Determine Configuration Entry Correlations for Web Application Systems

Web application systems, comprising of heterogeneous and loosely coupled components, are usually highly-configurable due to the large number of configuration entries scattering in the components. The dependencies between components lead their entries correlate to one another, which makes the system deployment and migration daunting and error-prone. For two correlated entries, changing value of one entry requires the value change of the other. Otherwise, some implied constraints would be violated and the system failure will occur. Keeping track of entry correlations, which is essential to system reliabilities, is not a simple work as it often crosses products and requires in-depth domain knowledge. This paper proposes a method to automate the process of determining entry correlations. The method first narrows down the exploring scale to those frequently-set entries based on crawled sample data. Then, it generates a correlation score for each entry pair, which is calculated according to entry names, values and inferred types. Thirdly, a set of heuristics are provided to determine a candidate set of the likely correlations. Finally, a rank-ordered list of entry correlations is output so that system administrators can consult it to check system configuration systematically. Based on the method, we implement a tool, Correlation Explorer, and make experiments and evaluations with some real world systems. The result shows that Correlation Explorer is effective in finding a large portion of entry correlations.