Enterprise Application Outage Prediction Using XGBoost and LSTM

A large percentage of the global GDP is dependent on the Internet servicing millions of applications, therefore monitoring the health of these applications is critical to businesses around the world. To this date, majority of the mechanisms to support applications have all been reactive in nature; that is, reacting when an application goes down and the business has suffered financial loss. The use of Machine Learning in this space has been very limited until very recently. In this paper, we aim to employ a multi-modal model in determining the health of an application based on several tangible metrics from the infrastructure. Our results indicate that our method is able to predict the health of the application successfully.