基于强化学习的温度感知多核任务调度

随着计算机中内核数量的增多，温度感知的多核任务调度算法成为计算机系统中的一个研究热点.近几年机器学习在各个领域展现出巨大的潜力，很多基于机器学习的系统温度管理研究工作应运而生.其中强化学习因其较强的自适应性，被广泛的运用于温度感知的任务调度算法中.然而目前基于强化学习的温度感知任务调度算法系统建模不够准确，很难做到温度、性能和复杂度的较好权衡.因此，本文提出一种新的基于强化学习的多核温度感知调度算法-ReLeTA，在新的算法中提出了更全面的状态建模方式和更加有效的奖励函数，从而帮助系统进一步降低温度.实验部分通过三个不同的真实计算机平台验证所提方法，实验结果表明了本文所提出方法的有效性以及可扩展性，相比现有方法ReLeTA可以更好的控制系统温度.

Temperature-aware Task Scheduling on Multicores Based on Reinforcement Learning

With the increase of the number of cores in computers, temperature-aware multi-core task scheduling algorithms have become a research hotspot in computer systems. In recent years, machine learning has shown great potential in various fields, and thus many work using machine learning techniques to manage system temperature have emerged. Among them, reinforcement learning is widely used for temperature-aware task scheduling algorithms due to its strong adaptability. However, the state-of-the-art temperature-aware task scheduling algorithms based on reinforcement learning do not effectively model the system, and it is difficult to achieve a good trade-off between temperature, performance and complexity. Therefore, this paper proposes a new multi-core temperature-aware scheduling algorithm based on reinforcement learning-ReLeTA. In the new algorithm, a more comprehensive state modeling method and a more effective reward function are proposed to help the system further reduce the temperature. We conduct experiments on three different real computer platforms. The experimental results show the effectiveness and scalability of the proposed method. Compared with existing methods, ReLeTA can control the system temperature better.