Work

On the Stability of the Kubernetes Horizontal Autoscaler Control Loop

Year: 2025

Type: article

Abstract: 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.

Source: IEEE Access

DOI: 10.1109/access.2025.3526751

Authors: Berta Serracanta, Andor Lukács, Alberto Rodriguez-Natal, Albert Cabellos, Gábor Rétvári

Institutions: Universitat Politècnica de Catalunya, Babeș-Bolyai University, Budapest University of Technology and Economics

Language: English

Topic: Cloud Computing and Resource Management

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075