As the digitalization of the economy and society accelerates, the enormous and fast-growing energy consumption of data centers is becoming a global concern. With the unique power consumption flexibility introduced by computing job scheduling, data centers could play an important role in enhancing the capability to integrate renewable generation as a demand-side resource. In this paper, we propose an online job scheduling scheme for low-carbon data center operation from an information and energy nexus perspective. We formulate the job scheduling problem as a Markov decision process in which job dependencies, job heterogeneity, and quality of service are considered comprehensively. To address the challenges of large-scale heterogeneous computing jobs, we propose a deep reinforcement learning-based approach to solve the energy-aware scheduling problem and achieve an optimal online policy. The case study results based on real-world data illustrate that the proposed scheme can effectively reduce the carbon footprint and energy cost of a data center while maintaining the quality of service for cloud products.

随着经济社会数字化进程的加快，数据中心巨大且快速增长的能源消耗正成为全球关注的问题。凭借计算作业调度引入的独特的功耗灵活性，数据中心可以在增强将可再生能源发电整合为需求侧资源的能力方面发挥重要作用。在本文中，我们从信息和能源关系的角度提出了一种用于低碳数据中心运营的在线作业调度方案。我们将作业调度问题表述为马尔可夫决策过程，其中综合考虑了作业依赖性、作业异质性和服务质量。为了应对大规模异构计算作业的挑战，我们提出了一种基于深度强化学习的方法来解决能量感知调度问题并实现最优在线策略。基于真实数据的案例研究结果表明，所提出的方案可以有效降低数据中心的碳足迹和能源成本，同时保持云产品的服务质量。