A Holistic Machine Learning-based Autoscaling Approach for Microservice Applications

Microservice architecture is the mainstream pattern for developing large-scale cloud applications as it allows for scaling application components on demand and independently. By designing and utilizing autoscalers for microservice applications, it is possible to improve their availability and reduce the cost when the traffic load is low. In this paper, we propose a novel predictive autoscaling approach for microservice applications which leverages machine learning models to predict the number of required replicas for each microservice and the effect of scaling a microservice on other microservices under a given workload. Our experimental results show that the proposed approach in this work offers better performance in terms of response time and throughput than HPA, the state-of-the-art autoscaler in the industry, and it takes fewer actions to maintain a desirable performance and quality of service level for the target application.