Quantitative Evaluation of Cloud Elasticity based on Fuzzy Analytic Hierarchy Process

Bolin Yang, Fan Zhang, S. Khan
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Abstract

Elasticity is one of the most important cloud computing characteristics, which enables deployed applications to dynamically adapt to workload-changing demands by acquiring and releasing shared computing resources at runtime. However, the existing cloud elasticity metrics are either oversimplified or hard to use, thereby lacking a comprehensive evaluation mech-anism to properly compare the elastic feature among different cloud providers. To address this gap, we propose an assessment method for cloud elasticity based on fuzzy hierarchical analysis. We use a fuzzy hierarchical model to quantitatively assess the qualitative metrics with a unified standard model. We compare three public cloud providers (Ali Cloud, HUAWEI Cloud, Tencent Cloud) as case studies and measure their cloud elasticity based on the proposed model on a cluster. To verify the effectiveness of our method, we also measure three cloud platforms using auto scaling performance metrics proposed by SPEC Cloud Group. The results show that our proposed elasticity quantification method is feasible.
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基于模糊层次分析法的云弹性定量评价
弹性是最重要的云计算特性之一,它使部署的应用程序能够通过在运行时获取和释放共享计算资源来动态适应工作负载变化的需求。然而,现有的云弹性指标要么过于简化,要么难以使用,从而缺乏一种全面的评估机制来正确比较不同云提供商之间的弹性特性。为了解决这一差距,我们提出了一种基于模糊层次分析的云弹性评估方法。采用模糊层次模型对定性指标进行定量评价,统一标准模型。我们比较了三家公共云提供商(阿里云、华为云、腾讯云)作为案例研究,并基于所提出的模型在集群上度量它们的云弹性。为了验证我们方法的有效性,我们还使用SPEC cloud Group提出的自动缩放性能指标对三个云平台进行了测量。结果表明,本文提出的弹性量化方法是可行的。
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