Recurrent Neural Network Architecture for Communication Log Analysis

Cloud computing infrastructure is an integral part of wireless communication applications, such as vRANs. Given the pervasive use of cloud computing, keeping track of its health is of utmost importance to its providers. Large scale of deployment of this aggravates the complexity. All vRAN based cloud applications generate logs which are raw text messages to reflect run-time execution. Analyzing such log messages and training a model can help an operator timely detect an anomaly, before it leads to service disruption. In this paper, we apply various natural language processing (NLP) models to detect system anomalies by analyzing logs and evaluate their precision and accuracy of anomaly detection. We make use of a machine learning (ML) model in form of a recurrent neural network (RNN) for anomaly detection. We have added attention layer to RNN models like LSTM and GRU models to improve accuracy. We found GRU models with attention are a good fit for logs dataset in cloud environment. Log messages are typically short sentences, linguistically much simpler, and don't suffer from long-range dependencies, hence adding attention layer didn't help for LSTM. GRU has a reduced instruction set, faster training time, hence are much more energy efficient without compromising on anomaly detection accuracy.