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Data-driven flexibility assessment for internet data center towards periodic batch workloads

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  • Cao, Yujie
  • Cheng, Ming
  • Zhang, Sufang
  • Mao, Hongju
  • Wang, Peng
  • Li, Chao
  • Feng, Yihui
  • Ding, Zhaohao

Abstract

Considering its unique operational and power consumption characteristics, internet data center (IDC) has been intensively investigated as a promising candidate to provide flexibility for electric power system. In this paper, a data-driven flexibility assessment scheme for IDC is proposed by investigating the temporal shifting capability of periodic batch workloads, which are the major flexibility source in the workload scheduling and execution process. We develop a four-step assessment procedure by identifying the periodic jobs, extracting key operational patterns, mapping the power consumption with workload execution, and quantifying the flexibility associated with power system operation, all of which are established in a data-driven manner. In addition, we adopt real-world production workload trace to verify and demonstrate the effectiveness of the proposed flexibility assessment scheme.

Suggested Citation

  • Cao, Yujie & Cheng, Ming & Zhang, Sufang & Mao, Hongju & Wang, Peng & Li, Chao & Feng, Yihui & Ding, Zhaohao, 2022. "Data-driven flexibility assessment for internet data center towards periodic batch workloads," Applied Energy, Elsevier, vol. 324(C).
    • Handle: RePEc:eee:appene:v:324:y:2022:i:c:s0306261922009631
    DOI: 10.1016/j.apenergy.2022.119665
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    References listed on IDEAS

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    Cited by:

    1. Liu, Wenyu & Yan, Yuejun & Sun, Yimeng & Mao, Hongju & Cheng, Ming & Wang, Peng & Ding, Zhaohao, 2023. "Online job scheduling scheme for low-carbon data center operation: An information and energy nexus perspective," Applied Energy, Elsevier, vol. 338(C).
    2. Cao, Yujie & Zhang, Sufang, 2023. "Facilitating the provision of load flexibility to the power system by data centers: A hybrid research method applied to China," Utilities Policy, Elsevier, vol. 84(C).
    3. Han, Ouzhu & Ding, Tao & Yang, Miao & Jia, Wenhao & He, Xinran & Ma, Zhoujun, 2024. "A novel 4-level joint optimal dispatch for demand response of data centers with district autonomy realization," Applied Energy, Elsevier, vol. 358(C).
    4. Ping, Xu & Yang, Fubin & Zhang, Hongguang & Zhang, Jian & Xing, Chengda & Yan, Yinlian & Yang, Anren & Wang, Yan, 2023. "Information theory-based dynamic feature capture and global multi-objective optimization approach for organic Rankine cycle (ORC) considering road environment," Applied Energy, Elsevier, vol. 348(C).
    5. Xu, Da & Xiang, Shizhe & Bai, Ziyi & Wei, Juan & Gao, Menglu, 2023. "Optimal multi-energy portfolio towards zero carbon data center buildings in the presence of proactive demand response programs," Applied Energy, Elsevier, vol. 350(C).
    6. Wang, Zhiying & Wang, Yang & Ji, Haoran & Hasanien, Hany M. & Zhao, Jinli & Yu, Lei & He, Jiafeng & Yu, Hao & Li, Peng, 2024. "Distributionally robust planning for data center park considering operational economy and reliability," Energy, Elsevier, vol. 290(C).
    7. Cao, Yujie & Cao, Fang & Wang, Yajing & Wang, Jianxiao & Wu, Lei & Ding, Zhaohao, 2024. "Managing data center cluster as non-wire alternative: A case in balancing market," Applied Energy, Elsevier, vol. 360(C).

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