By Topic

Experience Transfer for the Configuration Tuning in Large-Scale Computing Systems

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

3 Author(s)
Haifeng Chen ; NEC Laboratories America, Inc., Princeton ; Wenxuan Zhang ; Guofei Jiang

This paper proposes a new strategy, the experience transfer, to facilitate the management of large-scale computing systems. It deals with the utilization of management experiences in one system (or previous systems) to benefit the same management task in other systems (or current systems). We use the system configuration tuning as a case application to demonstrate all procedures involved in the experience transfer including the experience representation, experience extraction, and experience embedding. The dependencies between system configuration parameters are treated as transferable experiences in the configuration tuning for two reasons: 1) because such knowledge is helpful to the efficiency of the optimal configuration search, and 2) because the parameter dependencies are typically unchanged between two similar systems. We use the Bayesian network to model configuration dependencies and present a configuration tuning algorithm based on the Bayesian network construction and sampling. As a result, after the configuration tuning is completed in the original system, we can obtain a Bayesian network as the by-product which records the dependencies between system configuration parameters. Such a network is then embedded into the tuning process in other similar systems as transferred experiences to improve the configuration search efficiency. Experimental results in a web-based system show that with the help of transferred experiences, the configuration tuning process can be significantly accelerated.

Published in:

IEEE Transactions on Knowledge and Data Engineering  (Volume:23 ,  Issue: 3 )