【摘要】 随着数据中心规模的不断扩大，所引起的高能耗、高运营成本和环境污染等问题日益严重，严重影响了数据中心的可持续性。大多数数据中心能耗优化方法为了降低计算能耗，会将任务集中在尽可能少的服务器上，但这样做往往会导致数据中心热点的产生，并且提高了冷却能耗。为了解决这一问题，文中首先对数据中心进行建模，并将数据中心总能耗优化问题建模为一个任务调度问题，并且要求调度过程中不产生数据中心热点。为了解决该问题，文中提出了一种基于深度强化学习的数据中心任务调度方法，并使用奖励塑造对该方法进行优化，在不产生热点的前提下降低数据中心的总能耗。最后，通过仿真环境和真实数据中心负载跟踪数据进行了实验。仿真实验结果表明，所提方法相比其他现有的调度方法能够更好地降低数据中心总能耗，最多降低了25.5%。此外，提出的优化方法还不会产生热点，这进一步证明了其优越性。更多还原

【Abstract】 With the continuous expansion of the scale of data centers, the problems of high energy consumption, high operating costs and environmental pollution are becoming more and more serious, which seriously affect the sustainability of data centers.Most data center energy consumption optimization methods focus tasks on as few servers as possible, so as to reduce computing energy consumption.However, this often leads to the generation of data center hotspots and increases cooling energy consumption.In order to solve this problem, this paper first models the data center, and models the total energy consumption optimization problem of the data center as a task scheduling problem, and requires that no data center hotspots are generated.This paper proposes a task scheduling method based on deep reinforcement learning for data centers, and uses reward shaping to optimize the method to reduce the total energy consumption of data centers without generating hotspots.Finally, experiments are carried out through simulation environment and real data center load trace data.The simulation results show that the proposed method can reduce the total energy consumption of the data center better than other existing scheduling methods, and can reduce the total energy consumption by up to 25.5%.In addition, the proposed optimization method does not generate hot spots yet, which further proves its superiority.更多还原