A Simulated Annealing Method to Cover Dynamic Load Balancing in Grid Environment

1. Abraham, A., Buyya, R., Nath, B.: Nature’s Heuristics for Scheduling Jobs in Computational Grids. In: Sinha, P.S., Gupta, R. (eds.) Proc. 8th IEEE International Conference on Advanced Computing and Communications (ADCOM2000), pp. 45–52. Tata McGraw-Hill Publishing Co. Ltd, New Delhi (2000)

   Google Scholar 

2. Abraham, A., Liu, H., Zhang, W., Chang, T.G.: Scheduling Jobs on Computational Grids Using Fuzzy Particle Swarm Algorithm. In: Proc. of 10th International Conference on Knowledge-Based & Intelligent Information & Engineering Systems, England, pp. 500–507 (2006)

   Google Scholar 

3. Berman, F.: High-performance schedulers. In: Foster, I., Kesselman, C. (eds.) The Grid: Blueprint for a New Computing Infrastructure, pp. 279–309. Morgan Kaufmann, San Francisco (1999)

   Google Scholar 

4. Braun, R., Siegel, H., Beck, N., Boloni, L., Maheswaran, M., Reuther, A., Robertson, J., Theys, M., Yao, B., Hensgen, D., Freund, R.: 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), 810–837 (2001)

   Article  Google Scholar 

5. Buyya, R., Abramson, D., Giddy, J., Stockinger, H.: Economic Models for Resource Management and Scheduling in Grid Computing. Journal of Concurrency and Computation: Practice and Experience 14(13-15), 1507–1542 (2002)

   Article  MATH  Google Scholar 

6. Cao, J., Spooner, D.P., Jarvis, S.A., Nudd, G.R.: Grid load balancing using intelligent agents. Future Generation Computer Systems 21(1), 135–149 (2005)

   Article  Google Scholar 

7. Fangpeng, D., Selim, G.A.: Scheduling Algorithms for Grid Computing: State of the Art and Open Problems Technical Report No. 2006-504, Queen’s University, Canada, 55 pages (2006), http://www.cs.queensu.ca/TechReports/Reports/2006-504.pdf

8. Fernandez-Baca, D.: Allocating Modules to Processors in a Distributed System. IEEE Transactions on Software Engineering 15(11), 1427–1436 (1989)

   Article  Google Scholar 

9. Fidanova, S.: Simulated Annealing for Grid Scheduling Problem. In: Proc. IEEE John Vincent Atanasoff International Symposium on Modern Computing (JVA 2006), 10.1109/JVA.2006.44, pp. 41–44 (2006)

   Google Scholar 

10. Fidanova, S., Durchova, M.: Ant Algorithm for Grid Scheduling Problem. In: Lirkov, I., Margenov, S., Waśniewski, J. (eds.) LSSC 2005. LNCS, vol. 3743, pp. 405–412. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

11. Foster, I., Kesselman, C., Tuecke, S.: The Anatomy of the Grid: Enabling Scalable Virtual Organizations. International Journal of Supercomputer applications and High Performance Computing 15(3), 200–222 (2001)

    Article  Google Scholar 

12. Grosan, C., Abraham, A., Helvik, B.: Multiobjective Evolutionary Algorithms for Scheduling Jobs on Computational Grids. In: Guimares, N., Isaias, P. (eds.) IADIS International Conference, Applied Computing 2007, Salamanca, Spain, pp. 459–463 (2007) ISBN: 978-972-8924-30-0

    Google Scholar 

13. Herrero, P., Bosque, J.L., Pérez, M.S.: An Agents-Based Cooperative Awareness Model to Cover Load Balancing Delivery in Grid Environments. In: Meersman, R., Tari, Z., Herrero, P. (eds.) OTM-WS 2007, Part I. LNCS, vol. 4805, pp. 64–74. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

14. Herrero, P., Bosque, J.L., Pérez, M.S.: Managing Dynamic Virtual Organizations to get Effective Cooperation in Collaborative Grid Environments. In: Meersman, R., Tari, Z. (eds.) OTM 2007, Part II. LNCS, vol. 4804, pp. 1435–1452. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

15. Kirkpatrick, S.: Optimization by Simulated Annealing: Quantitative Studies. Journal of Statistical Physics 34(5-6), 975–986 (1984)

    Article  MathSciNet  Google Scholar 

16. Kumar, K.P., Agarwal, A., Krishnan, R.: Fuzzy based resource management framework for high throughput computing. In: Proc. of the 2004 IEEE International Symposium on Cluster Computing and the Grid, pp. 555–562 (2004)

    Google Scholar 

17. Lorpunmanee, S., Sap, M.N., Abdullah, A.H., Chompoo-inwai, C.: An Ant Colony Optimization for Dynamic Job Scheduling in Grid Environment. International Journal of Computer and Information Science and Engineering 1(4), 207–214 (2007)

    Google Scholar 

18. McMullan, P., McCollum, B.: Dynamic Job Scheduling on the Grid Environment Using the Great Deluge Algorithm. In: Malyshkin, V.E. (ed.) PaCT 2007. LNCS, vol. 4671, pp. 283–292. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

19. Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H., Teller, E.: Equations of state calculations by fast computing machines. Journal of Chemical Physics 21(6), 1087–1091 (1953)

    Article  Google Scholar 

20. Ritchie, G., Levine, J.: A fast, effective local search for scheduling independent jobs in heterogeneous computing environments, Technical report, Centre for Intelligent Systems and their Applications, School of Informatics, University of Edinburgh (2003)

    Google Scholar 

21. Yarkhan, A., Dongarra, J.: Experiments with Scheduling Using Simulated Annealing in a Grid Environment. In: Parashar, M. (ed.) GRID 2002. LNCS, vol. 2536, pp. 232–242. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

22. Ye, G., Rao, R., Li, M.: A Multiobjective Resources Scheduling Approach Based on Genetic Algorithms in Grid Environment. In: Fifth International Conference on Grid and Cooperative Computing Workshops, pp. 504–509 (2006)

    Google Scholar 

23. Young, L., McGough, S., Newhouse, S., Darlington, J.: Scheduling Architecture and Algorithms within the ICENI Grid Middleware. In: Proc. of UK e-Science All Hands Meeting, Nottingham, pp. 5–12 (2003)

    Google Scholar 

24. Zomaya, A.Y., The, Y.H.: Observations on using genetic algorithms for dynamic load-balancing. IEEE Transactions On Parallel and Distributed Systems 12(9), 899–911 (2001)

    Article  Google Scholar 

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