Kubernetes is a widely used platform for deploying and managing containerized applications due to its efficient elastic capabilities. The Horizontal Pod Autoscaler (HPA) in Kubernetes independently adjusts the number of pods for each service, yet these services often operate in an interconnected manner. This study aims to understand the effects of autoscaling events on a graph of interconnected services. To achieve this, we apply control theory to model the HPA’s behavior. We analyze the stability of this model, perform numerical simulations, and deploy a real testbed to evaluate the performance. Our findings demonstrate that the control theory-based model accurately predicts the HPA’s behavior, ensuring system stability with CPU utilization meeting desired thresholds and no traffic loss after a transitional period. The model provides insights into optimizing resource scheduling and improving application performance in Kubernetes environments. Additionally, we extend our model to the whole service graph to understand how individual scaling decisions influence the complex graphs of cloud applications.

This work was supported in part by the Spanish I+D+i Project TowaRds fully AI-empowered NetwoRks, subproject A (TRAINER-A), funded by Ministerio de Ciencia e Innovación (MCIN)/Agencia Estatal de Investigación (AEI)/10.13039/501100011033 under Grant PID2020-118011GB-C21; in part by the Catalan Institution for Research and Advanced Studies (ICREA) through the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia; in part by the European Social Fund; and in part by the National Research, Development and Innovation Fund of Hungary under Grant OTKA/ANN-135606, Grant OTKA/FK-135074, and Grant OTKA/FK-134604.

Peer Reviewed