NDLI: Energy-Efficient Virtual Machines Consolidation in Cloud Data Centers Using Reinforcement Learning

Content Provider	IEEE Xplore Digital Library
Author	Farahnakian, F. Liljeberg, P. Plosila, J.
Copyright Year	2014
Description	Author affiliation: Dept. of Inf. Technol., Univ. of Turku, Turku, Finland (Farahnakian, F.; Liljeberg, P.; Plosila, J.)
Abstract	Dynamic consolidation techniques optimize resource utilization and reduce energy consumption in Cloud data centers. They should consider the variability of the workload to decide when idle or underutilized hosts switch to sleep mode in order to minimize energy consumption. In this paper, we propose a Reinforcement Learning-based Dynamic Consolidation method (RL-DC) to minimize the number of active hosts according to the current resources requirement. The RL-DC utilizes an agent to learn the optimal policy for determining the host power mode by using a popular reinforcement learning method. The agent learns from past knowledge to decide when a host should be switched to the sleep or active mode and improves itself as the workload changes. Therefore, RL-DC does not require any prior information about workload and it dynamically adapts to the environment to achieve online energy and performance management. Experimental results on the real workload traces from more than a thousand PlanetLab virtual machines show that RL-DC minimizes energy consumption and maintains required performance levels.
Sponsorship	IBM
Starting Page	500
Ending Page	507
File Size	220129
Page Count	8
File Format	PDF
ISBN	9781479927296
ISSN	10666192
DOI	10.1109/PDP.2014.109
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-02-12
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Resource management Servers Heuristic algorithms Switches Power demand Energy consumption Prediction algorithms cloud data centers energy management dynamic consolidation reinforcement learning green IT
Content Type	Text
Resource Type	Article

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