Modern data centers hosting popular Internet services face significant and multi-facet challenges in performance and power control. The challenges are mainly due to complex interaction of highly dynamic and heterogeneous workloads in complex virtualized computing systems. In this research project, the investigators take an organized approach to autonomic performance and power control on virtualized servers. The project designs and develops automated, agile and scalable techniques for server parameter tuning, virtual machine capacity planning, non-invasive energy-efficient performance isolation, and elastic power-aware resource provisioning. The deliverables are innovative and practical approaches and mechanisms that provide performance assurance of applications, maximize effective system throughput of data centers with resources and power budget, mitigate performance interference among heterogeneous applications, and achieve performance and power targets with flexible tradeoffs while assuring control accuracy and system stability. The research methodology integrates strengths of reinforcement learning, fast online learning neural networks, fuzzy logic control, model predictive controls and distributed and coordinated control. The project broadens impact by developing a testbed in a university prototype data center to demonstrate the orchestration of developed approaches and mechanisms for autonomous management of virtualized computing systems, middleware, and services. The project engages women, US citizens, full-time and part-time graduate and undergraduate students in computer system research. The success will guide autonomous resource management for sustainable computing in next-generation data centers. The research data is deposited at a dedicated website at the host institution and research results are disseminated to the public as published technical reports.
