Skip to main content

Advertisement

SpringerLink
Log in

Multi-objective workflow grid scheduling using \(\varepsilon \)-fuzzy dominance sort based discrete particle swarm optimization

  • Original Paper
  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

With the rapid development of networking technology, grid computing has emerged as a source for satisfying the increasing demand of the computing power of scientific computing community. Mostly, the user applications in scientific and enterprise domains are constructed in the form of workflows in which precedence constraints between tasks are defined. Scheduling of workflow applications belongs to the class of NP-hard problems, so meta-heuristic approaches are preferred options. In this paper, \(\varepsilon \)-fuzzy dominance sort based discrete particle swarm optimization (\(\varepsilon \)-FDPSO) approach is used to solve the workflow scheduling problem in the grid. The \(\varepsilon \)-FDPSO approach has never been used earlier in grid scheduling. The metric, fuzzy dominance which quantifies the relative fitness of solutions in multi-objective domain is used to generate the Pareto optimal solutions. In addition, the scheme also incorporates a fuzzy based mechanism to determine the best compromised solution. For the workflow applications two scheduling problems are solved. In one of the scheduling problems, we addressed two major conflicting objectives, i.e. makespan (execution time) and cost, under constraints (deadline and budget). While, in other, we optimized makespan, cost and reliability objectives simultaneously in order to incorporate the dynamic characteristics of grid resources. The performance of the approach has been compared with other acknowledged meta-heuristics like non-dominated sort genetic algorithm and multi-objective particle swarm optimization. The simulation analysis substantiates that the solutions obtained with \(\varepsilon \)-FDPSO deliver better convergence and uniform spacing among the solutions keeping the computation overhead limited.

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
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Blythe J, Jain S, Deelaman E, Gil Y, Vahi K, Mandal A, Kennedy K (2005) Task scheduling strategies for workflow-based applications in grids. In: Proceedings of the 5th IEEE international symposium on cluster computing and the grid (CCGrid’05), vol 2, pp 759–767

  2. Braun TD, Siegal HJ, Beck N (2001) A comparision of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J Parallel Distrib Comput 61:810–837

    Google Scholar 

  3. Fujimoto N, Hagihara K (2004) A comparison among grid scheduling algorithms for independent coarse-grained tasks. In: IEEE international symposium on applications and the internet workshops (SAINT 2004 workshops), pp 674–680

  4. Sakellariou R, Zhao H, Tsiakkouri E, Dikaiakos MD (2007) Scheduling workflows with budget constraints. In: Integrated research in GRID computing. Springer, USA, pp 189–202

  5. Wieczorek M, Prodan R, Fahringer T (2005) Scheduling of scientific workflows in the ASKALON grid environment. ACM SIGMOD Rec 34(3):56–62

    Google Scholar 

  6. Dogan A, Ozguner F (2005) Biobjective scheduling algorithms for execution time-reliability trade-off in heterogeneous computing systems. Comput J 48(3):300–314

    Google Scholar 

  7. Wieczorek M, Podlipning S, Prodan R, Fahringer T (2008) Bi-criteria scheduling of scientific workflows for the grid. In: 8th IEEE international symposium on cluster computing and the grid, (CCGRID’08), pp 9–16

  8. Attiya G, Hamam Y (2006) Task allocation for maximizing reliability of distributed systems: a simulated annealing approach. J Parallel Distrib Comput 66:1259–1266

    Google Scholar 

  9. Falzon G, Li M (2012) Enhancing genetic algorithms for dependent job scheduling in grid computing environments. J Supercomput 62(1):290–314

    Article  Google Scholar 

  10. Subrata R, Zomaya YA, Landfeldt B (2007) Artificial life techniques for load balancing in computational grids. J Comput Syst Sci 73:1176–1190

    Google Scholar 

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

  12. Chen WH, Lin C-S (2000) A hybrid heuristic to solve a task allocation problem. Comput Oper Res 27:287–303

    Google Scholar 

  13. Grosan C, Abraham A, Helvik B (2007) Multiobjective evolutionary algorithms for scheduling jobs on computational grids. In: International conference on applied, computing, pp 459–463

  14. Carretero J, Xhafa F, Abraham A (2007) Genetic algorithm based schedulers for grid computing systems. Int J Innov Comput Inf Control 3(6):1–19

    Google Scholar 

  15. Izakian H, Ladani BT, Zamanifar K, Abraham A (2009) A novel particle swarm optimization approach for grid job scheduling. In: Proceedings of the 3rd international conference on information systems, technology and management. Springer, Heidelberg, pp 100–110

  16. Ye G, Rao R, Li M (2006) A multi objective resources scheduling approach based on genetic algorithms in grid environment. In: 5th international conference on grid and cooperative computing workshops, pp 504–509

  17. Koduru P, Dong Z, Das S, Welch SM, Roe JLE, Charbit JL (2008) A multi objective evolutionary-simplex hybrid approach for the optimization of differential equation models of gene networks. IEEE Trans Evol Comput 12(5):572–90

    Google Scholar 

  18. Koduru P, Dong Z, Das S, Welch (2007) Multi-objective hybrid PSO using \(\varepsilon \) -fuzzy dominance. In: Proceedings of the 9th annual conference on genetic and evolutionary computation (GECCO 2007), pp 853–860

  19. Deb K, Pratap A, Aggarwal S, Meyarivan T (2000) A fast elitist multi-objective genetic algorithm: NSGAII. In: Lecture notes in computer science, vol 1917, pp 849–858

  20. Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279

    Article  Google Scholar 

  21. Talukder AK, Kirley M, Buyya R (2009) Multi-objective differential evolution for scheduling workflow applications on global grids. Concurr Comput Pract Exp 21(13):1742–1756

    Article  Google Scholar 

  22. Abraham A, Liu H, Zhang W, Chang TG (2006) Scheduling jobs on computational grids using fuzzy particle swarm algorithm. In: Lecture notes in computer science, vol 4252, pp 500–507

  23. Garg R, Singh AK (2012) Reference point based multi-objective optimization to workflow grid scheduling. Int J Appl Evol Comput (IJAEC) 3(1):80–99

    Google Scholar 

  24. Thickins G (2003) Utility computing: the next new IT model. In: Darwin magazine, vol 3 (April)

  25. Deb K (2001) Multi-objective optimization using evolutionary algorithms. In: Multi-objective, optimization, pp 13–46

  26. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol 4, pp 1942–1948

  27. Li X (2003) Non dominated sorting particle swarm optimizer for multiobjective optimization. In: Genetic and evolutionary computation GECCO 2003. Springer, Berlin/Heidelberg, pp 37–48

  28. Parsopoulos KE, Vrahatis MN (2002) Recent approaches to global optimization problems through particle swarm optimization. Nat Comput 1(2–3):235–306

    Google Scholar 

  29. Tripathy PK, Bandyopadhyay S, Pal SK (2007) Multi objective particle swarm optimization with time variant inertia and acceleration coefficients. Inf Sci 177:5033–5049

    Google Scholar 

  30. Deb K, Agrawal S, Pratap A, Meyarivan T (2002) A fast and elitist multi objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Google Scholar 

  31. Berge FVD (2002) An analysis of particle swarm optimization. Ph.D dissertation, University of Petoria, Petoria

  32. Buyya R, Murshed M (2002) GridSim: a toolkit for modeling and simulation of grid resource management and scheduling. Concurr Comput Pract Exp 14:1175–1220

    Google Scholar 

  33. Salman A, Ahmad I, Al-Madani S (2002) Particle swarm optimization for task assignment problem. Microprocess Microsyst 26(8):363–371

    Google Scholar 

  34. Haluk T, Hariri S, Wu MY (2002) Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distrib Syst 13:260–274

    Google Scholar 

  35. Deb K, Jain S (2002) Running performance metrics for evolutionary multi-objective optimization. In: Proceedings of simulated evolution and, learning (SEAL-02), pp 13–20

  36. Abido MA (2003) Environmental/economic power dispatch using multi objective evolutionary algorithms. IEEE Trans Power Syst 18(4):1529–37

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Engineering Department, National Institute of Technology, Kurukshetra, Haryana, India

    Ritu Garg & Awadhesh Kumar Singh

Authors
  1. Ritu Garg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Awadhesh Kumar Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ritu Garg.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Garg, R., Singh, A.K. Multi-objective workflow grid scheduling using \(\varepsilon \)-fuzzy dominance sort based discrete particle swarm optimization. J Supercomput 68, 709–732 (2014). https://doi.org/10.1007/s11227-013-1059-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-013-1059-8

Keywords

Search

Navigation