TY - GEN
T1 - Joint Energy Optimization on the Server and Network Sides for Geo-Distributed Datacenters
AU - Qin, Yang
AU - Han, Wuji
AU - Yang, Yuanyuan
AU - Yang, Weihong
AU - Liu, Bing
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/5
Y1 - 2019/5
N2 - With the rapid development of cloud computing, many cloud service providers have been deploying more and more datacenters to provide better reliability and quality of service. The energy optimization problem has become an emerging concern. The current researches focus on how to either reduce the energy consumption of servers or reduce the consumption of inter-datacenters data transmission. However, energy optimization of joint inter-datacenter and servers has not been explored. In this paper, we first introduced the background to the energy consumption problem of geographically distributed datacenters. Then, based on the Service Level Agreement (SLA), this paper proposes an online control framework to minimize the energy consummation cost. The online control framework can dynamically make decisions by adjusting geographically load balancing, capacity right-sizing, server speed scaling, and flow programming. Finally, the simulation verifies the effectiveness of the proposed framework in cost saving.
AB - With the rapid development of cloud computing, many cloud service providers have been deploying more and more datacenters to provide better reliability and quality of service. The energy optimization problem has become an emerging concern. The current researches focus on how to either reduce the energy consumption of servers or reduce the consumption of inter-datacenters data transmission. However, energy optimization of joint inter-datacenter and servers has not been explored. In this paper, we first introduced the background to the energy consumption problem of geographically distributed datacenters. Then, based on the Service Level Agreement (SLA), this paper proposes an online control framework to minimize the energy consummation cost. The online control framework can dynamically make decisions by adjusting geographically load balancing, capacity right-sizing, server speed scaling, and flow programming. Finally, the simulation verifies the effectiveness of the proposed framework in cost saving.
UR - https://www.scopus.com/pages/publications/85070217664
U2 - 10.1109/ICC.2019.8761333
DO - 10.1109/ICC.2019.8761333
M3 - Conference contribution
AN - SCOPUS:85070217664
T3 - IEEE International Conference on Communications
BT - 2019 IEEE International Conference on Communications, ICC 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE International Conference on Communications, ICC 2019
Y2 - 20 May 2019 through 24 May 2019
ER -