DocumentCode
32479
Title
Greening Geographical Load Balancing
Author
Zhenhua Liu ; Minghong Lin ; Wierman, Adam ; Low, Steven ; Andrew, Lachlan L. H.
Author_Institution
Dept. of Comput. & Math. Sci., California Inst. of Technol., Pasadena, CA, USA
Volume
23
Issue
2
fYear
2015
fDate
Apr-15
Firstpage
657
Lastpage
671
Abstract
Energy expenditure has become a significant fraction of data center operating costs. Recently, “geographical load balancing” has been proposed to reduce energy cost by exploiting the electricity price differences across regions. However, this reduction of cost can paradoxically increase total energy use. We explore whether the geographical diversity of Internet-scale systems can also provide environmental gains. Specifically, we explore whether geographical load balancing can encourage use of “green” renewable energy and reduce use of “brown” fossil fuel energy. We make two contributions. First, we derive three distributed algorithms for achieving optimal geographical load balancing. Second, we show that if the price of electricity is proportional to the instantaneous fraction of the total energy that is brown, then geographical load balancing significantly reduces brown energy use. However, the benefits depend strongly on dynamic energy pricing and the form of pricing used.
Keywords
cost reduction; distributed algorithms; environmental factors; fossil fuels; power markets; pricing; resource allocation; Internet-scale system; brown fossil fuel energy; data center; distributed algorithm; dynamic energy pricing; electricity price; energy cost reduction; energy expenditure; greening geographical load balancing; renewable energy; Data models; Delays; Electricity; Load management; Load modeling; Routing; Servers; Data centers; demand response; distributed algorithms; geographical load balancing; renewable energy;
fLanguage
English
Journal_Title
Networking, IEEE/ACM Transactions on
Publisher
ieee
ISSN
1063-6692
Type
jour
DOI
10.1109/TNET.2014.2308295
Filename
6766273
Link To Document