A Framework for Predicting Services Delivery Efforts Using IT Infrastructure-to-Incident Correlation

Predicting IT infrastructure performance under varying conditions, e.g., the addition of a new server or increased transaction loads, has become a typical IT management exercise. However, within a services delivery context, enterprise clients are demanding predictive analytics that outline future “costs” associated with changing conditions. The services delivery staffing costs incurred in addressing problems and requests (arriving in the form of incident and other problem tickets) in the managed environment is especially of high importance. This paper describes a framework and analytical study addressing such cost prediction. Specifically, a novel approach is described in which (1) a framework combining various analytical models is proposed to predict services delivery staffing requirements under changing IT infrastructure characteristics and conditions, and (2) machine learning techniques are used to predict service delivery workloads (measured by ticket volumes) based on managed server characteristics. Detailed descriptions of the workload prediction techniques, as well as an evaluation using data from an actual large service delivery engagement, are presented in this paper