LAUSR

Enterprises and service providers are increasingly challenged with improving the quality of service delivery while containing the cost. However, it is often difficult to effectively manage the complex relationships among dynamic customer workloads, strict service level requirements, and efficient service management processes. In this paper, we present our progress on building autonomic systems for IT service management through a collection of automated data driven methodologies. This includes the design of feedback controllers for workload management, the use of simulation-optimization methodology for workforce management, and the development of machine learning models for event management. We demonstrate the applicability of the presented approaches using examples and data from a large IT service delivery environment.