TY - JOUR
T1 - Towards optimal electric demand management for internet data centers
AU - Li, Jie
AU - Li, Zuyi
AU - Ren, Kui
AU - Liu, Xue
N1 - Funding Information:
Manuscript received March 23, 2011; revised June 14, 2011; accepted August 01, 2011. Date of publication October 10, 2011; date of current version February 23, 2012. This work was supported in part by the U.S. Department of Energy under Grant # DE-FC26-08NT02875. Paper no. TSG-00109-2011. J. Li, Z. Li, and K. Ren are with the Electrical and Computer Engineering Department, Illinois Institute of Technology, Chicago, IL 60616 USA (e-mail: [email protected]; [email protected]; [email protected]). X. Liu is with the School of Computer Science, McGill University, Montreal, QC, Canada (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TSG.2011.2165567
PY - 2012/3
Y1 - 2012/3
N2 - Electricity cost is becoming a major portion of Internet data center (IDC)'s operation cost and large-scale IDCs are becoming important consumers of regional electricity markets. IDC's energy efficiency is gaining more attention by data center operators and electricity market operators. Effective IDC electric demand management solutions are eagerly sought by all stakeholders. In this paper, a mixed-integer programming model based IDC electric demand management solution is proposed, which integrates both the impacts of locational marginal electricity prices and power management capability of IDC itself. Dynamic voltage/frequency scaling of individual server, cluster server ON/OFF scheduling, and dynamic workload dispatching are optimized while complying with all the IDC system-wide and individual heterogeneous servers' operation constraints according to the IDC applications' temporal variant workload. Reduced electricity cost can be achieved together with guaranteed QoS requirement and reliability consideration by using the proposed model. World Cup '98 data is utilized to evaluate the effectiveness of the proposed solution. According to the experimental evaluation, electricity cost could be cut by more than 20% in a peak workload period and by more than 80% in a light workload period. Besides, more than 6% electricity cost could be cut by considering the impact of electricity price difference. Experimental results also reveal that higher QoS requirement and reliability consideration could result in higher electricity cost.
AB - Electricity cost is becoming a major portion of Internet data center (IDC)'s operation cost and large-scale IDCs are becoming important consumers of regional electricity markets. IDC's energy efficiency is gaining more attention by data center operators and electricity market operators. Effective IDC electric demand management solutions are eagerly sought by all stakeholders. In this paper, a mixed-integer programming model based IDC electric demand management solution is proposed, which integrates both the impacts of locational marginal electricity prices and power management capability of IDC itself. Dynamic voltage/frequency scaling of individual server, cluster server ON/OFF scheduling, and dynamic workload dispatching are optimized while complying with all the IDC system-wide and individual heterogeneous servers' operation constraints according to the IDC applications' temporal variant workload. Reduced electricity cost can be achieved together with guaranteed QoS requirement and reliability consideration by using the proposed model. World Cup '98 data is utilized to evaluate the effectiveness of the proposed solution. According to the experimental evaluation, electricity cost could be cut by more than 20% in a peak workload period and by more than 80% in a light workload period. Besides, more than 6% electricity cost could be cut by considering the impact of electricity price difference. Experimental results also reveal that higher QoS requirement and reliability consideration could result in higher electricity cost.
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U2 - 10.1109/TSG.2011.2165567
DO - 10.1109/TSG.2011.2165567
M3 - Article
AN - SCOPUS:84857657104
SN - 1949-3053
VL - 3
SP - 183
EP - 192
JO - IEEE Transactions on Smart Grid
JF - IEEE Transactions on Smart Grid
IS - 1
M1 - 6041050
ER -