Data-driven flexibility assessment for internet data center towards periodic batch workloads. (15th October 2022) - Cao, Yujie; Cheng, Ming; Zhang, Sufang; Mao, Hongju; Wang, Peng; Li, Chao; Feng, Yihui; Ding, Zhaohao
Highlights: A data-driven flexibility assessment scheme of internet data center is proposed by investigating the temporal shifting capability of periodic batch workloads. The power consumption model of data center is modeled by a data-driven manner based real-world workload processing and energy consumption data. The periodic arrival patterns and job dependencies are incorporated into the assessment model to avoid infeasible job scheduling. The effectiveness and accuracy of the proposed scheme is illustrated with real-world production workload trace from one of the major cloud service providers. 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.