Skip to main content
SpringerLink
Log in

Optimization of resources in parallel systems using a multiobjective artificial bee colony algorithm

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

Abstract

Most of the approaches to achieve exascale computing heavily rely on designing power efficient hardware, but experts usually forget that the usage of efficient middlewares, like resource managers or job schedulers, can also play an important role in optimizing power and performance of supercomputing infrastructures. For the optimization of both, power and performance, we propose the implementation of a multiobjective version of artificial bee colony algorithm (MOABC). We have compared our algorithm with other deterministic (first-fit and MOHEFT) and stochastic (NSGA-II) resource selection approaches. The results of our simulations show that, in real computing environments, MOABC is more likely to obtain better optimizations of response times and power consumption.

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

Similar content being viewed by others

References

  1. Bodas D, Song J, Rajappa M, Hoffman A (2014) Simple power-aware scheduler to limit power consumption by HPC system within a budget. In: Proceedings of the 2nd International Workshop on Energy Efficient Supercomputing, E2SC ’14. IEEE Press, Piscataway, NJ, USA, pp 21–30. https://doi.org/10.1109/E2SC.2014.8

  2. Chakraborty A, Kar AK (2017) Swarm intelligence: a review of algorithms. In: Patnaik S, Yang XS, Nakamatsu K (eds) Nature-inspired computing and optimization: theory and applications. Springer, Cham, pp 475–494. https://doi.org/10.1007/978-3-319-50920-4_19

    Chapter  Google Scholar 

  3. Chiesi M, Vanzolini L, Mucci C, Scarselli EF, Guerrieri R (2015) Power-aware job scheduling on heterogeneous multicore architectures. IEEE Trans Parallel Distrib Syst 26(3):868–877. https://doi.org/10.1109/TPDS.2014.2315203

    Article  Google Scholar 

  4. Cicirello VA (2006) Non-wrapping order crossover: an order preserving crossover operator that respects absolute position. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation. ACM, pp 1125–1132. https://doi.org/10.1145/1143997.1144177

  5. Coello CA, Dhaenens C, Jourdan L (2010) Advances in multi-objective nature inspired computing. Springer, Berlin. https://doi.org/10.1007/978-3-642-11218-8

    Book  MATH  Google Scholar 

  6. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197. https://doi.org/10.1109/4235.996017

    Article  Google Scholar 

  7. Durillo JJ, Nae V, Prodan R (2014) Multi-objective energy-efficient workflow scheduling using list-based heuristics. Fut Gener Comput Syst 36:221–236. https://doi.org/10.1016/j.future.2013.07.005

    Article  Google Scholar 

  8. Etinski M, Corbalan J, Labarta J, Valero M (2012) Parallel job scheduling for power constrained HPC systems. Parallel Comput 38(12):615–630. https://doi.org/10.1016/j.parco.2012.08.001

    Article  MathSciNet  Google Scholar 

  9. Feitelson D (2001) Metrics for parallel job scheduling and their convergence. In: Job Scheduling Strategies for Parallel Processing, Lecture Notes in Computer Science, vol. 2221. Springer Berlin Heidelberg, pp 188–205. https://doi.org/10.1007/3-540-45540-X_11

  10. Gómez-Martín C, Vega-Rodríguez MA, González-Sánchez JL (2015) Performance and energy aware scheduling simulator for HPC: evaluating different resource selection methods. Concurr Comput Pract Exp 27(17):5436–5459. https://doi.org/10.1002/cpe.3607

    Article  Google Scholar 

  11. Goldberg DE (1989) Genetic algorithm in search optimization and machine learning. Addison Wesley Publishing Company, Reading

    MATH  Google Scholar 

  12. Kaplan JM, Forrest W, Kindler N (2008) Revolutionizing data center energy efficiency. Technical Report, McKinsey & Company

  13. Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471. https://doi.org/10.1007/s10898-007-9149-x

    Article  MathSciNet  MATH  Google Scholar 

  14. Karaboga D, Gorkemli B, Ozturk C, Karaboga N (2014) A comprehensive survey: artificial bee colony (ABC) algorithm and applications. Artif Intell Rev 42(1):21–57. https://doi.org/10.1007/s10462-012-9328-0

    Article  Google Scholar 

  15. Lee EK, Viswanathan H, Pompili D (2017) Proactive thermal-aware resource management in virtualized HPC cloud datacenters. IEEE Trans Cloud Comput 5(2):234–248. https://doi.org/10.1109/TCC.2015.2474368

    Article  Google Scholar 

  16. Lifka D (1995) The ANL/IBM SP scheduling system. In: Job Scheduling Strategies for Parallel Processing: IPPS ’95 Workshop, Lecture Notes in Computer Science, vol. 949. Springer Berlin Heidelberg, pp 295–303. https://doi.org/10.1007/3-540-60153-8_35

  17. Mair J, Huang Z, Eyers D, Chen Y (2015) Quantifying the energy efficiency challenges of achieving exascale computing. In: 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp 943–950. https://doi.org/10.1109/CCGrid.2015.130

  18. Maqbool J, Oh S, Fox GC (2015) Evaluating ARM HPC clusters for scientific workloads. Concurr Comput Pract Exp 27(17):5390–5410. https://doi.org/10.1002/cpe.3602

    Article  Google Scholar 

  19. Messina P (2017) The exascale computing project. Comput Sci Eng 19(3):63–67. https://doi.org/10.1109/MCSE.2017.57

    Article  Google Scholar 

  20. Mu’alem AW, Feitelson DG (2001) Utilization, predictability, workloads, and user runtime estimates in scheduling the IBM SP2 with backfilling. IEEE Trans Parallel Distrib Syst 12(6):529–543. https://doi.org/10.1109/71.932708

    Article  Google Scholar 

  21. Novotnỳ M (2009) Job scheduling with the SLURM resource manager. Bachelor Thesis, Masaryk University, Brno, Czech Republic

  22. Rajovic N, Vilanova L, Villavieja C, Puzovic N, Ramirez A (2013) The low power architecture approach towards exascale computing. J Comput Sci 4(6):439–443. https://doi.org/10.1016/j.jocs.2013.01.002

    Article  Google Scholar 

  23. Sofia A Sathya, GaneshKumar P (2018) Multi-objective task scheduling to minimize energy consumption and makespan of cloud computing using NSGA-II. J Netw Syst Manag 26(2):463–485. https://doi.org/10.1007/s10922-017-9425-0

    Article  Google Scholar 

  24. Spec power and performance benchmark methodology v2.1. https://www.spec.org/power/docs/SPEC-Power_and_Performance_Methodology.pdf. Accessed: 2018-4-27

  25. Specpower_ssj2008 results. http://www.spec.org/power_ssj2008/results/. Accessed: 2018-4-27

  26. Staples G (2006) TORQUE resource manager. In: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing, SC ’06. ACM, New York, NY, USA. https://doi.org/10.1145/1188455.1188464

  27. Tang K, Tiwari D, Gupta S, Vazhkudai SS, He X (2017) Effective running of end-to-end HPC workflows on emerging heterogeneous architectures. In: 2017 IEEE International Conference on Cluster Computing (CLUSTER), pp 344–348. https://doi.org/10.1109/CLUSTER.2017.22

  28. The Green500 list. http://green500.org. Accessed: 2018-4-27

  29. Wang B, Schmidl D, Terboven C, Müller MS (2017) Dynamic application-aware power capping. In: Proceedings of the 5th International Workshop on Energy Efficient Supercomputing, E2SC’17. ACM, New York, NY, USA, pp 1:1–1:8. https://doi.org/10.1145/3149412.3149413

  30. Yang X, Zhou Z, Wallace S, Lan Z, Tang W, Coghlan S, Papka ME (2013) Integrating dynamic pricing of electricity into energy aware scheduling for HPC systems. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC ’13. ACM, New York, NY, USA, pp 60:1–60:11. https://doi.org/10.1145/2503210.2503264

  31. Yoo AB, Jette MA, Grondona M (2003) SLURM: simple linux utility for resource management. In: Job Scheduling Strategies for Parallel Processing. Springer, pp 44–60. https://doi.org/10.1007/10968987_3

  32. Zola E, Kassler AJ (2017) Optimising for energy or robustness? Trade-offs for VM consolidation in virtualized datacenters under uncertainty. Optim Lett 11(8):1571–1592. https://doi.org/10.1007/s11590-016-1065-x

    Article  MathSciNet  MATH  Google Scholar 

  33. Zotkin D, Keleher P (1999) Job-length estimation and performance in backfilling schedulers. In: Proceedings of the Eighth International Symposium on High Performance Distributed Computing, pp 236–243. https://doi.org/10.1109/HPDC.1999.805303

Download references

Acknowledgements

This work was partially Funded by the AEI (State Research Agency, Spain) and the ERDF (European Regional Development Fund, EU), under the contract TIN2016-76259-P (PROTEIN Project).

Author information

Authors and Affiliations

  1. Escuela Politécnica de Cáceres, University of Extremadura, Avda. Universidad s/n, 10003, Cáceres, Spain

    César Gómez-Martín & Miguel A. Vega-Rodríguez

Authors
  1. César Gómez-Martín
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Miguel A. Vega-Rodríguez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to César Gómez-Martín.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gómez-Martín, C., Vega-Rodríguez, M.A. Optimization of resources in parallel systems using a multiobjective artificial bee colony algorithm. J Supercomput 74, 4019–4036 (2018). https://doi.org/10.1007/s11227-018-2407-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-018-2407-5

Keywords

Search

Navigation