Classifying server behavior and predicting impact of modernization actions

Abstract

Today the decision of when to modernize which elements of the server HW/SW stack is often done manually based on simple business rules. In this paper we alleviate this problem by supporting the decision process with an automated approach based on incident tickets and server attributes data. As a first step we identify and rank servers with problematic behavior as candidates for modernization using a random forest classifier. Second, this predictive model is used to evaluate the impact of different modernization actions and suggest the most effective ones. We show that our chosen model yields high quality predictions and outperforms traditional linear regression models on a large set of real data.