On the Stability of the Kubernetes Horizontal Autoscaler Control Loop
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 man...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10829852/ |
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| author | Berta Serracanta Andor Lukacs Alberto Rodriguez-Natal Albert Cabellos Gabor Retvari |
| author_facet | Berta Serracanta Andor Lukacs Alberto Rodriguez-Natal Albert Cabellos Gabor Retvari |
| author_sort | Berta Serracanta |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5550fbb44f13438687e6958ecc076db4 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5550fbb44f13438687e6958ecc076db42025-08-20T02:41:43ZengIEEEIEEE Access2169-35362025-01-01137160716610.1109/ACCESS.2025.352675110829852On the Stability of the Kubernetes Horizontal Autoscaler Control LoopBerta Serracanta0https://orcid.org/0000-0003-3195-2576Andor Lukacs1https://orcid.org/0000-0003-0043-1591Alberto Rodriguez-Natal2https://orcid.org/0000-0002-4239-5309Albert Cabellos3Gabor Retvari4https://orcid.org/0000-0002-5958-7817Department of Computer Architecture, Universitat Politècnica de Catalunya, Barcelona, SpainFaculty of Mathematics and Computer Science, Babeş-Bolyai University, Cluj-Napoca, RomaniaCisco, Madrid, SpainDepartment of Computer Architecture, Universitat Politècnica de Catalunya, Barcelona, SpainDepartment of Telecommunications and Artificial Intelligence, Budapest University of Technology and Economics, Budapest, HungaryKubernetes 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.https://ieeexplore.ieee.org/document/10829852/Cloud autoscalingcontrol theoryHorizontal Pod AutoscalerKubernetesmicroservices architecturenumerical simulations |
| spellingShingle | Berta Serracanta Andor Lukacs Alberto Rodriguez-Natal Albert Cabellos Gabor Retvari On the Stability of the Kubernetes Horizontal Autoscaler Control Loop IEEE Access Cloud autoscaling control theory Horizontal Pod Autoscaler Kubernetes microservices architecture numerical simulations |
| title | On the Stability of the Kubernetes Horizontal Autoscaler Control Loop |
| title_full | On the Stability of the Kubernetes Horizontal Autoscaler Control Loop |
| title_fullStr | On the Stability of the Kubernetes Horizontal Autoscaler Control Loop |
| title_full_unstemmed | On the Stability of the Kubernetes Horizontal Autoscaler Control Loop |
| title_short | On the Stability of the Kubernetes Horizontal Autoscaler Control Loop |
| title_sort | on the stability of the kubernetes horizontal autoscaler control loop |
| topic | Cloud autoscaling control theory Horizontal Pod Autoscaler Kubernetes microservices architecture numerical simulations |
| url | https://ieeexplore.ieee.org/document/10829852/ |
| work_keys_str_mv | AT bertaserracanta onthestabilityofthekuberneteshorizontalautoscalercontrolloop AT andorlukacs onthestabilityofthekuberneteshorizontalautoscalercontrolloop AT albertorodrigueznatal onthestabilityofthekuberneteshorizontalautoscalercontrolloop AT albertcabellos onthestabilityofthekuberneteshorizontalautoscalercontrolloop AT gaborretvari onthestabilityofthekuberneteshorizontalautoscalercontrolloop |