Skip to main content

Penalty Scheduling Policy Applying User Estimates and Aging for Supercomputing Centers

  • Conference paper
  • First Online:
Book cover High Performance Computing (CARLA 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 697))

Included in the following conference series:

Abstract

In this article we address the problem of scheduling on realistic high performance computing facilities using incomplete information about tasks execution times. We introduce a variation of our previous Penalty Scheduling Policy, including an aging scheme that increases the priority of jobs over time. User-provided runtime estimates are applied as in the original Penalty Scheduling Policy, but a realistic priority schema is proposed to avoid starvation. The experimental evaluation of the proposed scheduler is performed using real workload logs, and validated using a job scheduler simulator. We study different realistic workload scenarios to evaluate the performance of the Penalty Scheduling Policy with aging. The main results suggest that using the proposed scheduler with the aging scheme, the waiting time of jobs in the high performance computing facility is significantly reduced (up to 50% in average).

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Cirne, W., Berman, F.: A comprehensive model of the supercomputer workload. In: IEEE International Workshop on Workload Characterization, pp. 140–148 (2001)

    Google Scholar 

  2. Tsafrir, D.: Using inaccurate estimates accurately. In: Frachtenberg, E., Schwiegelshohn, U. (eds.) JSSPP 2010. LNCS, vol. 6253, pp. 208–221. Springer, Heidelberg (2010). doi:10.1007/978-3-642-16505-4_12

    Chapter  Google Scholar 

  3. Tsafrir, D., Etsion, Y., Feitelson, D.G.: Modeling user runtime estimates. In: Feitelson, D., Frachtenberg, E., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2005. LNCS, vol. 3834, pp. 1–35. Springer, Heidelberg (2005). doi:10.1007/11605300_1

    Chapter  Google Scholar 

  4. Rocchetti, N., Iturriaga, S., Nesmachnow, S.: Including accurate user estimates in HPC schedulers: an empirical analysis. In: XXI Congreso Argentino de Ciencias de la Computación, pp. 1–10 (2015)

    Google Scholar 

  5. Lee, C.B., Schwartzman, Y., Hardy, J., Snavely, A.: Are user runtime estimates inherently inaccurate? In: Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2004. LNCS, vol. 3277, pp. 253–263. Springer, Heidelberg (2005). doi:10.1007/11407522_14

    Chapter  Google Scholar 

  6. Feitelson, D.: Parallel Workloads Archive. http://www.cs.huji.ac.il/labs/parallel/workload/. Accessed 12 July 2016

  7. Ward Jr., W.A., Mahood, C.L., West, J.E.: Scheduling jobs on parallel systems using a relaxed backfill strategy. In: Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2002. LNCS, vol. 2537, pp. 88–102. Springer, Heidelberg (2002). doi:10.1007/3-540-36180-4_6

    Chapter  Google Scholar 

  8. Chiang, S.-H., Arpaci-Dusseau, A., Vernon, M.K.: The impact of more accurate requested runtimes on production job scheduling performance. In: Feitelson, D.G., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2002. LNCS, vol. 2537, pp. 103–127. Springer, Heidelberg (2002). doi:10.1007/3-540-36180-4_7

    Chapter  Google Scholar 

  9. Hirales-Carbajal, A., Tchernykh, A., Yahyapour, R., González-García, J.L., Röblitz, T., Ramírez-Alcaraz, J.M.: Multiple workflow scheduling strategies with user runtime estimates on a grid. J. Grid Comput. 10, 325–346 (2012)

    Article  Google Scholar 

  10. Ramírez-Alcaraz, J.M., Tchernykh, A., Yahyapour, R., Schwiegelshohn, U., Quezada-Pina, A., González-García, J.L., Hirales-Carbajal, A.: Job allocation strategies with user run time estimates for online scheduling in hierarchical grids. J. Grid Comput. 9, 95–116 (2011)

    Article  Google Scholar 

  11. Tsafrir, D., Etsion, Y., Feitelson, D.G.: Backfilling using system-generated predictions rather than user runtime estimates. IEEE Trans. Parallel Distrib. Syst. 18, 789–803 (2007)

    Article  Google Scholar 

  12. Nesmachnow, S.: Computación Científica de Alto Desempeño en la Facultad de Ingeniería, Universidad de la República. Rev. Asoc. Ing. Urug. 61(1), 12–15 (2010)

    Google Scholar 

  13. Feitelson, D., Tsafrir, D.: Workload sanitation for performance evaluation. In: IEEE International Symposium on Performance Analysis of Systems and Software, pp. 221–230 (2006)

    Google Scholar 

  14. Slurm simulator web page. https://www.bsc.es/marenostrum-support-services/services/slurm-simulator. Accessed 12 July 2016

  15. Iturriaga, S., García, S., Nesmachnow, S.: An empirical study of the robustness of energy-aware schedulers for high performance computing systems under uncertainty. In: Hernández, G., Hernández, C.J.B., Díaz, G., Garino, C.G., Nesmachnow, S., Pérez-Acle, T., Storti, M., Vázquez, M. (eds.) CARLA 2014. CCIS, vol. 485, pp. 143–157. Springer, Heidelberg (2014). doi:10.1007/978-3-662-45483-1_11

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nestor Rocchetti .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Rocchetti, N., Da Silva, M., Nesmachnow, S., Tchernykh, A. (2017). Penalty Scheduling Policy Applying User Estimates and Aging for Supercomputing Centers. In: Barrios Hernández, C., Gitler, I., Klapp, J. (eds) High Performance Computing. CARLA 2016. Communications in Computer and Information Science, vol 697. Springer, Cham. https://doi.org/10.1007/978-3-319-57972-6_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-57972-6_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57971-9

  • Online ISBN: 978-3-319-57972-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics