research-article

Coalitional Datacenter Energy Cost Optimization in Electricity Markets

Authors:

Miao PanAuthors Info & Claims

e-Energy '17: Proceedings of the Eighth International Conference on Future Energy Systems

Pages 191 - 202

https://doi.org/10.1145/3077839.3077860

Published: 16 May 2017 Publication History

Abstract

In this paper, we study how datacenter energy cost can be effectively reduced in the wholesale electricity market via cooperative power procurement. Intuitively, by aggregating workloads across a group of datacenters, the overall power demand uncertainty of datacenters can be reduced, resulting in less chance of being penalized when participating in the wholesale electricity market. We use cooperative game theory to model the cooperative electricity procurement process of datacenters as a cooperative game, and show the cost saving benefits of aggregation. Then, a cost allocation scheme based on the marginal contribution of each datacenter to the total expected cost is proposed to fairly distribute the aggregation benefits among the participating datacenters. Finally, numerical experiments based on real-world traces are conducted to illustrate the benefits of aggregation compared to noncooperative power procurement.

References

[1]

National Resources Defense Council. Scaling up energy efficiency across the data center industry: evaluating key drivers and barriers. Issue Paper, 2014.

[2]

James Hamilton. Cost of power in large-scale data centers. Blog entry dated, 11:28, 2008.

[3]

Richard Brown et al. Report to congress on server and data center energy efficiency: Public law 109-431. Technical report, Lawrence Berkeley National Laboratory, 2008.

[4]

Liang Yu, Tao Jiang, Yang Cao, and Qian Zhang. Risk-constrained operation for internet data centers in deregulated electricity markets. IEEE Transactions on Parallel and Distributed Systems, 25(5):1306--1316, 2014.

Digital Library

[5]

Yuanxiong Guo, Yanmin Gong, Yuguang Fang, Pramod P Khargonekar, and Xiaojun Geng. Energy and network aware workload management for sustainable data centers with thermal storage. Parallel and Distributed Systems, IEEE Transactions on, 25(8):2030--2042, 2014.

[6]

Electric reliability council of texas. http://www.ercot.com/.

[7]

California iso. http://www.caiso.com/. In Green Computing Conference (IGCC), 2012 International, pages 1--10. IEEE, 2012.

[8]

Google gets go-ahead to buy, sell energy. https://www.cnet.com/news/google-gets-go-ahead-to-buy-sell-energy/, Feb. 2010.

[9]

Mahdi Ghamkhari, Adam Wierman, and Hamed Mohsenian-Rad. Energy portfolio optimization of data centers. IEEE Transactions on Smart Grid, 2016.

[10]

Walid Saad, Zhu Han, Mérouane Debbah, Are Hjorungnes, and Tamer Basar. Coalitional game theory for communication networks. IEEE Signal Processing Magazine, 26(5):77--97, 2009.

[11]

H Peyton Young. Cost allocation: methods, principles, applications. North Holland Publishing Co., 1985.

[12]

Stef H Tijs and Theo SH Driessen. Game theory and cost allocation problems. Management Science, 32(8):1015--1028, 1986.

Digital Library

[13]

Lloyd S Shapley. Cores of convex games. International journal of game theory, 1(1):11--26, 1971.

[14]

Lloyd S Shapley. On balanced sets and cores. Naval research logistics quarterly, 14(4):453--460, 1967.

[15]

Roger B Myerson. Game theory: analysis of conflict. Harvard University, 1991.

[16]

Guillermo Owen. Game theory. Emerald Group, 2013.

[17]

Theo SH Driessen. Cooperative games, solutions and applications, volume 3. Springer Science & Business Media, 2013.

[18]

Jayaram K Sankaran. On finding the nucleolus of an n-person cooperative game. International Journal of Game Theory, 19(4):329--338, 1991.

Digital Library

[19]

Zhenhua Liu, Yuan Chen, Cullen Bash, Adam Wierman, Daniel Gmach, Zhikui Wang, Manish Marwah, and Chris Hyser. Renewable and cooling aware workload management for sustainable data centers. In ACM SIGMETRICS Performance Evaluation Review, volume 40, pages 175--186. ACM, 2012.

Digital Library

[20]

Zhenhua Liu, Minghong Lin, Adam Wierman, Steven H Low, and Lachlan LH Andrew. Greening geographical load balancing. In Proceedings of the ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems, pages 233--244. ACM, 2011.

Digital Library

[21]

Minghong Lin, Adam Wierman, Lachlan LH Andrew, and Eno Thereska. Dynamic right-sizing for power-proportional data centers. IEEE/ACM Transactions on Networking (TON), 21(5):1378--1391, 2013.

[22]

Qiang Wu. Making Facebook's software infrastructure more energy efficient with Autoscale.

[23]

Martin J Osborne and Ariel Rubinstein. A course in game theory. MIT press, 1994.

[24]

Mahdi Kohansal and Hamed Mohsenian-Rad. Price-maker economic bidding in two-settlement pool-based markets: The case of time-shiftable loads. IEEE Transactions on Power Systems, 31(1):695--705, 2016.

[25]

Eilyan Y Bitar, Ram Rajagopal, Pramod P Khargonekar, Kameshwar Poolla, and Pravin Varaiya. Bringing wind energy to market. IEEE Transactions on Power Systems, 27(3):1225--1235, 2012.

[26]

Enrique Baeyens, Eilyan Y Bitar, Pramod P Khargonekar, and Kameshwar Poolla. Coalitional aggregation of wind power. IEEE Transactions on Power Systems, 28(4):3774--3784, 2013.

[27]

Charles Reiss, John Wilkes, and Joseph L Hellerstein. Google cluster-usage traces: format+ schema. Google Inc., White Paper, pages 1--14, 2011.

[28]

Brian Guenter, Navendu Jain, and Charles Williams. Managing cost, performance, and reliability tradeoffs for energy-aware server provisioning. In INFOCOM, 2011 Proceedings IEEE, pages 1332--1340. IEEE, 2011.

[29]

Minghong Lin, Adam Wierman, Lachlan LH Andrew, and Eno Thereska. Online dynamic capacity provisioning in data centers. In Communication, Control, and Computing (Allerton), 2011 49th Annual Allerton Conference on, pages 1159--1163. IEEE, 2011.

[30]

Adam Wierman, Lachlan LH Andrew, and Ao Tang. Power-aware speed scaling in processor sharing systems. In INFOCOM 2009, IEEE, pages 2007--2015. IEEE, 2009.

[31]

Rade Stanojevic and Robert Shorten. Distributed dynamic speed scaling. In INFOCOM, 2010 Proceedings IEEE, pages 1--5. IEEE, 2010.

[32]

Lijun Chen and Na Li. On the interaction between load balancing and speed scaling. IEEE Journal on Selected Areas in Communications, 33(12):2567--2578, 2015.

[33]

Minghong Lin, Zhenhua Liu, Adam Wierman, and Lachlan LH Andrew. Online algorithms for geographical load balancing. In Green Computing Conference (IGCC), 2012 International, pages 1--10. IEEE, 2012.

Digital Library

[34]

Mohammad A Islam, Anshul Gandhi, and Shaolei Ren. Minimizing electricity cost for geo-distributed interactive services with tail latency constraint.

[35]

Ying Zhang, Lei Deng, Minghua Chen, and Peijian Wang. Joint bidding and geographic load blancing for datacenters: Is uncertainty a blessing or a curse? In INFOCOM, 2017 Proceedings IEEE. IEEE, 2017.

[36]

Cheng Wang, Bhuvan Urgaonkar, George Kesidis, Uday V Shanbhag, and Qian Wang. A case for virtualizing the electric utility in cloud data centers. In HotCloud, 2014.

[37]

Zhenhua Liu, Iris Liu, Steven Low, and Adam Wierman. Pricing data center demand response. ACM SIGMETRICS Performance Evaluation Review, 42(1):111--123, 2014.

Digital Library

[38]

Lei Rao, Xue Liu, Le Xie, and Wenyu Liu. Minimizing electricity cost: optimization of distributed internet data centers in a multi-electricity-market environment. In INFOCOM, 2010 Proceedings IEEE, pages 1--9. IEEE, 2010.

Digital Library

[39]

Lei Rao, Xue Liu, Le Xie, and Zhan Pang. Hedging against uncertainty: A tale of internet data center operations under smart grid environment. IEEE Transactions on Smart Grid, 2(3):555--563, 2011.

[40]

Mahdi Ghamkhari, Hamed Mohsenian-Rad, and Adam Wierman. Optimal risk-aware power procurement for data centers in day-ahead and real-time electricity markets. In Computer Communications Workshops (INFOCOM WKSHPS), 2014 IEEE Conference on, pages 610--615. IEEE, 2014.

[41]

Yuanxiong Guo, Zongrui Ding, Yuguang Fang, and Dapeng Wu. Cutting down electricity cost in internet data centers by using energy storage. In Global Telecommunications Conference (GLOBECOM 2011), 2011 IEEE, pages 1--5. IEEE, 2011.

[42]

Yuan Yao, Longbo Huang, Abhihshek Sharma, Leana Golubchik, and Michael Neely. Data centers power reduction: A two time scale approach for delay tolerant workloads. In INFOCOM, 2012 Proceedings IEEE, pages 1431--1439. IEEE, 2012.

[43]

Yuanxiong Guo and Yuguang Fang. Electricity cost saving strategy in data centers by using energy storage. IEEE Transactions on Parallel and Distributed Systems, 24(6):1149--1160, 2013.

Digital Library

[44]

Yue Zhao, Junjie Qin, Ram Rajagopal, Andrea Goldsmith, and H Vincent Poor. Wind aggregation via risky power markets. IEEE Transactions on Power Systems, 30(3):1571--1581, 2015.

[45]

Dileep Kalathil, Chenye Wu, Kameshwar Poolla, and Pravin Varaiya. The sharing economy for the smart grid. arXiv preprint arXiv:1608.06990, 2016.

Cited By

Zhang YTsiligkaridis APaschalidis ICoskun A(2024)Data center and load aggregator coordination towards electricity demand responseSustainable Computing: Informatics and Systems10.1016/j.suscom.2024.10095742(100957)Online publication date: Apr-2024
https://doi.org/10.1016/j.suscom.2024.100957
Hogade NPasricha SSiegel H(2022)Energy and Network Aware Workload Management for Geographically Distributed Data CentersIEEE Transactions on Sustainable Computing10.1109/TSUSC.2021.30860877:2(400-413)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TSUSC.2021.3086087
Zhang YWilson DPaschalidis ICoskun A(2022)HPC Data Center Participation in Demand Response: An Adaptive Policy With QoS AssuranceIEEE Transactions on Sustainable Computing10.1109/TSUSC.2021.30772547:1(157-171)Online publication date: 1-Jan-2022
https://doi.org/10.1109/TSUSC.2021.3077254
Show More Cited By

Recommendations

Managing energy, performance and cost in large scale heterogeneous datacenters using migrations
Abstract
Improving datacenter energy efficiency becomes increasingly important due to energy supply problems, fuel costs and global warming. Virtualisation can help to improve datacenter energy efficiency through server consolidation which ...
Highlights
- The existence of a trade-off between overall energy consumption and performance (hence cost).
Electricity Cost Saving Strategy in Data Centers by Using Energy Storage

Electricity expenditure comprises a significant fraction of the total operating cost in data centers. Hence, cloud service providers are required to reduce electricity cost as much as possible. In this paper, we consider utilizing existing energy ...
Cost and emission allocation for joint replenishment systems subject to carbon constraints
Highlights
- Joint replenishment systems subject to four types of carbon constraints, respectively.
- Cooperative game theory is applied for cost and emission allocation.
- Cost allocations rules are proposed based on Lagrangian multipliers.
- A ...
Abstract
We consider joint replenishment among multiple retailers subject to various types of carbon constraints. In particular, we consider four types of carbon constraints: carbon tax, strict carbon cap, carbon cap-and-offset, and carbon cap-and-price. ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

e-Energy '17: Proceedings of the Eighth International Conference on Future Energy Systems

May 2017

388 pages

ISBN:9781450350365

DOI:10.1145/3077839

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGCOMM: ACM Special Interest Group on Data Communication

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 May 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

e-Energy '17

Sponsor:

SIGCOMM

e-Energy '17: The Eighth International Conference on Future Energy Systems

May 16 - 19, 2017

Shatin, Hong Kong

Acceptance Rates

Overall Acceptance Rate 160 of 446 submissions, 36%

Upcoming Conference

E-Energy '25

Sponsor:
sigenergy

The 16th ACM International Conference on Future and Sustainable Energy Systems

June 17 - 20, 2025

Rotterdam , Netherlands

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
236
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)0

Reflects downloads up to 15 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhang YTsiligkaridis APaschalidis ICoskun A(2024)Data center and load aggregator coordination towards electricity demand responseSustainable Computing: Informatics and Systems10.1016/j.suscom.2024.10095742(100957)Online publication date: Apr-2024
https://doi.org/10.1016/j.suscom.2024.100957
Hogade NPasricha SSiegel H(2022)Energy and Network Aware Workload Management for Geographically Distributed Data CentersIEEE Transactions on Sustainable Computing10.1109/TSUSC.2021.30860877:2(400-413)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TSUSC.2021.3086087
Zhang YWilson DPaschalidis ICoskun A(2022)HPC Data Center Participation in Demand Response: An Adaptive Policy With QoS AssuranceIEEE Transactions on Sustainable Computing10.1109/TSUSC.2021.30772547:1(157-171)Online publication date: 1-Jan-2022
https://doi.org/10.1109/TSUSC.2021.3077254
Sun JChen SGiannakis GYang QYang Z(2021)Cross Service Providers Workload Balancing for Data Centers in Deregulated Electricity MarketsIEEE Transactions on Control of Network Systems10.1109/TCNS.2021.30532368:2(803-815)Online publication date: Jun-2021
https://doi.org/10.1109/TCNS.2021.3053236
Moghaddam MManshaei MSoorki MSaad WGoudarzi MNiyato D(2021)On Coordination of Smart Grid and Cooperative Cloud ProvidersIEEE Systems Journal10.1109/JSYST.2020.298701715:1(672-683)Online publication date: Mar-2021
https://doi.org/10.1109/JSYST.2020.2987017
Zhang YPaschalidis ICoskun A(2019)Data Center Participation in Demand Response Programs with Quality-of-Service GuaranteesProceedings of the Tenth ACM International Conference on Future Energy Systems10.1145/3307772.3328309(285-302)Online publication date: 15-Jun-2019
https://dl.acm.org/doi/10.1145/3307772.3328309
Moghaddam MManshaei MSaad WGoudarzi M(2019)On Data Center Demand Response: A Cloud Federation ApproachIEEE Access10.1109/ACCESS.2019.29285527(101829-101843)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2928552

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents