论文译读 —— STUN: Reinforcement-Learning-Based Optimization of Kernel Scheduler Parameters 6

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6. 结论

CPU调度器是影响系统性能的一个重要因素。在本文中，我们提出了STUN——一个使用强化学习的Linux内核调度程序参数优化框架。STUN的使用可以增强各种调度器环境，而无需人工干预。

STUN具有高效和快速优化的特点。首先，通过有效优化筛过程所选出的参数对性能有显著影响。此外，我们细分了奖励算法，以便用于强化学习中的智能体能够有效地学习。

性能评估证实，与Linux的默认参数相比，Hackbench的性能提高了27.7%。对于实际工作负载，人脸检测应用程序显示执行时间减少了18.3%，并且每秒帧数增加了22.4%。通过优化运行于4核、44核和120核上的Sysbench，与机器的默认性能相比，性能分别提高了26.97%、54.43%和256.13%。

在今后研究中，我们计划采用另一种强化学习算法，诸如Asynchronous Advantage Actor-Critic (A3C)或policy gradient，以获得更精确的优化参数值和更多的并发参数。此外，我们还要将STUN的逻辑与Linux内核集成，以制作一个自适应的调度器。

Author Contributions: Author Contributions: Conceptualization, H.L. and H.J.; software, H.L.;

experiments, H.L. and S.J.; writing—original draft preparation, H.L. and H.J.; writing—review and

editing, H.L., S.J. and H.J.; supervision, H.J.; project administration, H.J. All authors have read and

agreed to the published version of the manuscript.

Funding: This work was supported by the National Research Foundation of Korea(NRF) grant

funded by the Korea government(MSIT) (No. 2020R1F1A1075941).

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Not applicable.

Data Availability Statement: The data presented in this study are available on request from the

fifirst author.

Conflflicts of Interest: The authors declare no conflflict of interest.

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论文译读 —— STUN: Reinforcement-Learning-Based Optimization of Kernel Scheduler Parameters 6

6. 结论

参考

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