Advertisement

Journal of Combinatorial Optimization

, Volume 30, Issue 3, pp 413–434 | Cite as

An efficient meta-heuristic algorithm for grid computing

  • Zahra Pooranian
  • Mohammad Shojafar
  • Jemal H. Abawajy
  • Ajith Abraham
Article

Abstract

A grid computing system consists of a group of programs and resources that are spread across machines in the grid. A grid system has a dynamic environment and decentralized distributed resources, so it is important to provide efficient scheduling for applications. Task scheduling is an NP-hard problem and deterministic algorithms are inadequate and heuristic algorithms such as particle swarm optimization (PSO) are needed to solve the problem. PSO is a simple parallel algorithm that can be applied in different ways to resolve optimization problems. PSO searches the problem space globally and needs to be combined with other methods to search locally as well. In this paper, we propose a hybrid-scheduling algorithm to solve the independent task-scheduling problem in grid computing. We have combined PSO with the gravitational emulation local search (GELS) algorithm to form a new method, PSO–GELS. Our experimental results demonstrate the effectiveness of PSO–GELS compared to other algorithms.

Keywords

Grid computing PSO algorithm GELS Scheduling  Independent tasks 

References

  1. Abdollahi Azgomi M, Eetezari-maleki R (2010) Task scheduling modeling and reliability evaluation of grid services using colored Petri nets. Future Gener Comput Syst 26(8):1141–1150CrossRefGoogle Scholar
  2. Balachandar S, Kannan K (2007) Randomized gravitational emulation search algorithm for symmetric traveling salesman problem. Appl Math Comput 192(2):413–421MathSciNetCrossRefzbMATHGoogle Scholar
  3. Barzegar B, Rahmani AM, Zamanifar K, Divsalar A (2009) Gravitational emulation local search algorithm for advanced reservation and scheduling in grid computing systems. In: Fourth international conference on computer sciences and convergence information technology ICCIT ’09, Seoul, pp 1240–1245Google Scholar
  4. Benedict SH, Vasudevan V (2008) Improving scheduling of scientific workflows using tabu search for computational grids. Inf Technol J 7(1):91–97CrossRefGoogle Scholar
  5. Chen R, Shiau D, Andlo SH (2009) Combined discrete particle swarm optimization and simulated annealing for grid computing scheduling problem. In: Lecture notes in computer science, vol, 57. Springer, Berlin, pp 242–251Google Scholar
  6. Cruz JB Jr, Chen G, Li D, Wang X (2003) Particle swarm optimization for resource allocation in UAV cooperative control. In: AIAA guidance navigation and control conference and exhibit, Reno, pp 1–11Google Scholar
  7. Cruz-Chavez M, Rodríguez-Leon A, Avila-Melgar E, Juarez-Perez F, Cruz-Rosales M, Rivera-Lopez R (2010) Genetic-annealing algorithm in grid environment for scheduling problems. In: Security-enriched urban computing and smart grid communications in computer and information science, vol 78. springer, New York, pp 1–9Google Scholar
  8. Eberhat R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Sixth international symposium on micro machine and human science, Piscataway, pp 39–43Google Scholar
  9. Foster I, Kesselman C, Nick J, Tuecke S (2002) The physiology of the grid: an open grid services architecture for distributed systems integration. Computer 35(6):1–4CrossRefGoogle Scholar
  10. Gao Y, Rong HQ, Huang JZ (2005) Adaptive grid job scheduling with genetic algorithms. Future Gener Comput Syst 21:151–161CrossRefGoogle Scholar
  11. Garg SK, Buyya R, Siegel HJ (2010) Time and cost trade-off management for scheduling parallel applications on utility Grids. Future Gener Comput Syst 26:1344–1355CrossRefGoogle Scholar
  12. Izakian H, Tork Ladani B, Zamanifar K, Abraham A (2009) A novel particle swarm optimization approach for grid job scheduling. Commun Comput Inf Sci 31:100–109CrossRefGoogle Scholar
  13. Joshua Samuel Raj R, Vasudevan V (2011) Beyond simulated annealing in grid scheduling. Int J Comput Sci Eng 3(3):1312–1318Google Scholar
  14. Liu H, Abraham A, Hassanien A (2010) Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm. Future Gener Comput Syst 26:1336–1343CrossRefGoogle Scholar
  15. Maheswaran M (1999) Dynamic mapping of a class of independent tasks onto heterogeneous computing systems. J Parallel Distributed Comput 59(2):107–131CrossRefGoogle Scholar
  16. Mathiyalagan P, Dhepthie UR, Sivanandam SN (2010) Grid scheduling using enhanced PSO algorithm. Int J Comput Sci Eng 2(2):140–145Google Scholar
  17. Orosz ZE, Jacobson SH (2002) Analysis of static simulated annealing algorithm. J Optim Theory Appl 115:165–182MathSciNetCrossRefzbMATHGoogle Scholar
  18. Padmavathi S, Mercy shalinie S (2010) Dag scheduling on cluster of workstations using hybrid particle swarm optimization. In: First international conference on emerging trends in engineering and technology ICETET ’08, vol 10, Mawson Lakes, no 6, pp 384–389Google Scholar
  19. Pooranian Z, Harounabadi A, Shojafar M, Hedayat N (2011) New hybrid algorithm for task scheduling in grid computing to decrease missed task. World Acad Sci Eng Technol 55:924–928Google Scholar
  20. Pooranian Z, Shojafar M, Javadi B (2012) Independent task scheduling in grid computing based on queen bee algorithm. IAES Int J Artif Intell 1(4):171–181Google Scholar
  21. Pooranian Z, Shojafar M, Abawajy JH, Singhal M (2013a) GLOA: a new job scheduling algorithm for grid computing. Int J Artif Intell Interact Multimed 2(1):59–64Google Scholar
  22. Pooranian Z, Shojafar M, Tavoli R, Singhal M, Abraham A (2013b) A hybrid meta-heuristic algorithm for job scheduling on computational grids. Inform J 37(2):157–164Google Scholar
  23. Shiau Der-Fang (2011) A hybrid particle swarm optimization for a university course scheduling problem with flexible preferences. Expert Syst Appl 38:235–248CrossRefGoogle Scholar
  24. Shiau D, Huang Y (2012) A hybrid two-phase encoding particle swarm optimization for total weighted completion time minimization in proportionate flexible flow shop scheduling. Int J Adv Manuf Technol 58(1):339–357CrossRefGoogle Scholar
  25. Shi Y, Eberhat R (1998) Parameter selection in particle swarm optimization. In: Proceedings of the 7th annuals conference on evolutionary programming. Springer, Berlin, pp 591–600Google Scholar
  26. Shi Y, Eberhat R (1999) Empirical study of particle swarm optimization. In: Proceedings of the IEEE congress on evolutionary computation, vol 3. IEEE Press, Los Alamitos, pp 1945–1950Google Scholar
  27. Shojafar M, Barzegar S, Meybodi MR (2010) A new method on resource scheduling in grid systems based on hierarchical stochastic Petri net. In: Proceedings of third international conference on computer and electrical engineering (ICCEE 2010), Chengdu, pp 175–180Google Scholar
  28. Shojafar M, Pooranian Z, Abawajy JH, Meybodi MR (2013) An efficient scheduling method for grid systems based on a hierarchical stochastic Petri net. J Comput Sci Eng 7(1):44–52CrossRefGoogle Scholar
  29. Sivanandam SN, Visalakshi P (2007) Multiprocessor scheduling using hybrid particle swarm optimization with dynamically varying inertia. Int J Comput Sci Appl 4(3):95–106Google Scholar
  30. Sullivan WT, Werthimer D, Bowyer S, Cobb J, Gedye D, Anderson D (1997) A new major SETI project based on Project Serendip data and 100000 personal computers. In: Proceedings of the fifth international conference on bioastronomy, Bologna, no 61, p 729Google Scholar
  31. Tao Q, Chang H, Yi Y, Gu CH, Li W (2011) A rotary chaotic PSO algorithm for trustworthy scheduling of a grid workflow. Comput Oper Res 38:824–836MathSciNetCrossRefzbMATHGoogle Scholar
  32. Voudouris CH, Tsang E (1995) Guided local search. Eur J Oper Res 16(3):46–50Google Scholar
  33. Webster B (2004) Solving combinatorial optimization problems using a new algorithm based on gravitational attraction. PhD thesis, Florida Institute of Technology, MelbourneGoogle Scholar
  34. Weijun X, Zhiming W, Wei ZH, Genke Y (2004) A new hybrid optimization algorithm for the job-shop scheduling problem. In: Proceeding of the 2004 American control conference, vol 6, Boston, pp 5552–5557Google Scholar
  35. Xhafa F, Gonzalez J, Dahal K, Abraham A (2009) A GA(TS) hybrid algorithm for scheduling in computational grids. In: Hybrid artificial intelligence systems. Lecture notes in computer science, vol 5572. Springer, Berlin, pp 285–292Google Scholar
  36. Yan-ping B, Wei ZH, Jin-shou Y (2008) An improved PSO algorithm and its application to grid scheduling problem. International symposium on computer science and computational technology ISCSCT ’08, Shanghai, pp 352–355Google Scholar
  37. Yusof M, Badak K, Stapa M (2010) Achieving of tabu search algorithm for scheduling technique in grid computing using GridSim simulation tool: multiple jobs on limited resource. Int J Grid Distributed Comput 3(4):19–32Google Scholar
  38. Zhang L, Chen Y, Sun R, Jing SH, Yang B (2008) A task scheduling algorithm based on PSO for grid computing. Int J Comput Intell Res 4(1):37–43CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Zahra Pooranian
    • 1
  • Mohammad Shojafar
    • 2
  • Jemal H. Abawajy
    • 3
  • Ajith Abraham
    • 4
  1. 1.Graduate SchoolDezful Islamic Azad UniversityDezfulIran
  2. 2.Department of Information Engineering, Electronics and Telecommunications (DIET)Sapienza University of RomeRomeItaly
  3. 3.School of Information TechnologyDeakin UniversityWaurn PondsAustralia
  4. 4.Machine Intelligence Research Labs (MIR Labs)Scientific Network for Innovation and Research ExcellenceAuburnUSA

Personalised recommendations