A Dynamic Interval Auto-Scaling Optimization Method Based on Informer Time Series Prediction

With the rapid development and application of container cloud computing-related technologies, more and more applications are being deployed to container cloud clusters. As an essential feature of container cloud platforms and cloud-native architecture, auto-scaling aims to automatically and quickly adjust the allocation of cloud resources according to the resource requirements of applications. Currently, widely used responsive auto-scaling methods, such as Kubernetes HPA, exhibit certain lags due to the startup time costs of containers and Pods. This lag makes it difficult to guarantee the service quality of applications when there is a sudden increase in online application load. This paper proposes a dynamic interval auto-scaling optimization method based on Informer time series prediction. By predicting online application load and dynamically determining the auto-scaling interval, sufficient resources are allocated to the application in advance. In the experiments conducted on the official World Cup forum load and Alibaba cluster CPU load, the Informer time series prediction algorithm demonstrated better long-sequence time series prediction capabilities compared to algorithms such as LSTM and RNN. In elastic scaling experiments, compared to Kubernetes HPA, the method proposed in this paper reduces the average application response time from 0.821 seconds to 0.692 seconds, and the SLA violation rate decreases from 18.277% to 9.157%. This indicates a significant improvement in the service quality metrics of online applications. Furthermore, the proposed method effectively maintains a balance between high CPU resource utilization and low application response time and SLA violation rate, which is something RNN-based elastic scaling method cannot achieve, as it can only reduce application response time and SLA violation rate by sacrificing CPU resource utilization.