News & Buzz
Open Access
In-Place Pod Resize in Kubernetes: Enabling Non-Disruptive Vertical Scaling
1
Department of Informatic Systems, Polytechnical University of Madrid, Madrid 28030, Spain
* Corresponding Author:
Joaquín Entrialgo,
[email protected]
Volume 1, Issue 1
2026
Received: 21 November 2025
Accepted: 25 March 2026
Published: 30 March 2026
Article Information
Abstract
Kubernetes now supports In-Place Pod Resize, a mechanism that allows CPU and memory adjustments without restarting Pods. This removes a key limitation of vertical scaling and reduces disruption for stateful or latency-sensitive workloads. In this News \& Buzz article, we explain how the feature works, its integration with the Kubelet and container runtimes, and its impact on the Vertical Pod Autoscaler and hybrid autoscaling. Finally, we highlight open research directions, including scheduler coordination, application adaptation, and real-world performance evaluation.
Graphical Abstract
Keywords
vertical autoscaling
kubernetes
containers
cloud-native
Data Availability Statement
Not applicable.
Funding
This work was funded by the the Spanish National Plan for Research, Development and Innovation from the Spanish Ministerio de Ciencia e Innovación under Grant PID2024-155752OB-I00.
Conflicts of Interest
The author declares no conflicts of interest.
AI Use Statement
The author declares that generative artificial intelligence (AI) was used solely for language editing to improve the clarity and readability of the manuscript. The AI tool utilized was ChatGPT-5. The authors take full responsibility for the content of the manuscript, including the accuracy of the information and the integrity of the research.
Ethical Approval and Consent to Participate
Not applicable.
References
- Sarkar, N. (2025, May 16). Kubernetes 1.35: In-Place Pod Resize graduates to stable. Kubernetes Blog. Retrieved from \url{https://kubernetes.io/blog/2025/12/19/kubernetes-v1-35-in-place-pod-resize-ga/
[Google Scholar] - Kubernetes. (n.d.). In-place updates support (Enhancement \#4016). GitHub. Retrieved November 20, 2025, from \url{https://github.com/kubernetes/autoscaler/tree/master/vertical-pod-autoscaler/enhancements/4016-in-place-updates-support
[Google Scholar] - Na, J. H., Yu, H. J., Kang, H., Kang, H., Lim, H. D., Shin, J. H., & Noh, S. Y. (2024). PVA: the persistent volume autoscaler for stateful applications in kubernetes. IEEE Access, 12, 179130-179143.
[CrossRef] [Google Scholar] - Kubernetes. (2025). In-place pod resize (KEP-1287). GitHub. Retrieved November 20, 2025, from \url{https://github.com/kubernetes/enhancements/issues/1287
[Google Scholar] - Baresi, L., Hu, D. Y. X., Quattrocchi, G., & Terracciano, L. (2021, November). KOSMOS: Vertical and horizontal resource autoscaling for kubernetes. In International Conference on Service-Oriented Computing (pp. 821-829). Cham: Springer International Publishing.
[CrossRef] [Google Scholar] - Feng, Y., Li, J., Li, L., Li, H., Xiao, Z., & Wang, X. (2025, August). HyMetricScaler: A Multi-Metric-Driven Hybrid Autoscaling Framework for Kubernetes. In 2025 IEEE International Conference on High Performance Computing and Communications (HPCC) (pp. 146-153). IEEE.
[CrossRef] [Google Scholar] - Marchese, A., & Tomarchio, O. (2025, July). SLO-aware container orchestration on Kubernetes clusters. In 2025 IEEE 18th International Conference on Cloud Computing (CLOUD) (pp. 318-327). IEEE.
[CrossRef] [Google Scholar]
Cite This Article
APA Style
Entrialgo, J. (2026). In-Place Pod Resize in Kubernetes: Enabling Non-Disruptive Vertical Scaling. Journal of Systems Scalability, 1(1), 35–38. https://doi.org/10.62762/JSS.2025.776624
Export Citation
RIS Format
Compatible with EndNote, Zotero, Mendeley, and other reference managers
TY - JOUR AU - Entrialgo, Joaquín PY - 2026 DA - 2026/03/30 TI - In-Place Pod Resize in Kubernetes: Enabling Non-Disruptive Vertical Scaling JO - Journal of Systems Scalability T2 - Journal of Systems Scalability JF - Journal of Systems Scalability VL - 1 IS - 1 SP - 35 EP - 38 DO - 10.62762/JSS.2025.776624 UR - https://www.icck.org/article/abs/JSS.2025.776624 KW - vertical autoscaling KW - kubernetes KW - containers KW - cloud-native AB - Kubernetes now supports In-Place Pod Resize, a mechanism that allows CPU and memory adjustments without restarting Pods. This removes a key limitation of vertical scaling and reduces disruption for stateful or latency-sensitive workloads. In this News \& Buzz article, we explain how the feature works, its integration with the Kubelet and container runtimes, and its impact on the Vertical Pod Autoscaler and hybrid autoscaling. Finally, we highlight open research directions, including scheduler coordination, application adaptation, and real-world performance evaluation. SN - 3142-7855 PB - Institute of Central Computation and Knowledge LA - English ER -
BibTeX Format
Compatible with LaTeX, BibTeX, and other reference managers
@article{Entrialgo2026InPlace,
author = {Joaquín Entrialgo},
title = {In-Place Pod Resize in Kubernetes: Enabling Non-Disruptive Vertical Scaling},
journal = {Journal of Systems Scalability},
year = {2026},
volume = {1},
number = {1},
pages = {35-38},
doi = {10.62762/JSS.2025.776624},
url = {https://www.icck.org/article/abs/JSS.2025.776624},
abstract = {Kubernetes now supports In-Place Pod Resize, a mechanism that allows CPU and memory adjustments without restarting Pods. This removes a key limitation of vertical scaling and reduces disruption for stateful or latency-sensitive workloads. In this News \\& Buzz article, we explain how the feature works, its integration with the Kubelet and container runtimes, and its impact on the Vertical Pod Autoscaler and hybrid autoscaling. Finally, we highlight open research directions, including scheduler coordination, application adaptation, and real-world performance evaluation.},
keywords = {vertical autoscaling, kubernetes, containers, cloud-native},
issn = {3142-7855},
publisher = {Institute of Central Computation and Knowledge}
}
Article Metrics
Publisher's Note
ICCK stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and Permissions
Copyright © 2026 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Scan to read this article