IEEE Computer Society, 2000.
3. Wright, S.J.: Solving optimization problems on computational grids. Optima, 65, 2001.
4. Newman, H.B., Ellisman, M.H. and Orcutt, J.A.: Data-intensive e-science frontier research, Communications of ACM, 46(11):68-77, 2003.
5. Paniagua, C., Xhafa, F., Caballé, S. and Daradoumis, Th.: 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.
6. Beynon, M.D., Sussman, A., Çatalyürek, Ü, Kure, T. and Saltz, J.: Optimization for data intensive grid applications. In Third Annual International Workshop on Active Middleware Services, pp. 97-106, 2001.
7. Abraham, A., Buyya, R. and Nath, B. Nature's heuristics for scheduling jobs on computational grids, The 8th IEEE International Conference on Advanced Computing and Communications (ADCOM 2000) India, 45-52, 2000.
8. Braun, H.J., Siegel, T.D., Beck, N., Bölöni, L.L., Maheswaran, M., Reuther, A.I., Robertson, J.P., Theys, M.D. and Yao, B.: A comparison of eleven static heuristics for mapping a class of independent jobs onto heterogeneous distributed computing systems. Journal of Parallel and Distributed Computing, 61(6):810-837, 2001.
9. Xhafa, F. A Hyper-heuristic for Adaptive Scheduling in Computational Grids, International Journal on Neural and Mass-Parallel Computing and Information Systems, 17(6), 639-656, 2007.
10. Xhafa, F., Alba, E., Dorronsoro, B. and Duran, B. Efficient Batch Job Scheduling in Grids using Cellular Memetic Algorithms, Journal of Mathematical Modelling and Algorithms, Published Online DOI: http://dx.doi.org/10.1007/ s10852-008-9076-y
11. Xhafa, F., Barolli, L. and Durresi, A. An Experimental Study On Genetic Algorithms for Resource Allocation On Grid Systems, Journal of Interconnection Networks, Volume: 8, Issue: 4 (December 2007), 427 - 443, World Sci. Pub.
12. Zomaya, A.Y. and Teh, Y.H. Observations on using genetic algorithms for dynamic load-balancing, IEEE Transactions On Parallel and Distributed Systems, 12(9):899-911,2001.
13. J.H.~Holland. Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI, 1975.
14. Gruemnger, T.: Multimodal optimization using genetic algorithms. Technical report. Department of Mechanical Engineering, MIT, Cambridge, MA, 1997.
15. Fogel, D.B.: Evolutionary computation: toward a new philosophy of machine intelligence. IEEE Press Piscataway, NJ, USA, 1995.
16. Ashlock, D.: Evolutionary Computation for Modeling and Optimization. Springer Verlag, 2005.