Skip to main content
SpringerLink
Log in

An efficient meta-heuristic algorithm for grid computing

  • Published:
Journal of Combinatorial Optimization Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • 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):1141–1150

    Article  Google Scholar 

  • Balachandar S, Kannan K (2007) Randomized gravitational emulation search algorithm for symmetric traveling salesman problem. Appl Math Comput 192(2):413–421

    Article  MathSciNet  MATH  Google Scholar 

  • 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 1240–1245

  • Benedict SH, Vasudevan V (2008) Improving scheduling of scientific workflows using tabu search for computational grids. Inf Technol J 7(1):91–97

    Article  Google Scholar 

  • 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 242–251

  • 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 1–11

  • 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 1–9

  • Eberhat R, Kennedy J (1995) A new optimizer using particle swarm theory. In: Sixth international symposium on micro machine and human science, Piscataway, pp 39–43

  • 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):1–4

    Article  Google Scholar 

  • Gao Y, Rong HQ, Huang JZ (2005) Adaptive grid job scheduling with genetic algorithms. Future Gener Comput Syst 21:151–161

    Article  Google Scholar 

  • Garg SK, Buyya R, Siegel HJ (2010) Time and cost trade-off management for scheduling parallel applications on utility Grids. Future Gener Comput Syst 26:1344–1355

    Article  Google Scholar 

  • Izakian H, Tork Ladani B, Zamanifar K, Abraham A (2009) A novel particle swarm optimization approach for grid job scheduling. Commun Comput Inf Sci 31:100–109

    Article  Google Scholar 

  • Joshua Samuel Raj R, Vasudevan V (2011) Beyond simulated annealing in grid scheduling. Int J Comput Sci Eng 3(3):1312–1318

    Google Scholar 

  • Liu H, Abraham A, Hassanien A (2010) Scheduling jobs on computational grids using a fuzzy particle swarm optimization algorithm. Future Gener Comput Syst 26:1336–1343

    Article  Google Scholar 

  • Maheswaran M (1999) Dynamic mapping of a class of independent tasks onto heterogeneous computing systems. J Parallel Distributed Comput 59(2):107–131

    Article  Google Scholar 

  • Mathiyalagan P, Dhepthie UR, Sivanandam SN (2010) Grid scheduling using enhanced PSO algorithm. Int J Comput Sci Eng 2(2):140–145

    Google Scholar 

  • Orosz ZE, Jacobson SH (2002) Analysis of static simulated annealing algorithm. J Optim Theory Appl 115:165–182

    Article  MathSciNet  MATH  Google Scholar 

  • 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 384–389

  • 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 55:924–928

    Google Scholar 

  • 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):171–181

    Google Scholar 

  • 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):59–64

    Google Scholar 

  • 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):157–164

    Google Scholar 

  • Shiau Der-Fang (2011) A hybrid particle swarm optimization for a university course scheduling problem with flexible preferences. Expert Syst Appl 38:235–248

    Article  Google Scholar 

  • 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):339–357

    Article  Google Scholar 

  • Shi Y, Eberhat R (1998) Parameter selection in particle swarm optimization. In: Proceedings of the 7th annuals conference on evolutionary programming. Springer, Berlin, pp 591–600

  • 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 1945–1950

  • 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 175–180

  • 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):44–52

    Article  Google Scholar 

  • Sivanandam SN, Visalakshi P (2007) Multiprocessor scheduling using hybrid particle swarm optimization with dynamically varying inertia. Int J Comput Sci Appl 4(3):95–106

    Google Scholar 

  • 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 729

  • 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 38:824–836

    Article  MathSciNet  MATH  Google Scholar 

  • Voudouris CH, Tsang E (1995) Guided local search. Eur J Oper Res 16(3):46–50

    Google Scholar 

  • Webster B (2004) Solving combinatorial optimization problems using a new algorithm based on gravitational attraction. PhD thesis, Florida Institute of Technology, Melbourne

  • 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 5552–5557

  • 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 285–292

  • 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 352–355

  • 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):19–32

    Google Scholar 

  • 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):37–43

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Graduate School, Dezful Islamic Azad University, Dezful, Iran

    Zahra Pooranian

  2. Department of Information Engineering, Electronics and Telecommunications (DIET), Sapienza University of Rome, Via Eudossiana 18, 00184 , Rome, Italy

    Mohammad Shojafar

  3. School of Information Technology, Deakin University, Waurn Ponds, VIC, Australia

    Jemal H. Abawajy

  4. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, Auburn, WA, 98071-2259, USA

    Ajith Abraham

Authors
  1. Zahra Pooranian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Shojafar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jemal H. Abawajy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ajith Abraham
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Shojafar.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Pooranian, Z., Shojafar, M., Abawajy, J.H. et al. An efficient meta-heuristic algorithm for grid computing. J Comb Optim 30, 413–434 (2015). https://doi.org/10.1007/s10878-013-9644-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10878-013-9644-6

Keywords

Search

Navigation