TY - GEN
T1 - A reinforcement learning-based power management framework for green computing data centers
AU - Lin, Xue
AU - Wang, Yanzhi
AU - Pedram, Massoud
N1 - Publisher Copyright:
© 2016 IEEE.
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2016/6/1
Y1 - 2016/6/1
N2 - Various power management techniques have been exploited to reduce the energy consumption of data centers. In this work, we propose a reinforcement learning-based power management framework for data centers, which does not rely on any given stationary assumptions of the job arrival and job service processes. By carefully designing the state space, the action space, and the reward of a learning process, the objective of the reinforcement learning agent coincides with our goal of reducing the server pool energy consumption with reasonable average job response time. Real Google cluster data traces are used to verify the effectiveness of the proposed reinforcement learning-based data center power management framework.
AB - Various power management techniques have been exploited to reduce the energy consumption of data centers. In this work, we propose a reinforcement learning-based power management framework for data centers, which does not rely on any given stationary assumptions of the job arrival and job service processes. By carefully designing the state space, the action space, and the reward of a learning process, the objective of the reinforcement learning agent coincides with our goal of reducing the server pool energy consumption with reasonable average job response time. Real Google cluster data traces are used to verify the effectiveness of the proposed reinforcement learning-based data center power management framework.
KW - power management
KW - reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=84978079861&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84978079861&partnerID=8YFLogxK
U2 - 10.1109/IC2E.2016.33
DO - 10.1109/IC2E.2016.33
M3 - Conference contribution
AN - SCOPUS:84978079861
T3 - Proceedings - 2016 IEEE International Conference on Cloud Engineering, IC2E 2016: Co-located with the 1st IEEE International Conference on Internet-of-Things Design and Implementation, IoTDI 2016
SP - 135
EP - 138
BT - Proceedings - 2016 IEEE International Conference on Cloud Engineering, IC2E 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th IEEE Annual International Conference on Cloud Engineering, IC2E 2016
Y2 - 4 April 2016 through 8 April 2016
ER -