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

—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 samp...

• Type: Journal
• Year: 2011
• Where: TKDE
• Authors: Haifeng Chen, Wenxuan Zhang, Guofei Jiang
• Keywords: Bayesian Network, Experiences, Systems, TKDE 2011