Autonomic resource provisioning for cloud-based software

Pooyan Jamshidi, Aakash Ahmad, C. Pahl
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引用次数: 164

Abstract

Cloud elasticity provides a software system with the ability to maintain optimal user experience by automatically acquiring and releasing resources, while paying only for what has been consumed. The mechanism for automatically adding or removing resources on the fly is referred to as auto-scaling. The state-of-the-practice with respect to auto-scaling involves specifying threshold-based rules to implement elasticity policies for cloud-based applications. However, there are several shortcomings regarding this approach. Firstly, the elasticity rules must be specified precisely by quantitative values, which requires deep knowledge and expertise. Furthermore, existing approaches do not explicitly deal with uncertainty in cloud-based software, where noise and unexpected events are common. This paper exploits fuzzy logic to enable qualitative specification of elasticity rules for cloud-based software. In addition, this paper discusses a control theoretical approach using type-2 fuzzy logic systems to reason about elasticity under uncertainties. We conduct several experiments to demonstrate that cloud-based software enhanced with such elasticity controller can robustly handle unexpected spikes in the workload and provide acceptable user experience. This translates into increased profit for the cloud application owner.
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基于云的软件的自主资源配置
云弹性为软件系统提供了通过自动获取和释放资源来维持最佳用户体验的能力,而只需为已消耗的资源付费。动态自动添加或删除资源的机制称为自动缩放。关于自动伸缩的实践状态涉及指定基于阈值的规则,以实现基于云的应用程序的弹性策略。然而,这种方法有几个缺点。首先,弹性规则必须用定量值精确地表示,这需要深厚的知识和专业知识。此外,现有的方法并没有明确地处理基于云的软件中的不确定性,其中噪音和意外事件是常见的。本文利用模糊逻辑对基于云的软件弹性规则进行定性描述。此外,本文还讨论了一种利用2型模糊逻辑系统对不确定条件下弹性进行推理的控制理论方法。我们进行了几个实验,以证明使用这种弹性控制器增强的基于云的软件可以健壮地处理工作负载中的意外峰值并提供可接受的用户体验。这将转化为云应用程序所有者增加的利润。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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