This research focuses on the design and implementation of a lightweight, yet, versatile middleware framework that provides effective and scalable solutions to the problem of interleaving storage workloads with a wide spectrum of demands. The framework uses simple and non-intrusive collection of workload statistics such as workload histograms and measures of temporal dependence to provide accurate forecasting of system workload characteristics and their impact on system metrics. The framework maps accurately and swiftly complex processes that exist and interact in storage clusters into robust allocation decisions. Central to the framework is its ability to estimate beforehand the effect of resource allocation policies on system metrics, which enables navigating through multiple possible allocations of system resources and selecting the on that best meets system targets. This research has the potential to revolutionize autonomic resource management in storage systems and provide methodologies to meet conflicting targets such as discovering trade-offs and dependencies between performance and other metrics including cost, energy consumption, reliability, and availability. This project enables enhancement of graduate courses on parallel and distributed systems with aspects of emerging paradigms such as data intensive, cloud, and green computing, as well as advances the education of the multiple students directly involved.
