TY - GEN
T1 - An Incentive Mechanism for Improving Energy Efficiency of Colocation Data Centers Based on Power Prediction
AU - Chi, Ce
AU - Ji, Kaixuan
AU - Marahatta, Avinab
AU - Zhang, Fa
AU - Wang, Youshi
AU - Liu, Zhiyong
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - Colocation data centers (colocations, for short) are developing rapidly in recent years, resulting in a heavy burden on the power grid and the environment. Due to the special management mode of colocations, even the colocation operators wish to reduce their power demand, they have no authority to control the servers because the servers belong to and are operated by the tenants themselves. To solve the "uncoordinated relationship"issue between operators and tenants, a truthful and feasible incentive mechanism MesPP is proposed in this paper. Different from existing works, MesPP aims at maximizing the energy reduction of colocations with a limited cost budget and can be applied even there is no demand response (DR) program. Meanwhile, power prediction is integrated into MesPP to further improve the energy efficiency of colocations and fairness of the mechanism. To solve the optimization problem, we develop a (1-ϵ)-approximation algorithm. Simulations are performed and show that MesPP can achieve 12.23% more energy saving compared with existing incentive mechanisms.
AB - Colocation data centers (colocations, for short) are developing rapidly in recent years, resulting in a heavy burden on the power grid and the environment. Due to the special management mode of colocations, even the colocation operators wish to reduce their power demand, they have no authority to control the servers because the servers belong to and are operated by the tenants themselves. To solve the "uncoordinated relationship"issue between operators and tenants, a truthful and feasible incentive mechanism MesPP is proposed in this paper. Different from existing works, MesPP aims at maximizing the energy reduction of colocations with a limited cost budget and can be applied even there is no demand response (DR) program. Meanwhile, power prediction is integrated into MesPP to further improve the energy efficiency of colocations and fairness of the mechanism. To solve the optimization problem, we develop a (1-ϵ)-approximation algorithm. Simulations are performed and show that MesPP can achieve 12.23% more energy saving compared with existing incentive mechanisms.
KW - Colocation data center
KW - energy efficiency
KW - mechanism design
KW - power prediction
UR - http://www.scopus.com/inward/record.url?scp=85094174820&partnerID=8YFLogxK
U2 - 10.1109/ISCC50000.2020.9219590
DO - 10.1109/ISCC50000.2020.9219590
M3 - Conference contribution
AN - SCOPUS:85094174820
T3 - Proceedings - IEEE Symposium on Computers and Communications
BT - 2020 IEEE Symposium on Computers and Communications, ISCC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Symposium on Computers and Communications, ISCC 2020
Y2 - 7 July 2020 through 10 July 2020
ER -