HoloScale:云资源的水平和垂直缩放

Victor Millnert, Johan Eker
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引用次数: 4

摘要

弹性和可伸缩的计算资源是云计算的基本组成部分。为了提供高质量的服务和应用程序,对云资源的有效管理至关重要。在这项工作中,我们提出了一种新的方法来扩展云资源并提供稳定性保证。我们利用了经典控制理论的思想和概念,即中程控制,并以一种新颖的方式将水平缩放和垂直缩放结合起来。水平扩展通常是指添加/删除整个资源单元(例如,虚拟机或容器),而垂直扩展是指增加/缩小已经分配的资源(例如,使已部署的虚拟机更大/更小)。每种方法都有自己的优缺点:1)水平扩展通常是缓慢和粗粒度的,但可以扩展到很大的范围;2)垂直扩展通常是快速和平滑的,但范围有限。提出的算法被称为HoloScale,它利用了两种缩放机制的优点,而没有缺点。该方法具有平滑、快速、范围大的特点。通过使用控制理论的核心概念,我们证明了HoloScale算法管理的系统在存在时变尺度延迟时是稳定的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
HoloScale: horizontal and vertical scaling of cloud resources
Elastic and scalable compute resources are a fundamental part of cloud computing. Efficient management of cloud resources is crucial in order to provide high quality services and applications. In this work we present a novel method for scaling cloud resources and provide stability guarantees. We do this by leveraging ideas and concepts from classic control theory, namely mid-range control and combine horizontal scaling and vertical scaling in a novel way. Horizontal scaling is typically when one adds/removes whole unites of resources (e.g., virtual machines or containers), while vertical scaling is when one grows/shrinks already allocated resources (e.g., making a deployed virtual machine larger/smaller). Each methods has their own trade-offs: i) horizontal scaling is often slow and coarse-grained, but can scale over a large range, and ii) vertical scaling is often quick and smooth, but has limited range.The proposed algorithm is called HoloScale, which leverages the strengths of both scaling mechanisms, without the drawbacks. The method is capable of scaling smoothly, quickly, and over a large range. By using core concepts from control theory, we show that systems managed by the HoloScale algorithm are stable in the presence of time-varying scaling delays.
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