By Topic

Adapting to Run-Time Changes in Policies Driving Autonomic Management

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)

The use of policies within autonomic computing has received significant interest in the recent past. Policy-driven management offers significant benefit since it makes it more straight forward to define and modify systems behavior at run-time, through policy manipulation, rather than through re- engineering. In this paper, we present an adaptive policy-driven autonomic management system which makes use of reinforcement learning methodologies to determine how to best use a set of active policies to meet different performance objectives. The focus, in particular, is on strategies for adapting what has been learned for one set of policy actions to a ";similar"; set of policies when run-time policy modifications occur. We illustrate the impact of the adaptation strategies on the behavior of a multi-component Web server.

Published in:

Fourth International Conference on Autonomic and Autonomous Systems (ICAS'08)

Date of Conference:

16-21 March 2008