基于在线模型估计的虚拟化应用运行时垂直扩展

Simon Spinner, Samuel Kounev, Xiaoyun Zhu, Lei Lu, Mustafa Uysal, Anne M. Holler, Rean Griffith
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引用次数: 49

摘要

虚拟化数据中心中的应用程序在性能(例如,延迟或吞吐量)方面经常受到服务水平目标(slo)的约束。为了实现这些slo,有必要为应用程序分配足够的不同类型的资源(CPU、内存、I/O等)。但是,应用程序性能和资源分配之间的关系是复杂的,并且取决于多种因素,包括应用程序体系结构、系统配置和工作负载需求。在本文中,我们提出了一种基于模型的方法,通过运行应用程序的单个虚拟机(vm)的运行时“垂直扩展”(即添加或删除资源)来确保应用程序性能有效地满足用户定义的SLO。描述资源分配和观察到的应用程序性能之间关系的分层性能模型使用资源需求估计技术自动提取和在线更新。然后在反馈控制器中使用这样的模型来动态地调整单个vm的虚拟cpu数量。我们在VMware vSphere平台上实现了控制器,并在使用真实电子邮件和群件服务器的案例研究中对其进行了评估。实验结果表明,我们的方法允许托管应用程序在工作负载需求变化的情况下实现SLO满足,同时避免了最先进的基于阈值的控制器通常观察到的振荡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Runtime Vertical Scaling of Virtualized Applications via Online Model Estimation
Applications in virtualized data centers are often subject to Service Level Objectives (SLOs) regarding their performance (e.g., latency or throughput). In order to fulfill these SLOs, it is necessary to allocate sufficient resources of different types (CPU, memory, I/O, etc.) to an application. However, the relationship between the application performance and the resource allocation is complex and depends on multiple factors including application architecture, system configuration, and workload demands. In this paper, we present a model-based approach to ensure that the application performance meets the user-defined SLO efficiently by runtime "vertical scaling" (i.e., adding or removing resources) of individual virtual machines (VMs) running the application. A layered performance model describing the relationship between the resource allocation and the observed application performance is automatically extracted and updated online using resource demand estimation techniques. Such a model is then used in a feedback controller to dynamically adapt the number of virtual CPUs of individual VMs. We have implemented the controller on top of the VMware vSphere platform and evaluated it in a case study using a real-world email and groupware server. The experimental results show that our approach allows the managed application to achieve SLO satisfaction in spite of workload demand variation while avoiding oscillations commonly observed with state-of-the-art threshold-based controllers.
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