The Community for Technology Leaders
Green Image
Issue No. 03 - March (2011 vol. 23)
ISSN: 1041-4347
pp: 388-401
Haifeng Chen , NEC Laboratories America, Inc., Princeton
Wenxuan Zhang , Rutgers University, Piscataway
Guofei Jiang , NEC Laboratories America, Inc., Princeton
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.
Distributed systems, knowledge acquisition, knowledge reuse, configuration tuning, active sampling.

G. Jiang, H. Chen and W. Zhang, "Experience Transfer for the Configuration Tuning in Large-Scale Computing Systems," in IEEE Transactions on Knowledge & Data Engineering, vol. 23, no. , pp. 388-401, 2010.
88 ms
(Ver 3.3 (11022016))