Abstract
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.
Original language | English (US) |
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Title of host publication | 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 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 135-138 |
Number of pages | 4 |
ISBN (Electronic) | 9781509019618 |
DOIs | |
State | Published - Jun 1 2016 |
Event | 4th IEEE Annual International Conference on Cloud Engineering, IC2E 2016 - Berlin, Germany Duration: Apr 4 2016 → Apr 8 2016 |
Other
Other | 4th IEEE Annual International Conference on Cloud Engineering, IC2E 2016 |
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Country | Germany |
City | Berlin |
Period | 4/4/16 → 4/8/16 |
Keywords
- power management
- reinforcement learning
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Networks and Communications