Research on the implementation of Kubernetes automatic scaling

Ziheng Ren; Jiangfeng Xu

doi:10.1117/12.2674779

2 May 2023 Research on the implementation of Kubernetes automatic scaling

Ziheng Ren, Jiangfeng Xu

Proceedings Volume 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023); 126422Z (2023) https://doi.org/10.1117/12.2674779
Event: Second International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT 2023), 2023, Xishuangbanna, China

Abstract

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.

Citation Download Citation

Ziheng Ren and Jiangfeng Xu "Research on the implementation of Kubernetes automatic scaling", Proc. SPIE 12642, Second International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2023), 126422Z (2 May 2023); https://doi.org/10.1117/12.2674779

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KEYWORDS

Autoregressive models

Elasticity

Fuzzy logic

Data modeling

Clouds

Astatine

Cloud computing

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