Microservice-Oriented Workload Prediction Using Deep Learning

Background: Service oriented architectures are becoming increasingly popular due to their flexibility and scalability which makes them a good fit for cloud deployments. Aim: This research aims to study how an efficient workload prediction mechanism for a practical proactive scaler, could be provided. Such a prediction mechanism is necessary since in order to fully take advantage of on-demand resources and reduce manual tuning, an auto-scaling, preferable predictive, approach is required, which means increasing or decreasing the number of deployed services according to the incoming workloads. Method: In order to achieve the goal, a workload prediction methodology that takes into account microservice concerns is proposed. Since, this should be based on a performant model for prediction, several deep learning algorithms were chosen to be analysed against the classical approaches from the recent research. Experiments have been conducted in order to identify the most appropriate prediction model. Results: The analysis emphasises very good results obtained using the MLP (MultiLayer Perceptron) model, which are better than those obtained with classical time series approaches, with a reduction of the mean error prediction of 49%, when using as data, two Wikipedia traces for 12 days and with two different time windows: 10 and 15min. Conclusion: The tests and the comparison analysis lead to the conclusion that considering the accuracy, but also the computational overhead and the time duration for prediction, MLP model qualifies as a reliable foundation for the development of proactive microservice scaler applications