Brian J. Watson

TL;DR: A high level overview of a virtual machine placement system in which an autonomic controller dynamically manages the mapping of virtual machines onto physical hosts in accordance with policies specified by the user is presented.

TL;DR: An automated capacity and workload management system that integrates multiple resource controllers at three different scopes and time scales is described and results confirm that such an integrated solution ensures efficient and effective use of data center resources while reducing service level violations for high priority applications.

TL;DR: This paper describes an automated capacity and workload management system that integrates multiple resource controllers at three different scopes and time scales and confirms that such an integrated solution ensures efficient and effective use of data center resources while reducing service level violations for high priority applications.

TL;DR: This paper examines the probabilistic relationships between virtualized CPU allocation, CPU contention, and application response time, to enable autonomic controllers to satisfy service level objectives (SLOs) while more effectively utilizing IT resources.

TL;DR: The empirical results show that AppRAISE can effectively allocate CPU resources to application components of multiple applications to meet end-to-end mean response time targets in the presence of variable workloads, while maintaining reasonable trade-offs between application performance, resource efficiency, and transient behavior.