RCA Prediction using Machine Learning

System developers and administrators are heavily dependent on log files for getting the system information in the event of any failure or disaster. Products that are built on layered architecture with multiple components and sub-components generate numerous logs. In case of hitting any error or failures, analyzing huge sets of log files is a daunting task since it often requires coordinated analysis from multiple product groups which might take several days before it is root caused. Machine learning based techniques can be one of the best possible methodology in classifying logs, which can correlate distributed information points while entropies are spread across several log files. It provides an efficient method to triage complex failure issues in lesser time and also capable to detect the appropriate element of failure at profound granular stage. This paper proposes an ML-based method for root cause analysis that proves to be more efficient with higher accuracy.