虚拟机亲和性感知动态固定调度

Zhi Li, Yuebin Bai, Huiyong Zhang, Yao Ma
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引用次数: 15

摘要

虚拟化为服务器整合提供了有效的管理。这种透明性使得不同类型的服务器可以在同一个平台上运行,充分利用硬件资源。然而,虚拟化引入了两个级别的调度器:一个来自Guest操作系统,任务被调度到虚拟cpu (vcpu),另一个来自虚拟机监视器(VMM), vcpu被调度到cpu。因此,低级调度器对任务信息一无所知,因此在某些情况下,它无法为每个来宾操作系统分配适当比例的CPU资源。提出了一种亲和感知的动态固定调度调度程序(DP-Scheduling)。我们的目标有两个:一是弥合Guest OS和VMM之间的语义差距;二是引入一种affinity-aware方法,向VMM提供CPU affinity的任务信息;三是提出一种新的调度方法DP-Scheduling,使VCPU可以动态地固定或解除固定在一个CPU的运行队列上。为此,我们首先从VMM的角度获取进程描述符的机器地址(MA)。亲和性信息也是在启用任务运行之前获取的。为了确认关联信息,DP-Scheduling调用我们提供的API。根据关联信息,我们提出了一系列实现动态绑定和保持工作负载平衡的措施。所有实现都局限于Xen VMM和Credit调度器。我们的实验表明,通过测试cpu绑定任务的各种指标,dp调度优于信用调度,而不会干扰负载平衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Affinity-Aware Dynamic Pinning Scheduling for Virtual Machines
Virtualization provides an effective management in server consolidation. The transparence enables different kinds of servers running in the same platform, making full use of hardware resource. However, virtualization introduces two-level schedulers: one from Guest OS, where the tasks are scheduled to virtual CPUs (VCPUs), the other from the virtual machine monitor (VMM), where VCPUs are scheduled to CPUs. As a result, the lower level scheduler is ignorant of the task information so that it cannot allocate appropriate proportion of CPU resource for every Guest OS in some cases. This paper presents an affinity-aware Dynamic Pinning Scheduling scheduler (DP-Scheduling). We aim at two objects: Bridging the semantic gap between Guest OS and VMM, introducing an affinity-aware method and providing the tasks information about CPU affinity to VMM, Bringing up a novel scheduling, DP-Scheduling, so that VCPU can be pinned or unpinned on one CPU¡¯s running queue dynamically. For this purpose, we first get the Machine Address (MA) of process descriptor from the angle of VMM. The affinity information is also acquired before the task is enabled to run. To acknowledge the affinity information, DP-Scheduling calls an API provided by us. Depending on the affinity information, we put forward a series of measures to implement pinning dynamically as well as to keep workload balance. All implementation is confined to Xen VMM and Credit scheduler. Our experiments demonstrate that DP-Scheduling outperforms Credit scheduling by testing various indicators for CPU-bound tasks, without interfering the load balance.
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