@inproceedings{fbab730a6bab4535869bc7fe1f949006,
title = "KAPET{\'A}NIOS: Automated Kubernetes Adaptation through a Digital Twin",
abstract = "This demo presents a self-operating Kubernetes (K8s) cluster that uses digital twinning and machine learning to autonomously adapt its Horizontal Pod Autoscaler (HPA) to workload changes. The demo uses a digital twin of a K8s cluster to gather performance statistics and learn a model for the workload. With the model, the cluster autonomously adjusts HPA parameters for better performance. The demo illustrates this process and shows that the requested pod seconds decrease by 37 \%, while the request latency stays mostly unaffected.",
keywords = "Digital Twin, Kubernetes, Machine Learning",
author = "Johannes Zerwas and Patrick Kr{\"a}mer and Ursu, \{Rǎzvan Mihai\} and Navidreza Asadi and Phil Rodgers and Leon Wong and Wolfgang Kellerer",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 13th International Conference on the Network of the Future, NoF 2022 ; Conference date: 05-10-2022 Through 07-10-2022",
year = "2022",
doi = "10.1109/NoF55974.2022.9942649",
language = "English",
series = "Proceedings of the 2022 13th International Conference on the Network of the Future, NoF 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Tim Wautres and Maurice Khabbaz and Federica Paganelli and Filip Idzikowski and Zuqing Zhu",
booktitle = "Proceedings of the 2022 13th International Conference on the Network of the Future, NoF 2022",
}