Research on the implementation of Kubernetes automatic scaling
Ziheng Ren, Jiangfeng Xu · 2023
Elastic scaling is one of the important features of cloud native. In the actual production environment, because the business requirements and application resource load are in a dynamic change, the deployment of business and allocation of resources can be adjusted in real time through elastic scaling to ensure the overall quality of service of the system. As the current mainstream container orchestration technology, Kubernetes' built-in elastic scaling strategy HPA (Horizontal Pod Autoscaler) obtains the corresponding indicators by monitoring the component metrics and calculates the expected value of the replica by comparing it with the user-defined threshold, thus realizing the elastic scaling function. Although this scaling strategy can solve the problem of dynamic scaling, there are problems of response delay and scaling jitter, which makes the quality of service of the system unable to be guaranteed in a specific scaling period. In view of the above problems, the predictive elastic scaling strategy is improved, and the fuzzy AR (p) time series model is built to predict the specified load indicators, so as to achieve predictive elastic scaling. The experimental results show that this strategy can solve the response delay problem well and reduce the unnecessary jitter scaling.