Skip to main content
Log in

Scalable, low complexity, and fast greedy scheduling heuristics for highly heterogeneous distributed computing systems

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

Abstract

For heterogeneous distributed computing systems, important design issues are scalability and system optimization. Given such systems, it is crucial to develop low computational complexity algorithms to schedule tasks in a manner that exploits the heterogeneity of the resources and applications. In this paper, we report and evaluate three scalable, and fast scheduling heuristics for highly heterogeneous distributed computing systems. We conduct a comprehensive performance evaluation study using simulation. The benchmarking outlines the performance of the schedulers, representing scalability, makespan, flowtime, computational complexity, and memory utilization. The set of experimental results shows that our heuristics perform as good as the traditional approaches, for makespan and flowtime, while featuring lower complexity, lower running time, and lower used memory. The experimental results also detail the various scenarios under which certain algorithms excel and fail.

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

Access this article

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

Instant access to the full article PDF.

Framework 1
Framework 2
Framework 3
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Tanenbaum AS, Van Steen M (2001) Distributed systems: principles and paradigms, 1st edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  2. Neuman BC (1994) Scale in distributed systems. In: Readings in distributed computing systems. IEEE CS Press, Los Alamitos, CA, USA, pp 463–489

    Google Scholar 

  3. Braun TD, Siegel HJ, Beck N, Bölöni LL, Maheswaran M, Reuther AI, Robertson JP, Theys MD, Yao B, Hensgen D, Freund RF (2001) A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J Parallel Distrib Comput 61:810–837

    Article  Google Scholar 

  4. Ibarra OH, Kim CE (1977) Heuristic algorithms for scheduling independent tasks on nonidentical processors. J ACM 24:280–289

    Article  MATH  MathSciNet  Google Scholar 

  5. Luo P, Lü K, Shi Z (2007) A revisit of fast greedy heuristics for mapping a class of independent tasks onto heterogeneous computing systems. J Parallel Distrib Comput 67:695–714

    Article  MATH  Google Scholar 

  6. Munir EU, Li J, Shi S, Zou Z, Rasool Q (2008) A performance study of task scheduling heuristics in hc environment. In: Thi HAL, Bouvry P, Dinh TP (eds) Modelling, computation and optimization in information systems and management sciences. Communications in computer and information science, vol 14. Springer, Berlin, pp 214–223

    Chapter  Google Scholar 

  7. Kolodziej J, Khan SU (2012) Multi-level hierarchic genetic-based scheduling of independent jobs in dynamic heterogeneous grid environment. Inf Sci 214:1–19

    Article  Google Scholar 

  8. Xhafa F, Abraham A (2010) Computational models and heuristic methods for grid scheduling problems. Future Gener Comput Syst 26:608–621

    Article  Google Scholar 

  9. YarKhan A, Dongarra J (2002) Experiments with scheduling using simulated annealing in a grid environment. In: Proceedings of the third int workshop on grid computing, GRID ’02, London, UK. Springer, Berlin, pp 232–242

    Google Scholar 

  10. Lindberg P, Leingang J, Lysaker D, Bilal K, Khan SU, Bouvry P, Ghani N, Min-Allah N, Li J (2012) Comparison and analysis of greedy energy-efficient scheduling algorithms for computational grids. In: Zomaya AY, Lee Y-C (eds) Energy aware distributed computing systems. Wiley, New York, pp 246–253

    Google Scholar 

  11. Pinel F, Dorronsoro B, Pecero JE, Bouvry P, Khan SU (2012) A two-phase heuristic for the energy-efficient scheduling of independent tasks on computational grids. In: Cluster computing, pp 1–13

    Google Scholar 

  12. Nesmachnow S, Cancela H, Alba E (2012) A parallel micro evolutionary algorithm for heterogeneous computing and grid scheduling. Appl Soft Comput 12(2):626–639

    Article  MathSciNet  Google Scholar 

  13. Wang L, Chen D, Zhao J, Tao J (2012) Resource management of distributed virtual machines. Int J Ad Hoc Ubiq Comput 10(2):96–111

    Article  Google Scholar 

  14. Wang L, Khan SU, Dayal J (2012) Thermal aware workload placement with task-temperature profiles in a data center. J Supercomput 61(3):780–803

    Article  Google Scholar 

  15. Frederic P, Bernabe D, Pascal B (2012) Solving very large instances of the scheduling of independent tasks problem on the gpu. J Parallel Distrib Comput, 1–8. Available online 9 March 2012

  16. Nesmachnow S, Canabé M (2011) Gpu implementations of scheduling heuristics for heterogeneous computing environments. In: Proceedings of the XVII congreso argentino de ciencias de la computación, pp 1563–1570

    Google Scholar 

  17. Diaz CO, Guzek M, Pecero JE, Danoy G, Bouvry P, Khan SU (2011) Energy-aware fast scheduling heuristics in heterogeneous computing systems. In: HPCS, 2011 international conference, pp 478–484

    Google Scholar 

  18. Diaz CO, Guzek M, Pecero JE, Bouvry P, Khan SU (2011) Scalable and energy-efficient scheduling techniques for large-scale systems. In: IEEE 11th international CIT conference, 31 August–2 September 2011, pp 641–647

    Google Scholar 

  19. Al-Qawasmeh A, Maciejewski AA, Wang H, Smith J, Siegel HJ, Potter J (2011) Statistical measures for quantifying task and machine heterogeneities. J Supercomput 57(1):34–50

    Article  Google Scholar 

  20. Ghafoor A, Yang J (1993) A distributed heterogeneous supercomputing management system. IEEE Comput 26(6):78–86

    Article  Google Scholar 

  21. Pinel F, Pecero JE, Bouvry P, Ullah Khan S (2011) A review on task performance prediction in multi-core based systems. In: CIT, pp 615–620

    Google Scholar 

  22. Maheswaran M, Ali S, Siegel HJ, Hensgen D, Freund RF (1999) Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems. In: Proceedings of the eighth heterogeneous computing workshop, HCW ’99, Washington, DC, USA

    Google Scholar 

  23. Casanova H, Legrand A, Zagorodnov D, Berman F (1999) Heuristics for scheduling parameter sweep applications in grid environments. Technical report, La Jolla, CA, USA

  24. Ali S, Siegel HJ, Maheswaran M, Hensgen D (2000) Representing task and machine heterogeneities for heterogeneous computing systems. J Sci Eng 3(3):195–207

    Google Scholar 

  25. Bansal N (2003) Algorithms for flow time scheduling. PhD thesis, School of Computer Science, Carnegie Mellon University

Download references

Acknowledgements

The authors would like to thank Professor Samee U. Khan for his strong scientific and technical contribution in this work. We also would like to thank Dr. Dzmitry Kliazovich and Daniel Di Nardo for their valuable comments during the preparation of this paper. We also thanks to Mateusz Guzek for the implementation of the first version of the TPD algorithms. This work was supported by the National Research Fund FNR INTER-CNRS-11-03 Green@cloud project.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Cesar O. Diaz.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Diaz, C.O., Pecero, J.E. & Bouvry, P. Scalable, low complexity, and fast greedy scheduling heuristics for highly heterogeneous distributed computing systems. J Supercomput 67, 837–853 (2014). https://doi.org/10.1007/s11227-013-1038-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-013-1038-0

Keywords

Navigation