Microservices based architectures are increasingly widespread in the cloud software industry. However, there is a shortage of auto-scaling methods designed to leverage the unique features of these architectures, such as the ability to independently scale a subset of microservices, as well as the ease of monitoring their state and reciprocal calls.
In the RADON project, we address this shortage with ATOM, a model-driven autoscaling controller for microservices. As we analyse in our paper ‘ATOM: model-driven autoscaling for microservices’, ATOM instantiates and solves at run-time a layered queueing network model of the application. Computational optimization is used to dynamically control the number of replicas for each microservice and its associated container CPU share, overall achieving a fine-grained control of the application capacity at run-time.
In addition, experimental results indicate that for heavy workloads ATOM offers around 30%-37% higher throughput than baseline model-agnostic controllers based on simple static rules. We also find that model-driven reasoning reduces the number of actions needed to scale the system as it reduces the number of bottleneck shifts that we observe with model-agnostic controllers.
You can read the paper ‘ATOM: model-driven autoscaling for microservices’ here.