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Data center demand response: avoiding the coincident peak via workload shifting and local generation

Liu, Zhenhua and Wierman, Adam and Cheng, Yuan and Razon, Benjamin and Chen, Niangjun (2013) Data center demand response: avoiding the coincident peak via workload shifting and local generation. ACM SIGMETRICS Performance Evaluation Review, 41 (4). pp. 341-342. ISSN 0163-5999. doi:10.1145/2494232.2465740.

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Demand response is a crucial aspect of the future smart grid. It has the potential to provide significant peak demand reduction and to ease the incorporation of renewable energy into the grid. Data centers' participation in demand response is becoming increasingly important given the high and increasing energy consumption and the flexibility in demand management in data centers compared to conventional industrial facilities. In this extended abstract we briefly describe recent work in our full paper on two demand response schemes to reduce a data center's peak loads and energy expenditure: workload shifting and the use of local power generations. In our full paper, we conduct a detailed characterization study of coincident peak data over two decades from Fort Collins Utilities, Colorado and then develop two algorithms for data centers by combining workload scheduling and local power generation to avoid the coincident peak and reduce the energy expenditure. The first algorithm optimizes the expected cost and the second one provides a good worst-case guarantee for any coincident peak pattern. We evaluate these algorithms via numerical simulations based on real world traces from production systems. The results show that using workload shifting in combination with local generation can provide significant cost savings (up to 40% in the Fort Collins Utilities' case) compared to either alone.

Item Type:Article
Related URLs:
URLURL TypeDescription
Chen, Niangjun0000-0002-2289-9737
Additional Information:Copyright is held by the author/owner(s). This work was supported by NSF grants CNS 0846025, DoE grant DE-EE0002890, and HP Labs.
Funding AgencyGrant Number
Department of Energy (DOE)DE-EE0002890
Subject Keywords:Demand response, coincident peak pricing, data center, work-load shifting, online algorithm
Issue or Number:4
Classification Code:C.0 [ Computer Systems Organization ]: General
Record Number:CaltechAUTHORS:20161128-165016440
Persistent URL:
Official Citation:Zhenhua Liu, Adam Wierman, Yuan Chen, Benjamin Razon, and Niangjun Chen. 2013. Data center demand response: avoiding the coincident peak via workload shifting and local generation. SIGMETRICS Perform. Eval. Rev. 41, 1 (June 2013), 341-342. DOI:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:72349
Deposited On:29 Nov 2016 04:26
Last Modified:11 Nov 2021 05:00

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