Microservices based architectures are increasingly widespread in the cloud software industry. Still, 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. We propose to address this shortage with ATOM, a model-driven autoscaling controller 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. 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.

Additional Metadata
Keywords Autoscaling, Layered queueing network, Microservices, Performance optimization
Persistent URL dx.doi.org/10.1109/ICDCS.2019.00197
Conference 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019
Citation
Gias, A.U. (Alim Ul), Casale, G. (Giuliano), & Woodside, C.M. (2019). ATOM: Model-driven autoscaling for microservices. In Proceedings - International Conference on Distributed Computing Systems (pp. 1994–2004). doi:10.1109/ICDCS.2019.00197