Online job scheduling scheme for low-carbon data center operation: An information and energy nexus perspective.

• A job scheduling model for low-carbon data center operation is established. • Practical challenges such as job dependency and heterogeneity are considered. • A reinforcement learning algorithm is proposed to obtain online scheduling policy. • The effectiveness of proposed scheme is verified with real-world scenarios. 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. [ABSTRACT FROM AUTHOR]