Jeff Chase, Duke University - December 2009

Within cloud-based infrastructures, many applications can share a set of storage resources, and each application has its own service level objective that should be satisfied within this environment. As workloads change and applications are started, stopped, or moved, the load placed on the storage system changes. The storage system needs to automatically respond to these load changes by adjusting where data is stored and how it is serviced in order to continue to efficiently meet each application’s SLO.This project focuses on performance control for storage-intensive workloads in such a cloud environment. Taking a control-theoretic approach, sensors placed throughout the system can monitor the current performance of various subsystems, and actuators can be used to tune the storage system. One goal of the project is to create control solutions and policies that are modular and non-intrusive, minimizing their assumptions about the system’s internal structure and behavior, including other resource management functions.