基于Kubernetes云平台的弹性伸缩方案设计与实现

doi:10.19678/j.issn.1000-3428.0056560

计算机工程 ›› 2021, Vol. 47 ›› Issue (1): 312-320. doi: 10.19678/j.issn.1000-3428.0056560

• 开发研究与工程应用 • 上一篇

基于Kubernetes云平台的弹性伸缩方案设计与实现

单朋荣¹, 杨美红¹, 赵志刚¹, 李志鹏², 杨丽娜¹

1. 齐鲁工业大学(山东省科学院) 山东省计算中心(国家超级计算济南中心) 山东省计算机网络重点实验室, 济南 250000;
2. 同济大学电子信息工程学院, 上海 201804

收稿日期:2019-11-11 修回日期:2020-01-07 发布日期:2020-01-10
作者简介:单朋荣(1995-),男,硕士研究生,主研方向为云计算、软件工程;杨美红,研究员;赵志刚,副研究员;李志鹏,博士研究生;杨丽娜,硕士研究生。
基金资助:
国家重点研发计划（2018YFB0203903）。

Design and Implementation of Elastic Scaling Scheme Based on Kubernetes Cloud Platform

SHAN Pengrong¹, YANG Meihong¹, ZHAO Zhigang¹, LI Zhipeng², YANG Lina¹

1. Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center(National Supercomputer Center in Jinan), Qilu University of Technology(Shandong Academy of Sciences), Jinan 250000, China;
2. School of Electronic Information Engineering, Tongji University, Shanghai 201804, China

Received:2019-11-11 Revised:2020-01-07 Published:2020-01-10

摘要/Abstract

摘要： 容器技术可为多个业务需求及其依赖组件提供独立的应用资源，在现实生产环境中由于容器中的业务需求不断变化，使得与其对应的应用资源在线负载处于动态变化中，面临固定资源容量规划不能满足在线负载变化的困境。为解决该问题，设计一种基于Kubernetes云平台的弹性伸缩方案。该方案通过集成Prometheus监控系统来自定义指标与采集业务指标，并结合HPA、VPA等组件，实现包括自定义指标和不同维度伸缩方法相结合的最佳弹性伸缩方法。通过集成Grafana页面显示和报警等组件，实现实时查看弹性伸缩状态变化以及伸缩预警功能，以实时观测集群健康状态，使得集群操作更加友好、便于维护。实验结果表明，在不同压力测量测试下，该弹性伸缩方案具有随负载增加扩大集群规模的作用，能够增强应用集群的高可用能力。

关键词: 云平台, Kubernetes技术, 弹性伸缩, Prometheus系统, HPA组件

Abstract: Container technology can provide independent application resources for multiple service requirements and their dependent components.In the real production scenarios,the online loads of application resources are always in change along with the continuously changing service requirements in the container,so the fixed resource capacity planning fail to meet the changing requirements of online loads.In order to solve the problem,this paper designs an elastic scaling scheme based on the Kubernetes cloud platform.This scheme integrates the Prometheus monitoring system to customize the indicators and collect service indicators,and combines Horizontal Pod Autoscaler(HPA),Vertical Pod Autoscaler(VPA) and other components to realize the optimal elastic scaling method that is capable of both customizing indicators and scaling in different dimensions.Furthermore,by integrating Grafana page display, alarm and other components,this scheme realizes the real-time view of elastic scaling state changes and early warning function for scaling,so as to observe the health state of the cluster in real time,making the cluster operations more friendly and easy to maintain.Experimental results show that under different pressure measurement and tests,the elastic scaling scheme is capable of increasing the cluster size along with the increase of loads,and can enhance the high availability of the application cluster.

Key words: cloud platform, Kubernetes technology, elastic scaling, Prometheus system, HPA components

中图分类号:

TP391

单朋荣, 杨美红, 赵志刚, 李志鹏, 杨丽娜. 基于Kubernetes云平台的弹性伸缩方案设计与实现[J]. 计算机工程, 2021, 47(1): 312-320.

SHAN Pengrong, YANG Meihong, ZHAO Zhigang, LI Zhipeng, YANG Lina. Design and Implementation of Elastic Scaling Scheme Based on Kubernetes Cloud Platform[J]. Computer Engineering, 2021, 47(1): 312-320.

http://www.ecice06.com/CN/Y2021/V47/I1/312

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参考文献

[1] ANDERSON C.Docker[J].IEEE Software,2015,32(3):102-103.
[2] Kubernetes Community.Kubernetes official website[EB/OL].[2019-10-05].https://kubernetes.io/docs/concepts.
[3] BURNS B,GRANT B,OPPENHEIMER D,et al.Borg,omega,and Kubernetes[J].Queue,2016,14(1):70-93.
[4] DIKALEH S,SHEIKH O,FELIX C.Introduction to Kubernetes[C]//Proceedings of the 27th Annual International Conference on Computer Science and Software Engineering.New York,USA:ACM Press,2017:310-310.
[5] GONG Zheng.Kubernetes authoritative guide[M].4th ed.Beijing:Electronic Industry Press,2019.(in Chinese)龚正.Kubernetes权威指南[M].4版.北京:电子工业出版社,2019.
[6] ROSADO T,BERNARDINO J.An overview of Openstack architecture[C]//Proceedings of the 18th International Database Engineering & Applications Symposium.New York,USA:ACM Press,2014:366-367.
[7] SAHA P,GOVINDARAJU M,MARRU S,et al.Integrating Apache Airavata with Docker,Marathon,and Mesos[J].Concurrency and Computation:Practice and Experience,2016,28(7):1952-1959.
[8] YU Jingang,ZHAI Yarong,YU Bo,et al.Research and application of auto-scaling unified communication server based on Docker[C]//Proceedings of the 10th International Conference on Intelligent Computation Technology and Automation.Washington D.C.,USA:IEEE Press,2017:152-156.
[9] YANG Mao.Research on container auto-scaling based on Kubernetes[D].Xian:Xidian University,2018.(in Chinese)杨茂.基于Kubernetes的容器自动伸缩技术的研究[D].西安:西安电子科技大学,2018.
[10] Compute resource usage analysis and monitoring of container clusters[EB/OL].[2019-10-05].https://github.com/kubernetes-retired/heapster.
[11] BRATTSTROM M,MORREALE P.Scalable agentless cloud network monitoring[C]//Proceedings of the 4th IEEE International Conference on Cyber Security and Cloud Computing.Washington D.C.,USA:IEEE Press,2017:171-176.
[12] TU Xuezhen,YANG Haichao.A horizontal elastic expansion and contraction system based on Kubernetes[J].Computer and Modernization,2019(7):25-31.(in Chinese)屠雪真,杨海潮.基于Kubernetes的水平弹性扩缩容系统[J].计算机与现代化,2019(7):25-31.
[13] ALTAF U,JAYAPUTERA G,LI J,et al.Auto-scaling a defence application across the cloud using Docker and Kubernetes[C]//Proceedings of IEEE/ACM International Conference on Utility and Cloud Computing Companion.Washington D.C.,USA:IEEE Press,2018:327-334.
[14] CHEN Xiaoyu.Detailed principles,applications,source code and expansion of Promethues[M].Beijing:Electronic Industry Press,2019.(in Chinese)陈晓宇.Promethues原理、应用、源码与拓展详情[M].北京:电子工业出版社,2019.
[15] CARDENAS Y M R.Scaling policies derivation for predictive autoscaling of cloud applications[EB/OL].[2019-10-10].https://mediatum.ub.tum.de/1462656.
[16] ALHAMAZANI K,RANJAN R,MITRA K,et al.CLAMS:cross-layer multi-cloud application monitoring-as-a-service framework[C]//Proceedings of 2014 IEEE International Conference on Services Computing.Washington D.C.,USA:IEEE Press,2014:283-290.
[17] ABUSINNAIN A K,MOKHTAR T A,YOUSEF M A,et al.Web-based application performance monitoring tool[EB/OL].[2019-10-10].http://repository.sustech.edu/handle/123456789/21332.
[18] VAYGHAN L A,SAIED M A,TOEROE M,et al.Microservice based architecture:towards high-availability for stateful applications with Kubernetes[C]//Proceedings of the19th IEEE International Conference on Software Quality,Reliability and Security.Washington D.C.,USA:IEEE Press,2019:176-185.
[19] JASSAT U F.Point 5 compliant Kubernetes cluster[M].Geneva,Swiss Confederation:[s.n.],2019.
[20] LIU Biao,WANG Baosheng,DENG Wenping.Containerized workflow framework supporting elastic scaling in cloud environment[J].Computer Engineering,2019,45(3):7-13.(in Chinese)刘彪,王宝生,邓文平.云环境下支持弹性伸缩的容器化工作流框架[J].计算机工程,2019,45(3):7-13.

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