Experience Transfer for the Configuration Tuning in Large Scale Computing Systems

摘要

This paper has proposed the experience transfer to improve the efficiencies of configuration tuning in computing systems. We have treated the dependencies between system configurations as useful experiences in configuration tuning, and proposed a Bayesian network guided tuning algorithm to discover the optimal configuration setting. Results have shown that our transferred dependency knowledge can achieve tremendous time savings for system operators in tuning the system performance.