Elastic Expansion Model of Container based on Combination Prediction
Yunchang Cheng, Xiaojie Qu, Hang Li, Pengxiang Gao · 2021
Container management platform needs to allocate resources reasonably for containers running different services. At present, the container mainly allocates resources through the responsive elastic scaling scheme, and the speed of resource adjustment is difficult to guarantee, while the predictive elastic scaling scheme has not reached the actual production requirements in terms of speed and accuracy. In this paper, after studying the existing responsive elastic expansion scheme and predictive elastic expansion scheme, a container elastic expansion scheme based on combined prediction model is proposed. Combining the advantages of ARIMA model and SVM model in short-term time series prediction, a combined prediction algorithm model based on ARIMA and SVM is designed. Compared with the existing prediction algorithm model, the accuracy of the combined prediction algorithm is improved by about 9 percentage points, and the accuracy can reach 92.03%, which provides a strong support for the realization of the elastic expansion of the predictive container.