TY - JOUR
T1 - Unveiling Kubernetes CNI
T2 - An In-Depth Analysis of Networking Performance and Resource Efficiency
AU - Choi, Wonmi
AU - Ahn, Juyoung
AU - Yoo, Yeonho
AU - Niu, Zhixiong
AU - Yang, Gyeongsik
AU - Yoo, Chuck
N1 - Publisher Copyright:
© 2025, Korean Institute of Communications and Information Sciences. All rights reserved.
PY - 2025/7/1
Y1 - 2025/7/1
N2 - Kubernetes relies heavily on its networking performance. It offers four representative networking plugins: Flannel, Calico, Cilium, and Kube-router. However, their performance differences are not well understood. This study evaluates these plugins using real-world workloads like Memcached, Nginx and Kafka, examining throughput, latency, and CPU usage in 10 GbE and 100 GbE environments. Results reveal significant performance differences due to each plugin’s architecture. Kube-router excels in CPU- and network-intensive scenarios but complicates network management. Among overlay plugins Flannel performs best in CPU-intensive tasks, while Cilium is superior for network-intensive tasks. This analysis provides insights for selecting suitable plugin based on workload characteristics.
AB - Kubernetes relies heavily on its networking performance. It offers four representative networking plugins: Flannel, Calico, Cilium, and Kube-router. However, their performance differences are not well understood. This study evaluates these plugins using real-world workloads like Memcached, Nginx and Kafka, examining throughput, latency, and CPU usage in 10 GbE and 100 GbE environments. Results reveal significant performance differences due to each plugin’s architecture. Kube-router excels in CPU- and network-intensive scenarios but complicates network management. Among overlay plugins Flannel performs best in CPU-intensive tasks, while Cilium is superior for network-intensive tasks. This analysis provides insights for selecting suitable plugin based on workload characteristics.
KW - Cloud networking
KW - Container network interface
KW - CPU usage
KW - Kubernetes
KW - Performance profiling
UR - https://www.scopus.com/pages/publications/105013605116
U2 - 10.7840/kics.2025.50.7.1085
DO - 10.7840/kics.2025.50.7.1085
M3 - Article
AN - SCOPUS:105013605116
SN - 1226-4717
VL - 50
SP - 1085
EP - 1099
JO - Journal of Korean Institute of Communications and Information Sciences
JF - Journal of Korean Institute of Communications and Information Sciences
IS - 7
ER -
