Skip to main content

Advertisement

Log in

A reformed task scheduling algorithm for heterogeneous distributed systems with energy consumption constraints

Neural Computing and Applications Aims and scope Submit manuscript

Abstract

As the scale increases and performance improves, the energy consumption of high-performance computer systems is rapidly increasing. The energy-aware task scheduling for high-performance computer systems has become a hot spot for major supercomputing centers and data centers. In this paper, we study the task scheduling problem to minimize the schedule length of parallel applications while satisfying the energy constraints in heterogeneous distributed systems. The existing approaches mainly allocate unassigned tasks with minimal energy consumption which cannot achieve optimistic scheduling length in most cases. Based on this situation, we propose a reformed scheduling method with energy consumption constraint algorithm, which is based on an energy consumption level to pre-allocate energy consumption for unassigned tasks. The experimental results show that compared with the existing algorithms, our new algorithm can achieve better scheduling length under the energy consumption constraints.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Baital K, Chakrabarti A (2019) Dynamic scheduling of real-time tasks in heterogeneous multicore systems. IEEE Embed Syst Lett 1(1):29–32

    Article  Google Scholar 

  2. Chase JB, Prachi Thakar DCA (2001) Managing energy and server resources for a hosting center. In: ACM symposium on operating systems principles. ACM, pp 528–535

  3. Chen C (2018) An improved approximation for scheduling malleable tasks with precedence constraints via iterative method. IEEE Trans Parallel Distrib Syst 29(9):1937–1946

    Article  Google Scholar 

  4. Chen J, Li K, Bilal K, Zhou X, Li K, Yu PS (2019) A bi-layered parallel training architecture for large-scale convolutional neural networks. IEEE Trans Parallel Distrib Syst 30(5):965–976

    Article  Google Scholar 

  5. Chen J, Li K, Deng Q, Li K, Yu PS (2019) Distributed deep learning model for intelligent video surveillance systems with edge computing. IEEE Trans Ind Inform 22(3):11–18

    Google Scholar 

  6. Chen J, Li K, Rong H, Bilal K, Yang N, Li K (2018) A disease diagnosis and treatment recommendation system based on big data mining and cloud computing. Inf Sci 435:124–149

    Article  Google Scholar 

  7. Chen J, Li K, Tang Z, yu S, Li K (2017) A parallel random forest algorithm for big data in spark cloud computing environment. IEEE Trans Parallel Distrib Syst 28(4):919–933

    Article  Google Scholar 

  8. Chen M, Hao Y, Lai CF, Wu D, Li Y, Hwang K (2018) Opportunistic task scheduling over co-located clouds in mobile environment. IEEE Trans Serv Comput 11(3):549–561

    Article  Google Scholar 

  9. Chen M, Zhang X, Gu H, Wei T, Zhu Q (2018) Sustainability-oriented evaluation and optimization for mpsoc task allocation and scheduling under thermal and energy variations. IEEE Trans Sustain Comput 3(2):84–97

    Article  Google Scholar 

  10. Chen Y, Li K, Yang W, Xiao G, Xie X, Li T (2019) Performance-aware model for sparse matrix–matrix multiplication on the sunway taihulight supercomputer. IEEE Trans Parallel Distrib Syst 30(4):923–938

    Article  Google Scholar 

  11. Demirci G, Marincic I, Hoffmann H (2018) A divide and conquer algorithm for dag scheduling under power constraints. In: The international conference for high performance computing, networking, storage, and analysis. IEEE, p 4660477

  12. Ge R, Feng X, Cameron K (2005) Performance-constrained distributed dvs scheduling for scientific applications on power-aware clusters. In: Proceedings of the ACM/IEEE supercomputing. ACM/IEEE, pp 34–34

  13. Jin Y, Xu J, Qiu L (2014) Energy-efficient scheduling with individual packet delay constraints and non-ideal circuit power. J Commun Netw 16(1):36–44

    Article  Google Scholar 

  14. Lee YC, Zomaya AY (2011) Energy conscious scheduling for distributed computing systems under different operating conditions. IEEE Trans Parallel Distrib Syst 22(8):1374–1381

    Article  Google Scholar 

  15. Li J, Liu Y, Li H, Yuan Z, Fu C, Yue J, Feng X, Xue CJ, Hu J, Yang H (2018) Path: performance-aware task scheduling for energy-harvesting nonvolatile processors. IEEE Trans Very Large Scale Integr Syst 26(9):1671–1684

    Article  Google Scholar 

  16. Li K, Tang X, Li K (2014) Energy-efficient stochastic task scheduling on heterogeneous computing systems. IEEE Trans Parallel Distrib Syst 25(11):2867–2876

    Article  Google Scholar 

  17. Li K, Tang X, Veeravalli B, Li K (2015) Scheduling precedence constrained stochastic tasks on heterogeneous cluster systems. IEEE Trans Comput 64(1):191–204

    Article  MathSciNet  MATH  Google Scholar 

  18. Namazi A, Safari S, Mohammadi S (2019) CMV: clustered majority voting reliability-aware task scheduling for multicore real-time systems. IEEE Trans Reliab 68(1):187–200

    Article  Google Scholar 

  19. Rountree B, Lownenthal DK, de Supinski BR, Schulz M, Freeh VW, Bletsch T (2009) Adagio: making DVS practical for complex HPC applications. In: International conference on supercomputing. ACM, pp 460–469

  20. Sanchez CA, Mokrenko O, Zaccarian L, Lesecq S (2018) A hybrid control law for energy-oriented tasks scheduling in wireless sensor networks. IEEE Trans Control Syst Technol 26(6):1995–2007

    Article  Google Scholar 

  21. Sun H, Elghazi R, Gainaru A, Aupy G, Raghavan P (2018) Scheduling parallel tasks under multiple resources: list scheduling vs. pack scheduling. In: International parallel and distributed processing symposium. pp 194–203

  22. Tang Z, Qi L, Cheng Z, Li K, Khan SU, Li K (2016) An energy-efficient task scheduling algorithm in dvfs-enabled cloud environment. J Grid Comput 14(1):55–74

    Article  Google Scholar 

  23. Wang N, Chen S, Ni J, Ling X, Zhu Y (2018) Security-aware task scheduling using untrusted components in high-level synthesis. IEEE Access 6:15663–15678

    Article  Google Scholar 

  24. Wang Y, Li K, Chen H, He L, Li K (2014) Energy-aware data allocation and task scheduling on heterogeneous multiprocessor systems with time constraints. IEEE Trans Emerg Top Comput 2(2):134–148

    Article  Google Scholar 

  25. Xiao G, Li K, Chen Y, He W, Zomaya A, Li T (2019) CASpMV: a customized and accelerative spmv framework for the sunway taihulight. IEEE Trans Parallel Distrib Syst 30(5):1–12

    Article  Google Scholar 

  26. Xiao G, Li K, Li K (2017) Reporting l most influential objects in uncertain databases based on probabilistic reverse top-k queries. Inf Sci 405:207–226

    Article  Google Scholar 

  27. Xiao X, Xie G, Li R, Li K (2016) Minimizing schedule length of energy consumption constrained parallel applications on heterogeneous distributed systems. In: IEEE Trustcom/BigDataSE/ISPA. IEEE, pp 1471–1476

  28. Xu Y, Li K, Hu J, Li K (2014) A genetic algorithm for task scheduling on heterogeneous computing systems using multiple priority queues. Inf Sci 270:255–287

    Article  MathSciNet  MATH  Google Scholar 

  29. Yadav R, Zhang W, Kaiwartya O, Singh PR, Elgendy IA, Tian Y (2018) Adaptive energy-aware algorithms for minimizing energy consumption and SLA violation in cloud computing. IEEE Access 6:55923–55936

    Article  Google Scholar 

  30. Yang Y, Wang K, Zhang G, Chen X, Luo X, Zhou MT (2018) MEETS: maximal energy efficient task scheduling in homogeneous fog networks. IEEE Internet Things J 5(5):4076–4087

    Article  Google Scholar 

  31. Zeng G, Matsubara Y, Tomiyama H, Takada H (2016) Energy-aware task migration for multiprocessor real-time systems. Future Gener Comput Syst 56:220–228

    Article  Google Scholar 

  32. Zhang G, Shen F, Chen N, Zhu P, Dai X, Yang Y (2019) DOTS: delay-optimal task scheduling among voluntary nodes in fog networks. IEEE Internet Things J 6(2):3533–3544

    Article  Google Scholar 

  33. Zhang L, Li K, Li C, Li K (2017) Bi-objective workflow scheduling of the energy consumption and reliability in heterogeneous computing systems. Inf Sci 379:241–256

    Article  Google Scholar 

  34. Zhang L, Li K, Xu Y, Mei J, Zhang F, Li K (2015) Maximizing reliability with energy conservation for parallel task scheduling in a heterogeneous cluster. Inf Sci 319:113–131

    Article  MathSciNet  Google Scholar 

  35. Zhang Q, Lin M, Yang LT, Chen Z, Li P (2019) Energy-efficient scheduling for real-time systems based on deep Q-learning model. IEEE Trans Sustain Comput 4(1):132–141

    Article  Google Scholar 

  36. Zhao B, Aydin H, Zhu D (2013) Shared recovery for energy efficiency and reliability enhancements in real-time applications with precedence constraints. ACM Trans Des Autom Electron Syst 18(2):23–35

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yikun Hu.

Ethics declarations

Conflict of interest

The authors Yikun Hu, Jinghong Li and Ligang He are from Hunan University, and they declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, Y., Li, J. & He, L. A reformed task scheduling algorithm for heterogeneous distributed systems with energy consumption constraints. Neural Comput & Applic 32, 5681–5693 (2020). https://doi.org/10.1007/s00521-019-04415-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04415-2

Keywords

Navigation