Performance and Latency Efficiency Evaluation of Kubernetes Container Network Interfaces for Built-In and Custom Tuned Profiles
Vedran Dakić, Jasmin Redžepagić, Matej Bašić and Luka Žgrablić. Performance and Latency Efficiency Evaluation of Kubernetes Container Network Interfaces for Built-In and Custom Tuned Profiles. Electronics 13(19), 3972, 2024.
Abstract
In the era of DevOps, developing new toolsets and frameworks that leverage DevOps principles is crucial. This paper demonstrates how Ansible’s powerful automation capabilities can be harnessed to manage the complexity of Kubernetes environments. This paper evaluates efficiency across various CNI (Container Network Interface) plugins by orchestrating performance analysis tools across multiple power profiles. Our performance evaluations across network interfaces with different theoretical bandwidths gave us a comprehensive understanding of CNI performance and overall efficiency, with performance efficiency coming well below expectations. Our research confirms that certain CNIs are better suited for specific use cases, mainly when tuning our environment for smaller or larger network packets and workload types, but also that there are configuration changes we can make to mitigate that. This paper also provides research into how to use performance tuning to optimize the performance and efficiency of our CNI infrastructure, with practical implications for improving the performance of Kubernetes environments in real-world scenarios, particularly in more demanding scenarios such as High-Performance Computing (HPC) and Artificial Intelligence (AI).
My contribution
The paper credits me with validation, formal analysis and investigation. I wrote the two custom optimization profiles it evaluates, the one that raises the socket buffer maximum and changes TCP settings such as window scaling and the SYN backlog, and the one that enables generic receive offload and raises the NIC ring buffers from 256 to 4096 packets. Text I drafted in July 2024 on test configurations and optimizations went into section 5.
CC BY 4.0.