Skip to main content

Advertisement

Log in

Reliability-aware task scheduling for energy efficiency on heterogeneous multiprocessor systems

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

Abstract

Recent studies mainly focus on high performance or low power consumption for task scheduling on heterogeneous multiprocessor systems (HMSs). Dynamic voltage and frequency scaling (DVFS) is an important energy reduction technique, which adjusts the voltage and frequency of the processor while the task is executing. However, some studies have shown that reducing the voltage of processor increases the transient failure rate, which reduces system reliability. In this paper, we aim at addressing the scheduling problem of optimizing energy under makespan and reliability constraints on HMSs with DVFS. We first propose an improved whale optimization algorithm (WOA) deploying opposition-based learning and individual selection strategy, which can balance the exploration and exploitation ability. To maintain population diversity, we then apply a constrained rank-based method which retains some infeasible individuals in the population. Finally, we reschedule the Critical Path Nodes (CPNs) to further improve the performance of improved WOA. The main difference between our work and most previous works is that we study a new scheduling problem, and utilize an improved WOA algorithm integrating with rescheduling CPNs and a constrained rank-based method. Extensive experiments are conducted to evaluate our proposed algorithm, and the evaluation results show that our proposed algorithm is compelling in comparison with the state-of-the-art algorithms.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Liu X, Sun J, Zheng L, Wang S, Liu Y, Wei T (2021) Parallelization and optimization of NSGA-II on sunway taihulight system. IEEE Trans Parallel Distrib Syst 32(4):975–987

    Article  Google Scholar 

  2. Taheri G, Khonsari A, Entezari-Maleki R, Sousa L (2020) A hybrid algorithm for task scheduling on heterogeneous multiprocessor embedded systems. Appl Soft Comput 1–14

  3. Venkatachalam V, Franz M (2005) Power reduction techniques for microprocessor systems. ACM Comput Surv (CSUR) 37(3):195–237

    Article  Google Scholar 

  4. 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 

  5. Chen S, Li Z, Yang B, Rudolph G (2016) Quantum-inspired hyper-heuristics for energy-aware scheduling on heterogeneous computing systems. IEEE Trans Parallel Distrib Syst 27(6):1796–1810

    Article  Google Scholar 

  6. Safari M, Khorsand R (2018) Pl-dvfs: combining power-aware list-based scheduling algorithm with dvfs technique for real-time tasks in cloud computing. J Supercomput 74(10):5578–5600

    Article  Google Scholar 

  7. Huang K, Jiang X, Zhang X, Yan R, Wang K, Xiong D, Yan X (2018) Energy-efficient fault-tolerant mapping and scheduling on heterogeneous multiprocessor real-time systems. IEEE Access 6:57614–57630

    Article  Google Scholar 

  8. Xiao X, Xie G, Xu C, Fan C, Li R, Li K (2018) Maximizing reliability of energy constrained parallel applications on heterogeneous distributed systems. J Comput Sci 26:344–353

    Article  MathSciNet  Google Scholar 

  9. Zhou J, Cao K, Cong P, Wei T, Chen M, Zhang G, Yan J, Ma Y (2017) Reliability and temperature constrained task scheduling for makespan minimization on heterogeneous multi-core platforms. J Syst Softw 133:1–16

    Article  Google Scholar 

  10. Xie G, Zeng G, Xiao X, Li R, Li K (2017) Energy-efficient scheduling algorithms for real-time parallel applications on heterogeneous distributed embedded systems. IEEE Trans Parallel Distrib Syst 28(12):3426–3442

    Article  Google Scholar 

  11. Tang X, Liao X, Zheng J, Yang X (2018) Energy efficient job scheduling with workload prediction on cloud data center. Clust Comput 21(3):1581–1593

    Article  Google Scholar 

  12. Deng Z, Yan Z, Huang H, Shen H (2020) Energy-aware task scheduling on heterogeneous computing systems with time constraint. IEEE Access 8:23936–23950

    Article  Google Scholar 

  13. 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 

  14. 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 

  15. Tang X, Shi W, Wu F (2020) Interconnection network energy-aware workflow scheduling algorithm on heterogeneous systems. IEEE Trans Ind Inf 16(12):7637–7645

    Article  Google Scholar 

  16. Tang X, Li X, Fu Z (2017) Budget-constraint stochastic task scheduling on heterogeneous cloud systems. Concurr Comput Pract Exp 29(19):e4210

    Article  Google Scholar 

  17. Tang X, Li K, Liao G (2014) An effective reliability-driven technique of allocating tasks on heterogeneous cluster systems. Clust Comput 17(4):1413–1425

    Article  Google Scholar 

  18. Quan Z, Wang Z, Ye T, Guo S (2020) Task scheduling for energy consumption constrained parallel applications on heterogeneous computing systems. IEEE Trans Parallel Distrib Syst 31(5):1165–1182

    Article  Google Scholar 

  19. Muhuri PK, Biswas SK (2020) Bayesian optimization algorithm for multi-objective scheduling of time and precedence constrained tasks in heterogeneous multiprocessor systems. Appl Soft Comput 1–27

  20. Djigal H, Feng J, Lu J, Ge J (2021) IPPTS: an efficient algorithm for scientific workflow scheduling in heterogeneous computing systems. IEEE Trans Parallel Distrib Syst 32(5):1057–1071

    Article  Google Scholar 

  21. Chen J, Du C, Han P, Du X (2019) Work-in-progress: non-preemptive scheduling of periodic tasks with data dependency upon heterogeneous multiprocessor platforms. In: IEEE real-time systems symposium, RTSS 2019, Hong Kong, SAR, China, December 3–6, 2019. IEEE, pp 540–543

  22. Aldegheri S, Bombieri N, Patel HD (2020) On the task mapping and scheduling for dag-based embedded vision applications on heterogeneous multi/many-core architectures. In: 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020, Grenoble, France, Mar 9–13, 2020. IEEE, pp 1003–1006

  23. Hu Y, Li J, He L (2020) A reformed task scheduling algorithm for heterogeneous distributed systems with energy consumption constraints. Neural Comput Appl 32(10):5681–5693

    Article  Google Scholar 

  24. Zhang L, Zhou L, Salah A (2020) Efficient scientific workflow scheduling for deadline-constrained parallel tasks in cloud computing environments. Inf Sci 531:31–46

    Article  MathSciNet  MATH  Google Scholar 

  25. Wen Y, Xu H, Yang J (2011) A heuristic-based hybrid genetic-variable neighborhood search algorithm for task scheduling in heterogeneous multiprocessor system. Inf Sci 181(3):567–581

    Article  Google Scholar 

  26. Omara FA, Arafa MM (2010) Genetic algorithms for task scheduling problem. J Parallel Distrib Comput 70(1):13–22

    Article  MATH  Google Scholar 

  27. Gu Q, Hao X (2018) Adaptive iterative learning control based on particle swarm optimization. J Supercomput 3615–3622

  28. Kansal S, Kumar H, Kaushal S, Sangaiah AK (2020) Genetic algorithm-based cost minimization pricing model for on-demand iaas cloud service. J Supercomput 76(3):1–26

    Article  Google Scholar 

  29. Alazzam H, Alhenawi E, Alsayyed RMH (2019) A hybrid job scheduling algorithm based on tabu and harmony search algorithms. J Supercomput 75(12):7994–8011

    Article  Google Scholar 

  30. Asghari A, Sohrabi MK, Yaghmaee F (2021) Task scheduling, resource provisioning, and load balancing on scientific workflows using parallel SARSA reinforcement learning agents and genetic algorithm. J Supercomput 77(3):2800–2828

    Article  Google Scholar 

  31. Alboaneen DA, Tianfield H, Zhang Y, Pranggono B (2021) A metaheuristic method for joint task scheduling and virtual machine placement in cloud data centers. Future Gener Comput Syst 115:201–212

    Article  Google Scholar 

  32. Deng Z, Shen H, Cao D, Yan Z, Huang H (2021) Task scheduling on heterogeneous multiprocessor systems through coherent data allocation. Concurr Comput Pract Exp 1–19

  33. Hu Y, Liu C, Li K, Chen X, Li K (2017) Slack allocation algorithm for energy minimization in cluster systems. Future Gener Comput Syst 74:119–131

    Article  Google Scholar 

  34. Zhao B, Aydin H, Zhu D (2010) On maximizing reliability of real-time embedded applications under hard energy constraint. IEEE Trans Ind Inf 6(3):316–328

    Article  Google Scholar 

  35. Mirjalili SM, Lewis A (2016) The whale optimization algorithm. Adv Eng Softw 95:51–67

    Article  Google Scholar 

  36. Gharehchopogh FS, Gholizadeh H (2019) A comprehensive survey: whale optimization algorithm and its applications. Swarm Evolut Comput 48:1–24

    Article  Google Scholar 

  37. Chu D, Chen H, Wang X (2019) Whale optimization algorithm based on adaptive weight and simulated annealing. Acta Electr Sin 47(5):992–999

    Google Scholar 

  38. Abdel-Basset M, El-Shahat D, Deb K, Abouhawwash M (2020) Energy-aware whale optimization algorithm for real-time task scheduling in multiprocessor systems. Appl Soft Comput 93:106349

    Article  Google Scholar 

  39. Li K, Tang X, Li K (2013) Energy-efficient stochastic task scheduling on heterogeneous computing systems. IEEE Trans Parallel Distrib Syst 25(11):2867–2876

    Article  Google Scholar 

  40. Tang X, Fu Z (2020) CPU-GPU utilization aware energy-efficient scheduling algorithm on heterogeneous computing systems. IEEE Access 8:58948–58958

    Article  Google Scholar 

  41. Paul S, Chatterjee N, Ghosal P, Diguet J (2021) Adaptive task allocation and scheduling on noc-based multicore platforms with multitasking processors. ACM Trans Embed Comput Syst 20(1):4:1-4:26

    Article  Google Scholar 

  42. Salami B, Noori H, Naghibzadeh M (2021) Fairness-aware energy efficient scheduling on heterogeneous multi-core processors. IEEE Trans Comput 70(1):72–82

    Article  MATH  Google Scholar 

  43. Goubaa A, Khalgui M, Li Z, Frey G, Zhou M (2020) Scheduling periodic and aperiodic tasks with time, energy harvesting and precedence constraints on multi-core systems. Inf Sci 520:86–104

    Article  MathSciNet  MATH  Google Scholar 

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

  45. Li K (2008) Performance analysis of power-aware task scheduling algorithms on multiprocessor computers with dynamic voltage and speed. IEEE Trans Parallel Distrib Syst 19(11):1484–1497

    Article  Google Scholar 

  46. Li K (2016) Energy and time constrained task scheduling on multiprocessor computers with discrete speed levels. J Parallel Distrib Comput 95:15–28

    Article  Google Scholar 

  47. Nesmachnow S, Dorronsoro B, Pecero JE, Bouvry P (2013) Energy-aware scheduling on multicore heterogeneous grid computing systems. J Grid Comput 11(4):653–680

    Article  Google Scholar 

  48. 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 

  49. Niu J, Liu C, Gao Y, Qiu M (2014) Energy efficient task assignment with guaranteed probability satisfying timing constraints for embedded systems. IEEE Trans Parallel Distrib Syst 25(8):2043–2052

    Article  Google Scholar 

  50. Li D, Wu J (2015) Minimizing energy consumption for frame-based tasks on heterogeneous multiprocessor platforms. IEEE Trans Parallel Distrib Syst 26(3):810–823

    Article  Google Scholar 

  51. Mezmaz M, Melab N, Kessaci Y, Lee YC, Talbi E-G, Zomaya AY, Tuyttens D (2011) A parallel bi-objective hybrid metaheuristic for energy-aware scheduling for cloud computing systems. J Parallel Distrib Comput 71(11):1497–1508

    Article  Google Scholar 

  52. Mashayekhy L, Nejad MM, Grosu D, Zhang Q, Shi W (2015) Energy-aware scheduling of mapreduce jobs for big data applications. IEEE Trans Parallel Distrib Syst 1:1

    Google Scholar 

  53. Zhang Y, Wang Y, Tang X, Yuan X, Xu Y (2018) Energy-efficient task scheduling on heterogeneous computing systems by linear programming. Concurr Comput Pract Exp 30(19):e4731

    Article  Google Scholar 

  54. Thammawichai M, Kerrigan EC (2018) Energy-efficient real-time scheduling for two-type heterogeneous multiprocessors. Real Time Syst 54(1):132–165

    Article  MATH  Google Scholar 

  55. 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 

  56. Li K (2012) Energy efficient scheduling of parallel tasks on multiprocessor computers. J Supercomput 60(2):223–247

    Article  Google Scholar 

  57. Xie G, Zeng G, Chen Y, Bai Y, Zhou Z, Li R, Li K (2020) Minimizing redundancy to satisfy reliability requirement for a parallel application on heterogeneous service-oriented systems. IEEE Trans Serv Comput 13(5):871–886

    Article  Google Scholar 

  58. Girault A, Kalla H (2009) A novel bicriteria scheduling heuristics providing a guaranteed global system failure rate. IEEE Trans Dependable Secure Comput 6(4):241–254

    Article  Google Scholar 

  59. Dongarra JJ, Jeannot E, Saule E, Shi Z (2007) Bi-objective scheduling algorithms for optimizing makespan and reliability on heterogeneous systems. In: Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures. ACM, pp 280–288

  60. Chen C-Y (2015) Task scheduling for maximizing performance and reliability considering fault recovery in heterogeneous distributed systems. IEEE Trans Parallel Distrib Syst 27(2):521–532

    Article  Google Scholar 

  61. Wang S, Li K, Mei J, Xiao G, Li K (2017) A reliability-aware task scheduling algorithm based on replication on heterogeneous computing systems. J Grid Comput 15(1):23–39

    Article  Google Scholar 

  62. Jeannot E, Saule E, Trystram D (2012) Optimizing performance and reliability on heterogeneous parallel systems: approximation algorithms and heuristics. J Parallel Distribut Comput 72(2):268–280

    Article  MATH  Google Scholar 

  63. Li R, Yu H, Jiang W, Ha Y (2020) Dvfs-based scrubbing scheduling for reliability maximization on parallel tasks in sram-based fpgas. In: 57th ACM/IEEE Design Automation Conference, DAC 2020, San Francisco, CA, USA, July 20–24, 2020. IEEE, pp 1–6

  64. Zhang L, Li K, Li K, Xu Y (2016) Joint optimization of energy efficiency and system reliability for precedence constrained tasks in heterogeneous systems. Int J Electr Power Energy Syst 78:499–512

    Article  Google Scholar 

  65. Zhu D, Aydin H (2009) Reliability-aware energy management for periodic real-time tasks. IEEE Trans Comput 58(10):1382–1397

    Article  MathSciNet  MATH  Google Scholar 

  66. 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 Electr Syst (TODAES) 18(2):23

    Google Scholar 

  67. Zhang L, Li K, Zheng W, Li K (2018) Contention-aware reliability efficient scheduling on heterogeneous computing systems. IEEE Trans Sustain Comput 3(3):182–194

    Article  Google Scholar 

  68. Kumar N, Mayank J, Mondal A (2020) Reliability aware energy optimized scheduling of non-preemptive periodic real-time tasks on heterogeneous multiprocessor system. IEEE Trans Parallel Distrib Syst 31(4):871–885

    Article  Google Scholar 

  69. Huang J, Li R, Jiao X, Jiang Y, Chang W (2020) Dynamic dag scheduling on multiprocessor systems: reliability, energy, and makespan. IEEE Trans Comput Aided Des Integr Circuits Syst 39(11):3336–3347

    Article  Google Scholar 

  70. Hassan HA, Salem SA, Saad EM (2020) A smart energy and reliability aware scheduling algorithm for workflow execution in dvfs-enabled cloud environment. Future Gener Comput Syst 112:431–448

    Article  Google Scholar 

  71. Abdi A, Girault A, Zarandi HR (2019) ERPOT: a quad-criteria scheduling heuristic to optimize execution time, reliability, power consumption and temperature in multicores. IEEE Trans Parallel Distrib Syst 30(10):2193–2210

    Article  Google Scholar 

  72. Deng W, Xu J, Zhao H (2019) An improved ant colony optimization algorithm based on hybrid strategies for scheduling problem. IEEE Access 7:20281–20292

    Article  Google Scholar 

  73. Song S, Wang P, Heidari AA, Wang M, Zhao X, Chen H, He W, Xu S (2021) Dimension decided harris hawks optimization with gaussian mutation: balance analysis and diversity patterns. Knowl Based Syst 215:106425

    Article  Google Scholar 

  74. 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 

  75. Xu Y, Li K, He L, Zhang L, Li K (2015) A hybrid chemical reaction optimization scheme for task scheduling on heterogeneous computing systems. IEEE Trans Parallel Distrib Syst 26(12):3208–3222

    Article  Google Scholar 

  76. Manudhane KA, Wadhe A (2013) Comparative study of static task scheduling algorithms for heterogeneous systems. Int J Comput Sci Eng 5(3):166

    Google Scholar 

  77. Veeravalli B, Li X, Ko CC (2000) On the influence of start-up costs in scheduling divisible loads on bus networks. IEEE Trans Parallel Distrib Syst 11(12):1288–1305

    Article  Google Scholar 

  78. Mingsheng S (2008) Optimal algorithm for scheduling large divisible workload on heterogeneous system. Appl Math Model 32(9):1682–1695

    Article  MathSciNet  MATH  Google Scholar 

  79. Zhu D, Melhem RG, Mossé D (2004) The effects of energy management on reliability in real-time embedded systems. In: 2004 International Conference on Computer-Aided Design, ICCAD 2004, San Jose, CA, USA, Nov 7–11, 2004. IEEE Computer Society/ACM, pp 35–40

  80. Izosimov V, Pop P, Eles P, Peng Z (2005) Design optimization of time-and cost-constrained fault-tolerant distributed embedded systems. In: 2005 Design, Automation and Test in Europe Conference and Exposition (DATE 2005), 7-11 Mar 05, Munich, Germany. IEEE Computer Society, pp 864–869

  81. Wang S, Li K, Mei J, Xiao G, Li K (2017) A reliability-aware task scheduling algorithm based on replication on heterogeneous computing systems. Grid Comput 15(1):23–39

    Article  Google Scholar 

  82. Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence, vol 1. pp 695–701

  83. Choi TJ, Togelius J, Cheong Y (2021) A fast and efficient stochastic opposition-based learning for differential evolution in numerical optimization. Swarm Evolut Comput 60:100768

    Article  Google Scholar 

  84. Xu Y, Yang Z, Li X, Kang H, Yang X (2020) Dynamic opposite learning enhanced teaching-learning-based optimization. Knowl Based Syst 188:104966

    Article  Google Scholar 

  85. Seif Z, Ahmadi MB (2015) An opposition-based algorithm for function optimization. Eng Appl Artif Intell 37:293–306

    Article  Google Scholar 

  86. Kaur P, Mehta S (2017) Resource provisioning and work flow scheduling in clouds using augmented shuffled frog leaping algorithm. J Parallel Distrib Comput 101:41–50

    Article  Google Scholar 

  87. Qiu X, Hu Y, Li B (2016) Multiprocessor task scheduling based on improved differential evolution algorithm. Control Decis 31(2):217–224

    MATH  Google Scholar 

  88. Topcuoglu H, Hariri S, Wu M-Y (2002) Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distrib Syst 13(3):260–274

    Article  Google Scholar 

  89. Cheng L, Xing YJ, Ren MF, Xie G, Chen J (2018) Multipath estimation algorithm using \(\epsilon\) constrained rank-based differential evolution. Acta Electr Sin 46(1):167–174

    Google Scholar 

  90. Takahama T, Sakai S (2012) Efficient constrained optimization by the \(\epsilon\) constrained rank-based differential evolution. In: Proceedings of the IEEE congress on evolutionary computation, CEC 2012, Brisbane, Australia, June 10–15, 2012. IEEE, pp 1–8

  91. 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 

  92. Akram A, Sawalha L (2019) Validation of the gem5 simulator for x86 architectures. In: 2019 IEEE/ACM performance modeling, benchmarking and simulation of high performance computer systems, PMBS@SC 2019, Denver, CO, USA, Nov 18, 2019. IEEE, pp 53–58

Download references

Acknowledgements

Supported by Key-Area Research and Development Plan of Guangdong Province #2020B010164003 and National Key R&D Program of China Project #2017YFB0203201.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hong Shen.

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

Deng, Z., Cao, D., Shen, H. et al. Reliability-aware task scheduling for energy efficiency on heterogeneous multiprocessor systems. J Supercomput 77, 11643–11681 (2021). https://doi.org/10.1007/s11227-021-03764-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-021-03764-x

Keywords

Navigation