Skip to main content
SpringerLink
Log in

An efficient grid scheduling strategy for data parallel applications

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

Abstract

Scheduling large-scale application in heterogeneous grid systems is a fundamental NP-complete problem that is critical to obtain good performance and execution cost. To achieve high performance in a grid system it requires effective task partitioning, resource management and load balancing. The heterogeneous and dynamic nature of a grid, as well as the diverse demands of applications running on the grid, makes grid scheduling a major task. Existing schedulers in wide-area heterogeneous systems require a large amount of information about the application and the grid environment to produce reasonable schedules. However, this required information may not be available, may be too expensive to collect, or may increase the runtime overhead of the scheduler such that the scheduler is rendered ineffective. We believe that no one scheduler is appropriate for all grid systems and applications. This is because while data parallel applications in which further data partitioning is possible can be further improved by efficient management of resources, smart selection of resources and load balancing can be possible, in functional/not-dividable-task parallel applications such partitioning is either not possible or difficult or expensive in term of performance. In this paper, we propose a scheduler for data parallel applications (SDPA) which offers an efficient task partitioning and load balancing strategy for data parallel applications in grid environment. The proposed SDPA offers two major features: maintaining job priority even if insufficient number of free resources is available and pre-task assignment to cut the idle time of nodes. The SDPA selects nodes smartly according to the nature of task and the nodes’ resources availability. Simulation results conducted reveal that SDPA achieves performance improvement over reported strategies in the reviewed literature in terms of execution time, throughput and waiting time.

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
Fig. 12

Similar content being viewed by others

References

  1. Yu J, Buyya R (2005) A taxonomy of scientific workflow systems for grid computing. Special issue on scientific workflows, ACM SIGMOD record, 34(3), ACM Press, New York, pp 44–49

  2. Torkestani JA (2011) A new approach to the job scheduling problem in computational grids. J Clust Comput. doi:10.1007/s10586-011-0192-5

  3. Qureshi K, Rehman A, Manuel P (2011) Enhanced GridSim architecture with load balancing. J Supercomput 57(3):265–275

    Article  Google Scholar 

  4. Khan FG, Qureshi K, Nazir B (2010) Performance evaluation of fault tolerance techniques in grid computing system. Int J Comput Electr Eng 36(6):1110–1122

    Google Scholar 

  5. Qureshi K, Majeed B, Kazmi JH, Madani SA (2012) Task partitioning, scheduling and load balancing strategy for mixed nature of tasks. J Supercomput 59(3):1348–1359

    Article  Google Scholar 

  6. Gao Y, Rong H et al (2004) Adaptive grid job scheduling with genetic algorithms, future generation computer systems. Elsevier 21:151–161

    Google Scholar 

  7. Nazir B, Qureshi K, Manuel P (2009) Adaptive checkpointing strategy to tolerate faults in economy based grid. J Supercomput 50(1):1–18

    Article  Google Scholar 

  8. Qureshi K, Rehman A, Manuel P (2011) Enhanced GridSim architecture with load balancing. J Supercomput 57(3):265–275

    Article  Google Scholar 

  9. Foster I, Kesselman C (eds) (2003) The grid: blueprint for a new computing infrastructure, 2nd edn. Morgan Kaufmann

  10. Abraham A, Buyya R, Nath B (2000) Nature’s heuristics for scheduling jobs on computational grids. In: the 8th IEEE international conference on advanced computing and communications, Cochin, pp 45–52

  11. Braun R, Siegel H, Beck N, Boloni L, Maheswaran M, Reuther A, Robertson J, Theys M, Yao B, Hensgen D, Freund R (2001) A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J Parallel Distrib Comput 61(6):810–837

    Article  Google Scholar 

  12. Lee H, Lee D, Ramakrishna RS (2006) An enhanced grid scheduling with job priority and equitable interval job distribution. In: Advances in grid and pervasive computing, Springer, Berlin, pp 53–62

  13. Tsafrir D, Etsion Y, Feitelson DG (2007) Backfilling using system-generated predictions rather than user runtime estimates. IEEE Trans Parallel Distrib Syst 18(6):789–807

    Article  Google Scholar 

  14. Lawson BG, Smirni E (2002) Multiple-queue backfilling scheduling with priorities and reservations for parallel systems, LNCS 2537. Springer, Berlin

    Google Scholar 

  15. Di Martino V, Mililotti M (2004) Sub optimal scheduling in a grid using genetic algorithms. Parallel Comput 30:553–565

    Article  Google Scholar 

  16. Gao Y, Rong H, Zhexue Huang J (2005) Adaptive Grid job scheduling with genetic algorithms. J Future Gener Comput Syst 21:151–161

    Article  Google Scholar 

  17. Carretero J, Xhafa F (2006) Using genetic algorithms for scheduling jobs in large scale grid applications. J Technol Econ Dev 12(1):11–17

    Google Scholar 

  18. Cheng W, Congfeng J, Xiaohu L (2007) Fuzzy logic-based secure and fault tolerant job scheduling in grid. Tsinghua Sci Technol 12(S1):45–50

    MATH  Google Scholar 

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

    Article  Google Scholar 

  20. Korkhov VV, Korkhov JT, Krzhizhanovskaya VV (2009) Dynamic workload balancing of parallel applications with user-level scheduling on the grid. Future Gener Comput Syst Elsevier 25:28–34

    Article  Google Scholar 

  21. Berman FD, Wolski R, Figueira S, Schopf J, Shao G (1996) Application-level scheduling on distributed heterogeneous networks. In: Proceedings of the 1996 ACM/IEEE conference on supercomputing, Pittsburgh, Pennsylvania, ISBN:0-89791-854

  22. Siyambalapitiya R, Sandirigama M (2011) Improvements to first-come-serve multiprocessor scheduling with gang scheduling. IUP J Comput Sci 5(3):11–17

    Google Scholar 

  23. Baruah S, Funk S, Goossens J (2003) Robustness results concerning EDF scheduling upon uniform multiprocessors. IEEE Trans Comput 52(9):1185–1195

    Article  Google Scholar 

  24. Moaddeli HR, Dastghaibyfard Gh, Moosavi MR Flexible advance reservation impact on backfilling scheduling strategies. Grid and cooperative computing, IEEE-2008. GCC\(\backslash \)08, seventh International conference, pp 151–159. ISBN:978-0-7695-3449-7

  25. Techiouba AD, Capannini G, Baraglia R, Puppin D, Pasquali M (2008) Backfilling strategies for scheduling streams of jobs on computational farms. In: CoreGRID workshop on grid programming model, grid and P2P systems architecture, grid systems, tools and environments, Springer

  26. Lifka DA (1995) The ANL/IBM SP scheduling system. In: Job scheduling strategies for parallel processing, volume 949 of LNCS, Springer, pp 295–303

  27. Chiang S-H, Arpaci-Dusseau A, Vernon MK (2002) The impact of more accurate requested runtimes on production job scheduling performance, Springer, LNCS, vol 2537, pp 103–127

  28. Klusáček D, Rudová H, Baraglia R, Pasquali M, Capannini G (2008) Comparison of multi-criteria scheduling techniques. pp 173–184. ISBN: 978-0-387-09457-1

  29. Terashima-Marin H et al (2007) Comparing two models to generate hyper-heuristics for the 2d-regular bin-packing problem. In: Proceedings of the 9th annual conference on genetic and evolutionary computation. ACM, New York, pp 2182–2189. ISBN:978-1-59593-697-4

  30. Klusáek D et al (2008) Alea-grid scheduling simulation environment. In: 7th international conference on parallel processing and applied mathematics, Springer, Berlin

  31. Buyya R, Murshed M (2007) GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for grid computing. Concur Comput Pract Exp 14(13–15):1175–1220 Special issue: grid computing environments

    Google Scholar 

  32. Feitelson et al (2006) Workload sanitation for performance evaluation. In: IEEE international symposium onperformance analysis of systems and software, pp 221–230. ISBN:1-4244-0186-0

Download references

Author information

Authors and Affiliations

  1. COMSATS Institute of Information Technology, Abbottabad, Pakistan

    Kashif Hesham Khan

  2. Information Science Department, Kuwait University, Kuwait, Kuwait

    Kalim Qureshi & Mostafa Abd-El-Barr

Authors
  1. Kashif Hesham Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kalim Qureshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mostafa Abd-El-Barr
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kalim Qureshi.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Khan, K.H., Qureshi, K. & Abd-El-Barr, M. An efficient grid scheduling strategy for data parallel applications. J Supercomput 68, 1487–1502 (2014). https://doi.org/10.1007/s11227-014-1114-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-014-1114-0

Keywords

Search

Navigation