Microservices performance forecast using dynamic Multiple Predictor Systems

Time series forecasting has been applied to predict performance degradation in Microservice-Based Applications (MBAs). The prediction enables MBA adaptation to avoid performance degradation and maintain the customer experience. The approaches in the literature commonly perform the forecast using a single monolithic model. However, since no model is better than others for all possible scenarios, using only one increases the risk of inaccurate estimates. Thus, an alternative for improving the accuracy and robustness of the performance degradation of MBAs is to adopt an ensemble, i.e., a Multiple Predictor Systems (MPS). This paper proposes an MPS methodology that selects and combines the most suitable models to forecast test patterns from a pool of models. Experiments were carried out with 32 time series containing performance metrics commonly used for MBA adaptation. Different scenarios concerning the pool (homogeneous or heterogeneous) and the selection phase (static or dynamic) are considered. Likewise, six widely used models in the literature were employed for pool generation: ARIMA, Multilayer Perceptron, Support Vector Regression, Random Forest, Long Short-Term Memory, and eXtreme Gradient Boosting. The evaluation shows that the proposed solution maintains or improves the forecast accuracy on 26 out of 32 datasets (81.25%) compared to monolithic models. In particular, the homogeneous pool and the dynamic selection of predictors obtained very satisfactory results. Therefore, MPS provide more accurate decision-making for MBA interventions, avoiding incorrect adjustments that could degrade the customer experience. Source code, figures, and data sets are publicly available at https://github.com/gfads/mps-methodology.