Development of Novel Ensemble Machine Learning Architecture for Forecasting Unresponsive Server State

Server unresponsiveness is a very big issue in software industry in a production environment. It is a very challenging job for the production team to utilize the server for top priority tasks making sure that these tasks are completed in time without the server going into hung or unresponsive state. This state usually happens when there is an overload on the server with services and tasks. When the server goes into unresponsive state, the applications and services go into an abrupt halt, only after the server is restarted, the applications and the services could be started again, but usually all the progress before the restart is lost. In this research work, a novel ensemble machine learning approach has been proposed here to predict the rough time when the server could go to an unresponsive state, keeping in mind the different factors responsible and thus avoiding any potential outage.