The Application of Neural Networks to Predicting the Root Cause of Service Failures

摘要

The principal objective when monitoring compute and communications infrastructure is to minimize the Mean Time To Resolution of service-impacting incidents. Key to achieving that goal is determining which of the many alerts that are presented to an operator are likely to be the root cause of an incident. In turn this is critical in identifying which alerts should be investigated with the highest priority. Noise reduction techniques can be employed to reduce the quantity of alerts a network operator needs to examine but even in favorable scenarios there may be multiple candidate alerts that need to be investigated before the root cause of the incident can be accurately identified, resolved and full service resumed. The current contribution describes a novel technique, Probable Root Cause, that applies supervised machine learning in the form of Neural Networks to determine the alerts most likely to be responsible for a service-impacting incident. An evaluation of different models and model parameters is presented. The effectiveness of the approach is demonstrated against sample data from a large commercial environment.