Effective resource and workload management in data centers

Abstract

The increasing demand for storage, computation, and business continuity has driven the growth of large data centers. Managing data centers efficiently is a difficult task because of the wide variety of datacenter applications, their ever-changing intensities, and the fact that application performance targets may differ widely. Server virtualization has been a game-changing technology for IT, providing the possibility to support multiple virtual machines (VMs) simultaneously. This dissertation focused on how virtualization technologies can be utilized to develop new tools for maintaining high resource utilization, for achieving high application performance, and for reducing the cost of data center management. This dissertation first focused on application workload management to improve web service performance especially under bursty conditions. Secondly, it concentrated on a resource measurement problem which serves as the basis of many autonomic computing solutions such as system optimization, adaptation, and troubleshooting. Thirdly, it presented AppRM, a resource allocation system that autonomically adapts to dynamic workload changes in a shared virtualized infrastructure to achieve application service level objectives (SLOs). Last, this dissertation presented PREMATCH, a tool that best co-locate different virtual machines (VMs) such that the performance of co-located VMs is maximized. All works have been implemented and tested over real enterprise applications and were accepted for publication at leading autonomic management conferences.