  Today, many users deploy their microservice-based applications with various
interconnections on a cluster of Cloud machines, subject to stochastic changes
due to dynamic user requirements. To address this problem, we compare three
machine learning (ML) models for predicting the microservice call rates based
on the microservice times and aiming at estimating the scalability
requirements. We apply the linear regression (LR), multilayer perception (MLP),
and gradient boosting regression (GBR) models on the Alibaba microservice
traces. The prediction results reveal that the LR model reaches a lower
training time than the GBR and MLP models. However, the GBR reduces the mean
absolute error and the mean absolute percentage error compared to LR and MLP
models. Moreover, the prediction results show that the required number of
replicas for each microservice by the gradient boosting model is close to the
actual test data without any prediction.
