TY - GEN
T1 - Energy efficiency in colocation data centers
T2 - 8th International Green and Sustainable Computing Conference, IGSC 2017
AU - Wang, Youshi
AU - Zhang, Fa
AU - Ren, Shaolei
AU - Liu, Fangming
AU - Wang, Rui
AU - Liu, Zhiyong
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Colocation data centers (or colocations, for short) are important participants in emergency demand response (EDR) programs. One key challenge in colocations is that tenants control their own servers, thus, may not coordinate to reduce their power consumption. In this paper, we propose a joint truthful incentive mechanism Co-Colo to encourage tenants joining EDR programs, which includes a local optimization mechanism (LocalOpt) and a global optimization mechanism (GlobalOpt). In LocalOpt, tenants are motivated to improve the energy efficiency locally. In GlobalOpt, tenants can request some public server resources to improve the energy efficiency. By jointly considering the two mechanisms, Co-Colo effectively reduces the energy-saving cost. A (1 + ϵ)-approximation algorithm is proposed to obtain the asymptotic optimal energy-saving scheme. We also consider a special case when the public resources are sufficient, and design a 2-approximation algorithm. Furthermore, the robustness of the proposed algorithms are proved. Trace-driven simulations verify the effectiveness and feasibility of Co-Colo.
AB - Colocation data centers (or colocations, for short) are important participants in emergency demand response (EDR) programs. One key challenge in colocations is that tenants control their own servers, thus, may not coordinate to reduce their power consumption. In this paper, we propose a joint truthful incentive mechanism Co-Colo to encourage tenants joining EDR programs, which includes a local optimization mechanism (LocalOpt) and a global optimization mechanism (GlobalOpt). In LocalOpt, tenants are motivated to improve the energy efficiency locally. In GlobalOpt, tenants can request some public server resources to improve the energy efficiency. By jointly considering the two mechanisms, Co-Colo effectively reduces the energy-saving cost. A (1 + ϵ)-approximation algorithm is proposed to obtain the asymptotic optimal energy-saving scheme. We also consider a special case when the public resources are sufficient, and design a 2-approximation algorithm. Furthermore, the robustness of the proposed algorithms are proved. Trace-driven simulations verify the effectiveness and feasibility of Co-Colo.
UR - http://www.scopus.com/inward/record.url?scp=85051016418&partnerID=8YFLogxK
U2 - 10.1109/IGCC.2017.8323564
DO - 10.1109/IGCC.2017.8323564
M3 - Conference contribution
AN - SCOPUS:85051016418
T3 - 2017 8th International Green and Sustainable Computing Conference, IGSC 2017
SP - 1
EP - 8
BT - 2017 8th International Green and Sustainable Computing Conference, IGSC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 October 2017 through 25 October 2017
ER -