Auto-scaling containerized cloud applications: A workload-driven approach

doi:10.1016/j.simpat.2022.102654

2022-12-01

PRE

Spyridon Chouliaras

Stelios Sotiriadis *

DOI：10.1016/j.simpat.2022.102654

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今天，云计算带来了新的商机，因为它提供了各种技术优势，包括弹性计算和有效的定价策略。尽管云用户可以访问大量资源，但是高效地管理云环境中的硬件资源仍然是一项具有挑战性的任务。在本文中，我们介绍了PACE (Performanceaware Auto-scaler for Cloud Elasticity)，这是一个基于工作负载需求自动扩展容器化云应用程序的框架。该框架提供a) 使用基于阈值的规则的反应式自动扩展，以避免在密集的工作负载任务期间发生应用程序故障，以及b) 使用卷积神经网络 (CNN) 和K均值的主动自动扩展，以生成包含未来工作负载需求的弹性扩展策略。实验分析基于Yahoo!在Google Cloud Platform上部署的Redis容器中执行的Cloud Serving Benchmark (YCSB)。所提出的框架可以自动调整云资源以满足工作负载需求并确保服务质量 (QoS) 要求。

Keyword:

Cloud computing

Elastic scaling

Convolutional neural network

K-means

Time series analysis

Machine learning

期刊

Simulation Modelling Practice and Theory

IF:

4.6

论文数:

2.6K

被引数:

4.8K

机构

university of london

学者数:

21.5W

论文数: 19.7W

被引数: 305

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