一种比较基于云的服务缩放算法的方法

IF 5.3 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

IEEE Transactions on Cloud Computing Pub Date : 2024-11-18 DOI:10.1109/TCC.2024.3500139

Danny De Vleeschauwer;Chia-Yu Chang;Paola Soto;Yorick De Bock;Miguel Camelo;Koen De Schepper

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引用次数: 0

摘要

如今，许多服务都是通过云提供的，也就是说，它们依赖于交互软件组件，这些组件可以在位于数据中心的一组连接的商用现货（COTS）服务器上运行。随着对任何特定服务的需求随着时间的推移而发展，与服务相关的计算资源必须相应地进行扩展，同时保持与服务相关的关键性能指标（kpi）处于控制之下。因此，扩展总是涉及到使用云资源和遵守kpi之间的微妙权衡。在本文中，我们展示了（工作量依赖的）帕累托前沿体现了这种权衡的局限性。我们为各种工作负载确定了这个帕累托前沿，并评估了几种缩放算法接近帕累托前沿的能力。

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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A Method to Compare Scaling Algorithms for Cloud-Based Services

Nowadays, many services are offered via the cloud, i.e., they rely on interacting software components that can run on a set of connected Commercial Off-The-Shelf (COTS) servers sitting in data centers. As the demand for any particular service evolves over time, the computational resources associated with the service must be scaled accordingly while keeping the Key Performance Indicators (KPIs) associated with the service under control. Consequently, scaling always involves a delicate trade-off between using the cloud resources and complying with the KPIs. In this paper, we show that a (workload-dependent) Pareto front embodies this trade-off’s limits. We identify this Pareto front for various workloads and assess the ability of several scaling algorithms to approach that Pareto front.

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来源期刊

IEEE Transactions on Cloud Computing Computer Science-Software

CiteScore

9.40

自引率

6.20%

发文量

167

期刊介绍： The IEEE Transactions on Cloud Computing (TCC) is dedicated to the multidisciplinary field of cloud computing. It is committed to the publication of articles that present innovative research ideas, application results, and case studies in cloud computing, focusing on key technical issues related to theory, algorithms, systems, applications, and performance.