Skip to main content
SpringerLink
Log in

Characterization of the iterative application of makespan heuristics on non-makespan machines in a heterogeneous parallel and distributed environment

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

Abstract

Heterogeneous computing (HC) is the coordinated use of different types of machines, and networks to process a diverse workload in a manner that will maximize the combined performance and/or cost effectiveness of the system. Heuristics for allocating resources in an HC system are based on some optimization criterion. A common optimization criterion is to minimize the completion time of the machine that finishes last (makespan). In this study, we consider an iterative approach that repeatedly runs a mapping heuristic to minimize the makespan of the considered machines and tasks. For each successive iteration, the makespan machine of the previous iteration and the tasks assigned to it are removed from the set of considered machines and tasks. This study focuses on understanding the different mathematical characteristics of resource allocation heuristics that cause them to behave differently when combined with this iterative approach. This paper has three main contributions. The first contribution is the study of an iterative technique used in conjunction with resource allocation heuristics. The second contribution is the definition and mathematical characterization of “iteration invariant” heuristics. The third contribution is to determine the characteristics of a heuristic that will cause the mapping to change across iterations.

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

Similar content being viewed by others

References

  1. Ali S, Braun TD, Siegel HJ, Maciejewski AA, Beck N, Boloni L, Maheswaran M, Reuther AI, Robertson JP, Theys MD, Yao B (2005) Characterizing resource allocation heuristics for heterogeneous computing systems. In: Parallel, distributed, and pervasive computing. Advances in computers, vol 63, pp 91–128

    Google Scholar 

  2. Al-Azzoni I, Down DG (2007) Linear programming based affinity scheduling for heterogeneous computing systems. In: International conference on parallel and distributed processing techniques and applications (PDPTA’07), June 2007

    Google Scholar 

  3. Barada H, Sait SM, Baig N (2001) Task matching and scheduling in heterogeneous systems using simulated evolution. In: 10th IEEE heterogeneous computing workshop (HCW 2001) in the proceedings of the 15th international parallel and distributed processing symposium (IPDPS 2001), Apr 2001, pp 875–882

    Chapter  Google Scholar 

  4. Barbulescu L, Whitley LD, Howe AE (2004) Leap before you look: an effective strategy in an oversubscribed scheduling problem. In: 19th national conference on artificial intelligence, July 2004, pp 143–148

    Google Scholar 

  5. Berman F, Casanova H, Chien A, Cooper K, Dail H, Dasgupta A, Deng W, Dongarra J, Johnsson L, Kennedy K, Koelbel C, Liu B, Liu X, Mandal A, Marin G, Mazina M, Mellor-Crummey J, Mendes C, Olugbile A, Patel M, Reed D, Shi Z, Sievert O, Xia H, YarKhan A (2005) New grid scheduling and rescheduling methods in the GrADS project. Int J Parallel Program 33(2–3):209–229

    Article  Google Scholar 

  6. Berman F, Wolski R, Casanova H, Cirne W, Dail H, Faerman M, Figueira S, Hayes J, Obertelli G, Schopf J, Shao G, Smallen S, Spring S, Su A, Zagorodnov D (2003) Adaptive computing on the grid using AppLeS. IEEE Trans. Parallel Distrib. Syst. 14(4):369–382

    Article  Google Scholar 

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

    Chapter  Google Scholar 

  8. Briceno LD, Khemka B, Siegel HJ, Maciejewski AA, Groer C, Koenig G, Okonski G, Poole S (2011) Time utility functions for modeling and evaluating resource allocations in a heterogeneous computing system. In: 20th international heterogeneity in computing workshop (HCW’11) in the proceedings of the parallel and distributed processing workshops and Phd forum (IPDPSW 2011), pp 7–19

    Chapter  Google Scholar 

  9. Braun TD, Siegel HJ, Beck N, Boloni L, Freund RF, Hensgen D, Maheswaran M, Reuther AI, Robertson JP, Theys MD, Yao B (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 

  10. Briceño LD, Siegel HJ, Maciejewski AA, Oltikar M, Brateman J, White J, Martin J, Knapp K (2011) Heuristics for robust resource allocation of satellite weather data processing on a heterogeneous parallel system. IEEE Trans Parallel Distrib Syst 22(11):1780–1787

    Article  Google Scholar 

  11. Caron E, Garonne V, Tsaregorodtsev A (2007) Definition, modelling and simulation of a grid computing scheduling system for high throughput computing. Future Gener Comput Syst 23(8):968–976

    Article  Google Scholar 

  12. Caniou Y, Jeannot E (2006) Multicriteria scheduling heuristics for Gridrpc systems. Int J High Perform Comput Appl 20(1):443–454

    Article  Google Scholar 

  13. Casanova H, Legrand A, Zagorodnov D, Berman F (2000) Heuristics for scheduling parameter sweep applications in grid environments. In: 9th IEEE heterogeneous computing workshop (HCW’2000), Mar. 2000, pp 349–363

    Chapter  Google Scholar 

  14. Curran O, Shearer A (2009) A workflow model for heterogeneous computing environments. Future Gener Comput Syst 25(4):414–425

    Article  Google Scholar 

  15. Dhodhi MK, Ahmad I, Yatama A (2002) An integrated technique for task matching and scheduling onto distributed heterogeneous computing systems. J Parallel Distrib Comput 62(9):1338–1361

    Article  MATH  Google Scholar 

  16. Ding Q, Chen G (2001) A benefit function mapping heuristic for a class of meta-tasks in grid environments. In: 1st international symposium on cluster computing and the grid (CCGRID ’01), May 2001

    Google Scholar 

  17. Dogan A, Ozguner F (2004) Genetic algorithm based scheduling of meta-tasks with stochastic execution times in heterogeneous computing systems. Clust Comput 7(2):177–190

    Article  Google Scholar 

  18. Freund RF, Siegel HJ (1993) Heterogeneous processing. Computer 26(6):13–17

    Google Scholar 

  19. Ghanbari S, Meybodi MR (2005) Learning automata based algorithms for mapping of a class of independent tasks over highly heterogeneous grids. In: European grid conference (EGC2005), Feb 2005, pp 681–690

    Google Scholar 

  20. Ghanbari S, Meybodi MR (2005) On-line mapping algorithms in highly heterogeneous computational grids: a learning automata approach. In: International conference on information and knowledge technology (IKT2005), May 2005

    Google Scholar 

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

    Article  Google Scholar 

  22. Ibarra OH, Kim CE (1977) Heuristic algorithms for scheduling independent tasks on non-identical processors. J ACM 24(2):280–289

    Article  MathSciNet  MATH  Google Scholar 

  23. Jinquan Z, Lina N, Changjun J (2005) A heuristic scheduling strategy for independent tasks on grid. In: Eighth international conference on high-performance computing in Asia–Pacific region 2005, Nov 2005

    Google Scholar 

  24. Kafil M, Ahmad I (1998) Optimal task assignment in heterogeneous distributed computing systems. IEEE Concurr 6(3):42–51

    Article  Google Scholar 

  25. Kaya K, Ucar B, Aykanat C (2007) Heuristics for scheduling file-sharing tasks on heterogeneous systems with distributed repositories. J Parallel Distrib Comput 67(3):271–285

    Article  MATH  Google Scholar 

  26. Khokhar A, Prasanna VK, Shaaban ME, Wang C (1993) Heterogeneous computing: challenges and opportunities. Computer 26(6):18–27

    Article  Google Scholar 

  27. Kim J-K, Siegel HJ, Maciejewski AA, Eigenmann R (2008) Dynamic resource management in energy constrained heterogeneous computing systems using voltage scaling. IEEE Trans Parallel Distrib Syst 19(11):1445–1457 (Special issue on power-aware parallel and distributed systems)

    Article  Google Scholar 

  28. Kumar A, Shorey R (1993) Performance analysis and scheduling of stochastic fork-join jobs in a multicomputer system. IEEE Trans Parallel Distrib Syst 4(10):1147–1164

    Article  Google Scholar 

  29. Leangsuksun C, Potter J, Scott S (1995) Dynamic task mapping algorithms for a distributed heterogeneous computing environment. In: 4th IEEE heterogeneous computing workshop (HCW’1995), Apr 1995, pp 30–34

    Google Scholar 

  30. Lee YC, Zomaya AY, Siegel HJ (2010) Robust task scheduling for volunteer computing systems. J Supercomput 53:163–181

    Article  Google Scholar 

  31. Lindberg P, Leingang J, Lysaker D, Khan SU, Li J (2012) Comparison and analysis of eight scheduling heuristics for the optimization of energy consumption and makespan in large-scale distributed systems. J Supercomput 59:323–360

    Article  Google Scholar 

  32. Maheswaran M, Ali S, Siegel HJ, Hensgen D, Freund RF (1999) Dynamic mapping of a class of independent tasks onto heterogeneous computing systems. J Parallel Distrib Comput 59(2):107–121

    Article  Google Scholar 

  33. Maheswaran M, Braun TD, Siegel HJ (1999) Heterogeneous distributed computing. In: Webster JG (ed) Encyclopedia of electrical and electronics engineering, vol 8. Wiley, New York, pp 679–690

    Google Scholar 

  34. Magoulès F, Pan J, Tan K-A, Kumar A (2009) Introduction to grid computing. Numerical anal & scient comp series. Chapman & Hall/CRC, London

    MATH  Google Scholar 

  35. Machtans E, Sato L, Deppman A (2009) Improvement on scheduling dependent tasks for grid applications. In: International conference on computational science and engineering (CSE’09), Aug 2009, vol 1, pp 95–102

    Chapter  Google Scholar 

  36. Mehta AM, Smith J, Siegel HJ, Maciejewski AA, Jayaseelan A, Ye B (2007) Dynamic resource allocation heuristics that manage tradeoff between makespan and robustness. J Supercomput 42(1):33–58 (Special issue on grid technology)

    Article  Google Scholar 

  37. Paranhos D, Cirne W, Brasileiro F (2003) Trading cycles for information: using replication to schedule bag-of-tasks applications on computational grids. In: International conference on parallel and distributed computing, Aug. 2003

    Google Scholar 

  38. Russell S, Norvig P (2005) Artificial intelligence a modern approach, 2nd edn. Prentice Hall, New York

    Google Scholar 

  39. SaiRanga P, Baskiyar S (2005) A low complexity algorithm for dynamic scheduling of independent tasks onto heterogeneous computing systems. In: 43rd annual southeast regional conference, Mar 2005, vol 1, pp 63–68

    Chapter  Google Scholar 

  40. Shestak V, Chong EKP, Siegel HJ, Maciejewski AA, Benmohamed L, Wang I-J, Daley R (2008) A hybrid branch-and-bound and evolutionary approach for allocating strings of applications to heterogeneous distributed computing systems. J Parallel Distrib Comput 68(4):410–426

    Article  MATH  Google Scholar 

  41. Singh H, Youssef A (1996) Mapping and scheduling heterogeneous task graphs using genetic algorithms. In: 5th IEEE heterogeneous computing workshop (HCW ’1996), Apr 1996, pp 86–97

    Google Scholar 

  42. Sonmez OO, Gursoy A (2007) A novel economic-based scheduling heuristic for computational grids. Int J High Perform Comput Appl 21(1):21–29

    Article  Google Scholar 

  43. Wei B, Fedak G, Cappello F (2005) Scheduling independent tasks sharing large data distributed with bittorrent. In: The 6th IEEE/ACM international workshop on grid computing, Nov 2005

    Google Scholar 

  44. Whitley D (1989) The GENITOR algorithm and selective pressure: why rank based allocation of reproductive trials is best. In: 3rd international conference on genetic algorithms, June 1989, pp 116–121

    Google Scholar 

  45. Wu M, Shu W (2000) Segmented min-min: a static mapping algorithm for meta-tasks on heterogeneous computing systems. In: 9th IEEE heterogeneous computing workshop (HCW’2000), Mar. 2000, pp 375–385

    Google Scholar 

  46. Xu D, Nahrstedt K, Wichadakul D (2001) QoS and contention-aware multi-resource reservation. Clust Comput 4(2):95–107

    Article  Google Scholar 

  47. Yang J, Ahmad I, Ghafoor A (1993) Estimation of execution times on heterogeneous supercomputer architectures. In: International conference on parallel processing, Aug 1993, vol I, pp 219–225

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical & Computer Engineering, Colorado State University, Fort Collins, CO, 80523, USA

    Luis Diego Briceño, Howard Jay Siegel, Anthony A. Maciejewski & Mohana Oltikar

  2. Department of Computer Science, Colorado State University, Fort Collins, CO, 80523, USA

    Howard Jay Siegel

Authors
  1. Luis Diego Briceño
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Howard Jay Siegel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anthony A. Maciejewski
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohana Oltikar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luis Diego Briceño.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Briceño, L.D., Siegel, H.J., Maciejewski, A.A. et al. Characterization of the iterative application of makespan heuristics on non-makespan machines in a heterogeneous parallel and distributed environment. J Supercomput 62, 461–485 (2012). https://doi.org/10.1007/s11227-011-0729-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-011-0729-7

Keywords

Search

Navigation