基于Kubernetes的水平弹性扩缩容系统
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摘要
Kubernetes的诞生,降低了应用开发者对基础架构和运维团队的依赖,为其提供了强大的工具来编排和调度容器及虚拟机,已经成为分布式集群系统开发及管理的事实标准。Kubernetes通过核心组件HPA(Horizontal Pod Autoscaler)监测集群中CPU和内存的使用情况,并依据这些指标来对微服务集群进行扩容和缩容。但是这些简单的度量指标不能满足实际应用的扩缩容诉求,虽然在社区的最新版本中已经做出了改进,实际效果依然差强人意。本文在原生平台的基础上,设计一种叠加的弹性扩缩容系统E-HPA,可以通过灵活简易的配置方式提供丰富的用户可定制的度量指标,弥补Kubernetes在水平弹性扩缩容方面的缺陷和不足;阐述该系统的设计思想和具体实现细节,并以电信业务应用为例,验证其实验效果。
The birth of Kubernetes has reduced application developers' reliance on infrastructure and operations teams, it provides powerful tools for orchestrating and scheduling containers and virtual machines, which has become the de facto standard for the development and management of distributed cluster systems. Kubernetes monitors CPU and memory usage in the cluster through the core component HPA(horizontal pod autoscaler), and scales and shrinks the microservice cluster based on these metrics. However, these simple metrics can not meet the expansion and contraction requirements of the actual application. Although the improvements have been made in the latest version of the community, the actual results are still unsatisfactory. Based on the original platform, this paper designs a superimposed elastic expansion and contraction system E-HPA to make up for the defects and shortcomings of Kubernetes in horizontal elastic expansion and contraction, which can provide rich user-customizable metrics through flexible and simple configuration. The design ideas and specific implementation details of the system are expounded, and the experimental results are verified by taking the telecom business application as an example.
The birth of Kubernetes has reduced application developers' reliance on infrastructure and operations teams, it provides powerful tools for orchestrating and scheduling containers and virtual machines, which has become the de facto standard for the development and management of distributed cluster systems. Kubernetes monitors CPU and memory usage in the cluster through the core component HPA(horizontal pod autoscaler), and scales and shrinks the microservice cluster based on these metrics. However, these simple metrics can not meet the expansion and contraction requirements of the actual application. Although the improvements have been made in the latest version of the community, the actual results are still unsatisfactory. Based on the original platform, this paper designs a superimposed elastic expansion and contraction system E-HPA to make up for the defects and shortcomings of Kubernetes in horizontal elastic expansion and contraction, which can provide rich user-customizable metrics through flexible and simple configuration. The design ideas and specific implementation details of the system are expounded, and the experimental results are verified by taking the telecom business application as an example.
引文
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[2] CABALLER M,BLANQUER I,MOLTO G,et al.Dynamic management of virtual infrastructures[J].Journal of Grid Computing,2015,13(1):53-70.
[3] HERBST N R,KOUNEV S,REUSSNER R.Elasticity in cloud computing:What it is,and what it is not[C]// Proceedings of the 10th International Conference on Autonomic Computing.2013:23-27.
[4] NASKOS A,GOUNARIS A,SIOUTAS S.Cloud elasticity:A survey[C]// Proceedings of the 1st International Workshop on Algorithmic Aspects of Cloud Computing.2015:151-167.
[5] YOUSIF M.Microservices[J].IEEE Cloud Computing,2016,3(5):4-5.
[6] 龚正,吴治辉,叶伙荣,等.Kubernetes权威指南[M].北京:电子工业出版社,2016.
[7] BERNSTEIN D.Containers and cloud:From LXC to Docker to Kubernetes[J].IEEE Cloud Computing,2014,1(3):81-84.
[8] PAHL C.Containerization and the PaaS cloud[J].IEEE Cloud Computing,2015,2(3):24-31.
[9] SILL A.The design and architecture of microservices[J].IEEE Cloud Computing,2016,3(5):76-80.
[10] DUALVEN_in_CSDN.Kubernetes和OpenStack到底是什么关系?[EB/OL].(2018-01-30)[2018-12-15].https://blog.csdn.net/dualvencsdn/article/details/79207281.
[11] Kubernetes中文社区.IT专家们谈OpenStack和Kubernetes的未来[EB/OL].(2017-05-15)[2018-12-15].https://www.kubernetes.org.cn/1999.html.
[12] 云栖社区.2017 OpenStack峰会:K8s抢尽风头?[EB/OL].(2017-05-11)[2018-12-15].https://yq.aliyun.com/articles/80116.
[13] LUYEGANG.OpenStack、Kubernetes、Mesos谁主沉浮?[EB/OL].(2017-03-28)[2018-12-15].https://blog.csdn.net/luyegang1/article/details/67639501.
[14] AI W,LI K L,LAN S L,et al.On elasticity measurement in cloud computing[J].Scientific Programming,2016,2016,DOI:10.1155/2016/7519507.
[15] ISLAM S,LEE K,FEKETE A,et al.How a consumer can measure elasticity for cloud platforms[C]// Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering.2012:85-96.
[16] GitHub.Prometheus[EB/OL].[2018-12-15].https://github.com/prometheus.
[17] NEWMAN S.微服务设计[M].岳新欣,译.北京:人民邮电出版社,2016.
[18] 杨茂.基于Kubernetes的容器自动伸缩技术的研究[D].西安:西安邮电大学,2018.
[19] 台慧敏.基于Kubernetes-on-EGO的两级资源调度器的设计与实现[D].西安:西安电子科技大学,2017.
[20] 徐凯.基于Kubernetes的伸缩性分布式资源调度器的设计与实现[D].西安:西安电子科技大学,2018.
[21] Linux Foundation.Kubernetes[EB/OL].[2018-12-15].https://kubernetes.io/.
[22] HAMILTON JD.时间序列分析[M].夏晓华,译.北京:中国人民大学出版社,2015.
[23] 王长江.指数平滑法中平滑系数的选择研究[J].中北大学学报(自然科学版),2006,27(6):558-561.
[24] 刘思峰,杨英杰.灰色系统研究进展(2004-2014)[J].南京航空航天大学学报,2015,47(1):1-18.