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Optimization of uncertain dependent task mapping on heterogeneous computing platforms

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Abstract

Dependent tasks are typically modeled using directed acyclic graphs (DAGs), and scheduling algorithms based on DAGs have been extensively researched. Most of the existing algorithms assume that the task or communication duration is deterministic. Nevertheless, any delays in task execution or communication can significantly affect the scheduling results. Aiming at minimizing the DAGs’ makespan, a heuristic algorithms called heterogeneous optimistic complete time (HOCT) is proposed. The algorithm assumes that the task characteristic values are modeled randomly. It calculates task priorities based on the acceleration ratio and allocates computing resources using an optimistic execution timetable. Then, a Monte-Carlo simulation-based scheduling algorithm which built on the top of HOCT is proposed. Experimental results show that the proposed algorithm achieves better makespan of the stochastic DAG. It also provides a more robust scheduling solution to unpredictability than critical-path-on-a-processor, heterogeneous earliest finish time-no cross and parental prioritization earliest finish time algorithms.

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Data availability

The datasets generated are available from the corresponding author on reasonable request.

References

  1. Kim KD, Kumar PR (2012) Cyber-physical systems: a perspective at the centennial. Proc IEEE 100:1287–1308. https://doi.org/10.1109/JPROC.2012.2189792

    Article  Google Scholar 

  2. Han Z, Qu G, Liu B et al (2022) Exploit the data level parallelism and schedule dependent tasks on the multi-core processors. Inf Sci 585:382–394. https://doi.org/10.1016/j.ins.2021.10.072

    Article  Google Scholar 

  3. Lumpp F, Aldegheri S, Patel HD et al (2021) Task mapping and scheduling for openVX applications on heterogeneous multi/many-core architectures. IEEE Trans Comput 70(8):1148–1159. https://doi.org/10.1109/TC.2021.3059528

    Article  Google Scholar 

  4. Singh AK, Shafique M, Kumar A et al (2013) Mapping on multi many-core systems survey of current. In: 2013 50th ACM/EDAC/IEEE Design Automation Conference (DAC), IEEE, Austin, TX, USA, pp. 1–10. https://doi.org/10.1145/2463209.2488734

  5. Liu J, Li K, Zhu D et al (2017) Minimizing cost of scheduling tasks on heterogeneous multicore embedded systems. ACM Trans Embed Comput Syst 16(2):1–25. https://doi.org/10.1145/2935749

    Article  Google Scholar 

  6. Jarvis SA, He L, Spooner DP et al (2005) The impact of predictive inaccuracies on execution scheduling. Perform Eval 60(1–4):127–139. https://doi.org/10.1016/j.peva.2004.10.015

    Article  Google Scholar 

  7. Mollajafari M (2023) An efficient lightweight algorithm for scheduling tasks onto dynamically reconfigurable hardware using graph-oriented simulated annealing. Neural Comput Appl 35(24):18035–18057. https://doi.org/10.1007/s00521-023-08682-y

    Article  Google Scholar 

  8. Momtazpour M, Assare O, Rahmati N et al (2015) Yield-driven design-time task scheduling techniques for multi-processor system on chips under process variation: a comparative study. IET Comput Digit Tech 9(4):221–229. https://doi.org/10.1049/iet-cdt.2014.0126

    Article  Google Scholar 

  9. Nathan G, Olivier B, Emmanuel J et al (2021) READYS: A reinforcement learning based strategy for heterogeneous dynamic scheduling. In: 2021 IEEE International Conference on Cluster Computing (Cluster), Portland, OR, USA, pp. 70–81. https://doi.org/10.1109/Cluster48925.2021.00031

  10. Cheng Y, Cao Z, Zhang X et al (2023) Multi objective dynamic task scheduling optimization algorithm based on deep reinforcement learning. J Supercomput. https://doi.org/10.1007/s11227-023-05714-1

    Article  Google Scholar 

  11. Emeretlis A, Theodoridis G, Alefragis P et al (2018) Static mapping of applications on heterogeneous multi-core platforms combining logic-based benders decomposition with integer linear programming. ACM Trans Des Autom Electron Syst 23(2):1–24. https://doi.org/10.1145/3133219

    Article  Google Scholar 

  12. Pautet L, Robert T, Tardieu S (2021) Litmus-RT plugins for global static scheduling of mixed criticality systems. J Syst Archit 118:102221–102232. https://doi.org/10.1016/j.sysarc.2021.102221

    Article  Google Scholar 

  13. Jiang X, Sha T, Liu D et al (2022) Flexible and dynamic scheduling of mixed-criticality systems. Sensors 22(19):7528. https://doi.org/10.3390/s22197528

    Article  Google Scholar 

  14. Alsheikhy A, Ammar R, Elfouly R et al (2016) An efficient dynamic scheduling algorithm for periodic tasks in real-time systems using dynamic average estimation. In: 2016 IEEE Symposium on Computers and Communication (ISCC), Messina, Italy, pp. 773–777. https://doi.org/10.1109/ISCC.2016.7543830

  15. Choudhury P, Chakrabarti PP, Kumar R (2012) Online scheduling of dynamic task graphs with communication and contention for multiprocessors. IEEE Trans Parallel Distrib Syst 23(1):126–133. https://doi.org/10.1109/TPDS.2011.104

    Article  Google Scholar 

  16. Topcuoglu H, Hariri S, Wu MY (2002) Performance-effective and low-complexity task scheduling for heterogeneous computing. IEEE Trans Parallel Distrib Syst 13(3):260–274. https://doi.org/10.1109/71.993206

    Article  Google Scholar 

  17. Shetti KR, Fahmy SA, Bretschneider T (2013) Optimization of the HEFT algorithm for a CPU-GPU environment. In: 2013 International Conference on Parallel and Distributed Computing, Applications and Technologies, pp. 212–218. https://doi.org/10.1109/pdcat.2013.40

  18. Arif MS, Iqbal Z, Tariq R et al (2019) Parental prioritization-based task scheduling in heterogeneous systems. Arab J Sci Eng 44(4):3943–3952. https://doi.org/10.1007/s13369-018-03698-2

    Article  Google Scholar 

  19. Emeretlis A, Theodoridis G, Alefragis P et al (2014) A hybrid ilp-cp model for mapping directed acyclic task graphs to multicore architectures. In: Proceedings of 2014 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 176–182. https://doi.org/10.1109/IPDPSW.2014.24

  20. Shi Z, Zhao T, Li Q et al (2023) Workflow migration in uncertain edge computing environments based on interval many-objective evolutionary algorithm. Egypt Inform J 24(4):100418–100431. https://doi.org/10.1016/j.eij.2023.100418

    Article  Google Scholar 

  21. Mollajafari M, Shojaeefard MH (2021) TC3PoP: a time-cost compromised workflow scheduling heuristic customized for cloud environments. Clust Comput 24(3):2639–2656. https://doi.org/10.1007/s10586-021-03285-5

    Article  Google Scholar 

  22. Ferrandi F, Lanzi PL, Pilato C et al (2010) Ant colony heuristic for mapping and scheduling tasks and communications on heterogeneous embedded systems. IEEE Trans Comput Aided Des Integr Circuits Syst 29(6):911–924. https://doi.org/10.1109/TCAD.2010.2048354

    Article  Google Scholar 

  23. Prongnuch S, Sitjongsataporn S, Wiangtong T (2020) A heuristic approach for scheduling in heterogeneous distributed embedded systems. Int J Intell Eng Syst 13(1):135–145. https://doi.org/10.22266/ijies2020.0229.13

    Article  Google Scholar 

  24. Mohtavipour SM, Shahhoseini HS (2019) A link-elimination partitioning approach for application graph mapping in reconfigurable computing systems. J Supercomput 76(1):726–754. https://doi.org/10.1007/s11227-019-03056-5

    Article  Google Scholar 

  25. Mohtavipour SM, Shahhoseini HS (2022) An analytically derived vectorized model for application graph mapping in interconnection networks. J Ambient Intell Humaniz Comput 14(7):8899–8911. https://doi.org/10.1007/s12652-021-03637-4

    Article  Google Scholar 

  26. Li K, Tang X, Veeravalli B et al (2015) Scheduling precedence constrained stochastic tasks on heterogeneous cluster systems. IEEE Trans Comput 64(1):191–204. https://doi.org/10.1109/tc.2013.205

    Article  MathSciNet  Google Scholar 

  27. Shreya A, Zhihui Z, Marc G et al (2014) Robustness analysis of multiprocessor schedules. In: 2014 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS XIV), Agios Konstantinos, Greece, pp. 9–17. https://doi.org/10.1109/SAMOS.2014.6893189

  28. Zhang J, Chen C, Zheng HK et al (2019) A high priority random task fuzzy scheduling algorithm for CPS. In: 2019 Chinese Control And Decision Conference (CCDC), Nanchang, China, pp. 482–487. https://doi.org/10.1109/CCDC.2019.8832879

  29. Muhuri PK, Nath R, Shukla AK (2021) Energy efficient task scheduling for real-time embedded systems in a fuzzy uncertain environment. IEEE Trans Fuzzy Syst 29(5):1037–1051. https://doi.org/10.1109/TFUZZ.2020.2968864

    Article  Google Scholar 

  30. Tang X, Li K, Liao G et al (2011) A stochastic scheduling algorithm for precedence constrained tasks on Grid. Future Gener Comput Syst 27(8):1083–1091. https://doi.org/10.1016/j.future.2011.04.007

    Article  Google Scholar 

  31. Huang K, Wang K, Zheng D et al (2021) Expected energy optimization for real-time multiprocessor SoCs running periodic tasks with uncertain execution time. IEEE Trans Sustain Comput 6(3):398–411. https://doi.org/10.1109/TSUSC.2018.2853621

    Article  Google Scholar 

  32. Canon LC, Jeannot E (2010) Evaluation and optimization of the robustness of DAG schedules in heterogeneous environments. IEEE Trans Parallel Distrib Syst 21(4):532–546. https://doi.org/10.1109/TPDS.2009.84

    Article  Google Scholar 

  33. Sajid M, Raza Z (2017) Energy-aware stochastic scheduler for batch of precedence-constrained jobs on heterogeneous computing system. Energy 125:258–274. https://doi.org/10.1016/j.energy.2017.02.069

    Article  Google Scholar 

  34. Young BD, Pasricha S, Maciejewski AA et al (2013) Heterogeneous energy and makespan constrained DAG scheduling. In: Proceedings of the 2013 Workshop on Energy Efficient High Performance Parallel and Distributed Computing, pp. 3–12. https://doi.org/10.1145/2480347.2480348

  35. Raji M, Nikseresht M (2022) UMOTS: an uncertainty-aware multi-objective genetic algorithm-based static task scheduling for heterogeneous embedded systems. J Supercomput 78(1):279–314. https://doi.org/10.1007/s11227-021-03887-1

    Article  Google Scholar 

  36. McSweeney T, Walton N, Zounon M (2020) An efficient new static scheduling heuristic for accelerated architectures. In: Computational Science – ICCS 2020, Springer International Publishing, Cham, pp. 3–16. https://doi.org/10.1007/978-3-030-50371-0_1

  37. Arabnejad H, Barbosa JG (2014) List scheduling algorithm for heterogeneous systems by an optimistic cost table. IEEE Trans Parallel Distrib Syst 25(3):682–694. https://doi.org/10.1109/tpds.2013.57

    Article  Google Scholar 

  38. Mack J, Arda SE, Ogras UY et al (2022) Performant, multi-objective scheduling of highly interleaved task graphs on heterogeneous system on chip devices. IEEE Trans Parallel Distrib Syst 33(9):2148–2162. https://doi.org/10.1109/tpds.2021.3135876

    Article  Google Scholar 

  39. Braun TD, Siegel HJ, Beck N et al (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. https://doi.org/10.1006/jpdc.2000.1714

    Article  Google Scholar 

  40. Zheng W, Sakellariou R (2013) Stochastic DAG scheduling using a Monte Carlo approach. J Parallel Distrib Comput 73(12):1673–1689. https://doi.org/10.1016/j.jpdc.2013.07.019

    Article  Google Scholar 

  41. Ling-xia W, Hong Z (2015) Research on task scheduling problem based on immune genetic algorithm under the cloud environment. Autom Instrum 3:114–116. https://doi.org/10.14016/j.cnki.1001-9227.2015.03.114

    Article  Google Scholar 

  42. Tobita T, Kasahara H (2002) A standard task graph set for fair evaluation of multiprocessor scheduling algorithms. J Sched 5(5):379–394. https://doi.org/10.1002/jos.116

    Article  MathSciNet  Google Scholar 

  43. Emmanuel A, Olivier B, Lionel ED et al (2016) Are static schedules so bad? A case study on cholesky factorization. In: 2016 IEEE International Parallel and Distributed Processing Symposium (IPDPS), IEEE, Chicago, IL, USA, pp. 1021–1030. https://doi.org/10.1109/IPDPS.2016.90

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Funding

This work was supported by Yunnan Fundamental Research Key Projects (202101AS070016); Yunnan Province Science and Technology Major Project (202302AD080002); Yunnan Province Key Laboratory of Computer Technology Application Open Fund (CB22144S073A); Yunnan Province “Xingdian Talents Support Plan” Industrial Innovation Talents Project (Yfgr [2019] No. 1096).

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Authors and Affiliations

  1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500, China

    Jing Zhang & Zhanwei Han

  2. Yunnan Xiaorun Technology Service Co., Kunming, 650500, China

    Jing Zhang

  3. Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, 650500, China

    Jing Zhang & Zhanwei Han

  4. Yunnan Key Laboratory of Computer Technology Application, Kunming University of Science and Technology, Kunming, 650500, China

    Jing Zhang & Zhanwei Han

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  1. Jing Zhang
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Zhang, J., Han, Z. Optimization of uncertain dependent task mapping on heterogeneous computing platforms. J Supercomput (2024). https://doi.org/10.1007/s11227-024-06032-w

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