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HoloScale: horizontal and vertical scaling of cloud resources

机译:Holoscale:云资源的水平和垂直缩放

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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.
机译:弹性和可扩展的计算资源是云计算的基本部分。云资源的有效管理至关重要,以提供高质量的服务和应用。在这项工作中,我们提出了一种用于缩放云资源并提供稳定性保证的新方法。我们通过利用经典控制理论的思路和概念来实现这一目标,即中档控制,并以新颖的方式结合水平缩放和垂直缩放。水平缩放通常是当一个增加/删除资源(例如,虚拟机或容器)的整体相联,而垂直缩放是当一个生长/收缩已经分配的资源(例如,使得部署的虚拟机更大/更小)。每个方法都有自己的权衡:1)水平缩放往往是缓慢和粗粒度的,但可以扩展在大范围内,以及ii)垂直缩放往往是快速而平稳,但限于range.The算法被称为Holoscale,它利用了两个缩放机制的优势,没有缺点。该方法能够快速,快速地缩放,并且在大范围内进行缩放。通过使用来自控制理论的核心概念,我们表明,在存在时变缩放延迟的情况下,由Holoscale算法管理的系统是稳定的。

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