CSR: Small: Autonomous Performance and Power Control on Virtualized Servers

承载流行互联网服务的现代数据中心在性能和功耗控制方面面临着重大的、多方面的挑战。这些挑战主要是由于复杂的虚拟化计算系统中高度动态和异构工作负载的复杂交互。在本研究项目中，研究人员采用有组织的方法对虚拟化服务器进行自主性能和电源控制。该项目设计和开发自动化、敏捷和可扩展的技术，用于服务器参数调优、虚拟机容量规划、非侵入性节能性能隔离和弹性功率感知资源供应。交付成果是创新和实用的方法和机制，为应用程序提供性能保证，在资源和电力预算的情况下最大限度地提高数据中心的有效系统吞吐量，减轻异构应用程序之间的性能干扰，在保证控制精度和系统稳定性的同时，通过灵活的权衡实现性能和电力目标。该研究方法综合了强化学习、快速在线学习神经网络、模糊逻辑控制、模型预测控制和分布式协调控制的优势。该项目通过在大学原型数据中心中开发一个测试平台来扩展影响，以演示用于虚拟化计算系统、中间件和服务的自治管理的开发方法和机制的编排。该项目招收妇女、美国公民、全日制和非全日制研究生和本科生从事计算机系统研究。这一成功将指导下一代数据中心实现可持续计算的自主资源管理。研究数据存放在主办机构的一个专门网站上，研究结果以出版的技术报告的形式向公众传播。

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.