Univariate vs multivariate prediction for containerised applications auto-scaling: a comparative study

Adaptive containerised systems have been developed using the Time Series Forecasting (TSF) technique. TSF analyses historical data patterns to estimate future trends, assuming they will occur again. Identifying future trends allows anticipating problems (e.g., high latency) and acting (e.g., replicating the service) to fix them before they occur. Depending on the number of features (i.e., metrics) used as input for prediction, TSF can be classified as univariate (single feature) or multivariate (two or more features). Despite the popularity of both TSF strategies, a unique strategy is typically implemented, and there is no comparison with the other. However, it is known that no strategy is the best choice for all possible scenarios. This paper presents a comparative study assessing univariate and multivariate proactive auto-scaling of containerised applications. A custom-made multivariate auto-scaling tool called Multivariate Forecasting Tool (MFT) was developed and compared with a production-grade univariate system called Predict Kube (PK). Both applications were evaluated using four popular open-source benchmark applications. The results show that the multivariate strategy decreased the response time of the evaluated applications in 75% of the experiments (i.e., 9 out of 12) compared to the univariate, and it was more cost-effective in half of them (i.e., 6 out of 12). Furthermore, they also indicate that the multivariate strategy efficiency is more significant as the number of containers composing the application increases. This comparative study is expected to be a helpful guide for developers who want to choose the most effective proactive approach for their auto-scaling solutions.