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

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

Applied Energy, 2022, vol. 324, issue C, No S0306261922009631

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.

Keywords: Data-driven; Flexibility assessment; Data center; Periodic jobs (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261922009631
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:324:y:2022:i:c:s0306261922009631

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2022.119665

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().