Runtime Vertical Scaling of Virtualized Applications via Online Model Estimation
Runtime Vertical Scaling of Virtualized Applications via Online Model Estimation
复制标题
通过在线模型估计虚拟化应用程序的运行时垂直扩展
DOI：
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发表时间：
2014
期刊：
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通讯作者：
Rean Griffith
中科院分区：
文献类型：
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作者：
Simon Spinner;Samuel Kounev;Xiaoyun Zhu;Lei Lu;Mustafa Uysal;Anne M. Holler;Rean Griffith
虚拟化数据中心中的应用程序通常会受到有关其性能（例如延迟或吞吐量）的服务水平目标（SLO）。为了实现这些SLO，有必要为应用程序分配不同类型（CPU，内存，I/O等）的足够资源。但是，应用程序性能与资源分配之间的关系很复杂，取决于多个因素，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，以确保应用程序性能通过运行时间“垂直缩放”（即添加或删除资源）的单个虚拟机（VMS）（VMS）运行该应用程序的运行时有效地符合用户定义的SLO。使用资源需求估计技术自动提取和在线提取和更新了一个描述资源分配与观察到的应用程序性能之间关系的分层性能模型。然后将这种模型用于反馈控制器中，以动态调整单个VM的虚拟CPU数量。我们已经在VMware VSphere平台上实现了控制器，并在使用现实世界中的电子邮件和组件服务器的案例研究中对其进行了评估。实验结果表明，尽管有工作负载需求差异，但我们的方法允许托管应用程序达到SLO满意度，同时避免使用基于最先进的阈值控制器观察到通常观察到的振荡。
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.