虚拟化数据中心中多层应用的电源优化和性能保证

Yefu Wang, Xiaorui Wang
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引用次数: 41

摘要

现代数据中心必须为多层web应用等复杂系统软件提供性能保证。此外,为了降低运营成本和避免系统过热,需要将数据中心的功耗降至最低。基于动态电压和频率缩放(DVFS)的各种节能性能管理策略已经被提出。虚拟化技术还使将多个虚拟机(vm)合并到数量较少的活动物理服务器上成为可能,从而实现更大的节能,但代价是更高的开销。针对具有多层应用的虚拟化数据中心,提出了一种性能可控的电源优化方案。虽然现有的工作以不同的方式依赖于DVFS或服务器整合,但我们的解决方案通过集成反馈控制和优化策略,利用这两种策略来最大限度地节省电力。在应用程序级别,设计一个多输入多输出控制器,通过重新分配CPU资源和DVFS,在短时间内为跨多个vm的应用程序实现所需的性能。在数据中心级别,建议使用一个电源优化器,以便在较长的时间范围内逐步将vm整合到最节能的服务器上。在硬件测试平台上的经验结果表明,我们的解决方案可以有效地实现性能保证的节能。基于5,415个真实服务器的跟踪文件的广泛仿真结果证明了我们的解决方案在大型数据中心中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Power Optimization with Performance Assurance for Multi-tier Applications in Virtualized Data Centers
Modern data centers must provide performance assurance for complex system software such as multi-tier web applications. In addition, the power consumption of data centers needs to be minimized to reduce operating costs and avoid system overheating. Various power-efficient performance management strategies have been proposed based on dynamic voltage and frequency scaling (DVFS). Virtualization technologies have also made it possible to consolidate multiple virtual machines (VMs) onto a smaller number of active physical servers for even greater power savings, but at the cost of a higher overhead. This paper proposes a performance-controlled power optimization solution for virtualized data centers with multi-tier applications. While existing work relies on either DVFS or server consolidation in a separate manner, our solution utilizes both strategies for maximized power savings by integrating feedback control with optimization strategies. At the application level, a multi-input-multi-output controller is designed to achieve the desired performance for applications spanning multiple VMs, on a short time scale, by reallocating the CPU resources and DVFS. At the data center level, a power optimizer is proposed to incrementally consolidate VMs onto the most power-efficient servers on a longer time scale. Empirical results on a hardware testbed demonstrate that our solution can effectively achieve performance-assured power savings. Extensive simulation results, based on a trace file of 5,415 real servers, demonstrate the efficacy of our solution in large-scale data centers.
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