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

Wenyu Liu, Yuejun Yan, Yimeng Sun, Hongju Mao, Ming Cheng, Peng Wang and Zhaohao Ding

Applied Energy, 2023, vol. 338, issue C, No S0306261923002829

Abstract: 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.

Keywords: Data center; Job scheduling; Demand-side resource; Deep reinforcement learning; Low-carbon operation (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.apenergy.2023.120918

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