NDLI: A Reinforcement Learning Approach to Online Web Systems Auto-configuration

Content Provider	IEEE Xplore Digital Library
Author	Xiangping Bu Jia Rao Cheng-Zhong Xu
Copyright Year	2009
Abstract	In a web system, configuration is crucial to the performance and service availability. It is a challenge, not only because of the dynamics of Internet traffic, but also the dynamic virtual machine environment the system tends to be run on. In this paper, we propose a reinforcement learning approach for autonomic configuration and reconfiguration of multi-tier web systems. It is able to adapt performance parameter settings not only to the change of workload, but also to the change of virtual machine configurations. The RL approach is enhanced with an efficient initialization policy to reduce the learning time for online decision. The approach is evaluated using TPC-W benchmark on a three-tier website hosted on a Xen-based virtual machine environment. Experiment results demonstrate that the approach can autoconfigure the web system dynamically in response to the change in both workload and VM resource. It can drive the system into a near-optimal configuration setting in less than 25 trial-and-error iterations.
Starting Page	2
Ending Page	11
File Size	278048
Page Count	10
File Format	PDF
ISBN	9780769536590
ISSN	10636927
DOI	10.1109/ICDCS.2009.76
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-22
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning Virtual machining Hardware Virtual manufacturing Web and internet services Resource management Distributed computing Availability System performance Automatic control Web Systems Reinforcement Learning Auto-configuration
Content Type	Text
Resource Type	Article
Subject	Computer Networks and Communications Hardware and Architecture Software

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