VMware的ESXi Hypervisor中的Power Aware NUMA Scheduler

Qasim Ali, Haoqiang Zheng, Tim Mann, Raghunathan Srinivasan
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引用次数: 6

摘要

虚拟化平台已经成为云计算的顶级解决方案,特别是在当今电力受限的数据中心中。虚拟化允许将物理机替换为虚拟机(vm),然后将其整合到数量较少的物理主机上,从而有助于节省电力和能源。上电的物理主机数量甚至可以动态变化,就像VMware的分布式电源管理(DPM)功能一样。在较低的级别上,管理每个单独主机内的电源使用仍然很有价值,典型的系统,包括VMware的ESXi管理程序,通过根据当前工作负载的需求调整每个处理器的p状态(频率和电压状态)和cstate(空闲状态)来做到这一点。然而,在目前的NUMA系统中,有一种中间水平的电源管理可能尚未得到很大程度的探索。在本文中,我们建议在启用NUMA的系统上优化虚拟机的位置,以便在对VM性能影响最小的情况下降低虚拟化系统的总体能耗。我们的启发式方法利用了一个相对较新的CPU硬件特性,称为独立包c状态。据我们所知,本文介绍了通过利用独立的包c状态使NUMA调度器具有功率感知的第一项工作。我们在ESXi中实现了一个简单的启发式方法,通过使用四个实际工作负载和两个微基准测试,观察到节能高达26%,能效提高高达30%。
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Power Aware NUMA Scheduler in VMware's ESXi Hypervisor
Virtualized platforms have emerged as the top solution for cloud computing, especially in today's power-constrained data centers. Virtualization helps save power and energy by allowing physical machines to be replaced by virtual machines (VMs) and then consolidated onto a smaller number of physical hosts. The number of physical hosts that are powered on can even be dynamically varied, as with VMware's Distributed Power Management (DPM) feature. At a lower level, it remains valuable to manage power usage within each individual host, and typical systems, including VMware's ESXi hypervisor, do so by adjusting each processor's P-states (frequency and voltage states) and Cstates (idle states) according to the demands of the current workload. With current NUMA systems, however, there is an intermediate level of power management possible that has gone largely unexplored. In this paper we propose to optimize the placement of virtual machines on NUMA enabled systems, such that the overall energy consumption of the virtualized system is reduced with minimal impact on VM performance. Our heuristics exploit a relatively new CPU hardware feature, called independent package C-states. To the best of our knowledge, this paper presents the first work on making a NUMA scheduler power-aware by exploiting independent package C-states. We implemented a simple heuristic in ESXi and observed power savings of up to 26% and energy efficiency improvements of up to 30% using four realistic workloads and two micro-benchmarks.
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