TY - GEN
T1 - Green-aware online resource allocation for geo-distributed cloud data centers on multi-source energy
AU - He, Huaiwen
AU - Shen, Hong
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/7/2
Y1 - 2016/7/2
N2 - Huge energy consumption of large-scale cloud data centers damages the environment with excessive carbon emission. More and more data center operators are seeking to reduce carbon footprint via various types of renewable energy sources. However, the intermittent availability of renewable energy source makes it quite challenging to cooperate the dynamic workload arrivals. In this paper, we investigate how to coordinate multi-Type renewable energy (e.g. wind power and solar power) in order to reduce the long-Term energy cost with spatio-Temporal diversity of electricity price for geo-distributed cloud data centers under the constraints of service level agreement (SLA) and carbon footprints. To tackle the randomness of workload arrival, dynamic electricity price change and renewable energy generation, we first formulate the minimizing energy cost problem into a constrained stochastic optimization problem. Then, based on Lyapunov optimization technique, we design an online control algorithm which can work without long-Term future system information for solving the problem. Finally, we evaluate the effectiveness of the algorithm with extensive simulations based on real-world workload traces, electricity price and historic climate data.
AB - Huge energy consumption of large-scale cloud data centers damages the environment with excessive carbon emission. More and more data center operators are seeking to reduce carbon footprint via various types of renewable energy sources. However, the intermittent availability of renewable energy source makes it quite challenging to cooperate the dynamic workload arrivals. In this paper, we investigate how to coordinate multi-Type renewable energy (e.g. wind power and solar power) in order to reduce the long-Term energy cost with spatio-Temporal diversity of electricity price for geo-distributed cloud data centers under the constraints of service level agreement (SLA) and carbon footprints. To tackle the randomness of workload arrival, dynamic electricity price change and renewable energy generation, we first formulate the minimizing energy cost problem into a constrained stochastic optimization problem. Then, based on Lyapunov optimization technique, we design an online control algorithm which can work without long-Term future system information for solving the problem. Finally, we evaluate the effectiveness of the algorithm with extensive simulations based on real-world workload traces, electricity price and historic climate data.
UR - http://www.scopus.com/inward/record.url?scp=85021863537&partnerID=8YFLogxK
U2 - 10.1109/PDCAT.2016.037
DO - 10.1109/PDCAT.2016.037
M3 - Conference contribution
AN - SCOPUS:85021863537
T3 - Parallel and Distributed Computing, Applications and Technologies, PDCAT Proceedings
SP - 113
EP - 118
BT - Proceedings - 17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016
A2 - Shen, Hong
A2 - Shen, Hong
A2 - Sang, Yingpeng
A2 - Tian, Hui
PB - IEEE Computer Society
T2 - 17th International Conference on Parallel and Distributed Computing, Applications and Technologies, PDCAT 2016
Y2 - 16 December 2016 through 18 December 2016
ER -
