基于Kubernetes云原生的弹性伸缩研究  

Research and Practice on Elastic Scaling of Cloud-Native 5G Network

作　　者：赵树君 黄倩[1] ZHAO Shu-jun;HUANG Qian(College of Computer and Information, Hohai University, Nanjing 211100, China)

机构地区：[1]河海大学计算机与信息学院,江苏南京211100

出　　处：《计算机与现代化》2021年第11期28-38,共11页Computer and Modernization

基　　金：国家重点研发计划项目(2018YFC0407905)。

摘　　要：随着云技术的不断发展和普及,为了更好地利用云平台的优点和特性,云原生应用服务不断涌现,如何利用云平台的特性来服务软件设计和开发成为了难题,例如如何利用云平台的弹性伸缩特性。云原生目前主流的容器编排技术Kubernetes支持自动伸缩,却存在一些需要针对具体情况进行优化改进的问题。本文主要针对使用Kubernetes编排的5G核心网网元PCF(Policy Control Function)的水平自动伸缩进行研究,通过基于自定义的负载数据(CPU使用率、内存使用率、交易量、带宽使用率)统计,根据历史负载数据使用LSTM来预测未来的负载,并设计了一种基于预测负载的可行的弹性伸缩算法,从而提出一种提前感知的、弹性的、不影响业务的弹性伸缩方法,并进行了大量的实验和统计,来论证方法的可行性和正确性。As the fast development and wide use of cloud technology,in order to better use the advantages and characteristics of the cloud platform,a lot of cloud-native applications continue to emerge.It is difficult how to solve the problem by using the characteristics of the cloud platform,such as using the elastic scaling.The main container orchestration tool of cloud-native environment is Kubernetes,which supports automatic scaling,but there are some problems that need to be optimized and improved according to specific conditions.This article focuses on researching the horizontal automatic scaling of the 5G network function PCF(Policy Control Function)in Kubernetes environment,by collecting custom customized metrics data(CPU usage,memory usage,transaction count,bandwidth usage),doing a prediction of the future load according to some history load data with LSTM,a feasible elastic scaling algorithm is designed,so as to put forward a method which is perceived in advance,elastic and has no effort on business.A lot of tests and statistics verify the feasibility and correctness of the method.

关 键 词：Kubernetes 云原生 弹性伸缩 预测 LSTM 提前感知 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]