关键词: Kubernetes, 云原生, 弹性伸缩, 预测, LSTM, 提前感知

Abstract: 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.

Key words: Kubernetes, cloud native, elastic scaling, prediction, LSTM, early perception