Misconfiguration detection for cloud datacenters using decision tree analysis
Tetsuya Uchiumi, Shinji Kikuchi, Yasuhide Matsumoto · 2012
Since many components comprising large scale cloud datacenters have a great number of configuration parameters (e.g. hostnames, languages, and time zones), it is difficult to keep consistencies in the configuration parameters. In such cases, misconfigured parameters can cause service failures. For this reason, we propose a misconfiguration detection method for large-scale cloud datacenters, which can automatically determine possible misconfigurations by identifying the relations existing among majority of the parameters using statistical decision tree analysis. We have also developed a pattern modification method to improve the accuracy of the decision tree approach. We evaluated the misconfiguration detection performance of the proposed method by using both artificial data and actual data. The results show that we can achieve higher accuracy (78.6% in the actual data) in misconfiguration detection by using the pattern modification.