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Smart DAG Tasks Scheduling between Trusted and Untrusted Entities Using the MCTS Method

Author

Listed:
  • Yuxia Cheng

    (School of Computer Science and Technology, Hangzhou Dianzi University, 1158 Baiyang No. 2 Street, Hangzhou 310018, China)

  • Zhiwei Wu

    (School of Computer Science and Technology, Hangzhou Dianzi University, 1158 Baiyang No. 2 Street, Hangzhou 310018, China)

  • Kui Liu

    (School of Computer Science and Technology, Hangzhou Dianzi University, 1158 Baiyang No. 2 Street, Hangzhou 310018, China)

  • Qing Wu

    (School of Computer Science and Technology, Hangzhou Dianzi University, 1158 Baiyang No. 2 Street, Hangzhou 310018, China)

  • Yu Wang

    (School of Computer Science, Guangzhou University, 230 Wai Huan Xi Road, Guangzhou Higher Education Mega Center, Guangzhou 510006, China)

Abstract

Task scheduling is critical for improving system performance in the distributed heterogeneous computing environment. The Directed Acyclic Graph (DAG) tasks scheduling problem is NP-complete and it is hard to find an optimal schedule. Due to its key importance, the DAG tasks scheduling problem has been extensively studied in the literature. However, many previously proposed traditional heuristic algorithms are usually based on greedy methods and also lack the consideration of scheduling tasks between trusted and untrusted entities, which makes the problem more complicated, but there still exists a large optimization space to be explored. In this paper, we propose a trust-aware adaptive DAG tasks scheduling algorithm using the reinforcement learning and Monte Carlo Tree Search (MCTS) methods. The scheduling problem is defined using the reinforcement learning model. Efficient scheduling state space, action space and reward function are designed to train the policy gradient-based REINFORCE agent. The MCTS method is proposed to determine actual scheduling policies when DAG tasks are simultaneously executed in trusted and untrusted entities. Leveraging the algorithm’s capability of exploring long term reward, the proposed algorithm could achieve good scheduling policies while guaranteeing trusted tasks scheduled within trusted entities. Experimental results showed the effectiveness of the proposed algorithm compared with the classic HEFT/CPOP algorithms.

Suggested Citation

  • Yuxia Cheng & Zhiwei Wu & Kui Liu & Qing Wu & Yu Wang, 2019. "Smart DAG Tasks Scheduling between Trusted and Untrusted Entities Using the MCTS Method," Sustainability, MDPI, vol. 11(7), pages 1-16, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:7:p:1826-:d:217419
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    References listed on IDEAS

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    1. David Silver & Julian Schrittwieser & Karen Simonyan & Ioannis Antonoglou & Aja Huang & Arthur Guez & Thomas Hubert & Lucas Baker & Matthew Lai & Adrian Bolton & Yutian Chen & Timothy Lillicrap & Fan , 2017. "Mastering the game of Go without human knowledge," Nature, Nature, vol. 550(7676), pages 354-359, October.
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    Cited by:

    1. M. Saqlain & S. Ali & J. Y. Lee, 2023. "A Monte-Carlo tree search algorithm for the flexible job-shop scheduling in manufacturing systems," Flexible Services and Manufacturing Journal, Springer, vol. 35(2), pages 548-571, June.

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