###### Abstract  
Maintaining a fast, low-latency network must be balanced in the demanding world of High-Performance Computing (HPC). Any compromise in network performance can severely affect distributed HPC applications, leading to bottlenecks that undermine the entire system's efficiency. This paper highlights the critical need for precise and consistent evaluation of Kubernetes Container Network Interfaces (CNIs) to ensure that HPC workloads can operate at their full potential. Traditional manual methods for evaluating network bandwidth and latency are time-consuming and prone to errors, making them inadequate for the rigorous demands of HPC environments. To address this, we introduce a novel approach that leverages Ansible to automate and standardize the evaluation process across diverse CNIs, performance profiles, and configurations. By eliminating human error and ensuring replicability, this method significantly enhances the reliability of performance assessments. The Ansible playbooks we developed enable the efficient deployment, configuration, and execution of CNIs and evaluations, providing a robust framework for ensuring that Kubernetes-based infrastructures can meet the stringent performance requirements of HPC applications. This approach is vital for safeguarding the performance integrity of HPC workloads, ensuring that inadequate network configurations do not cripple them. Kubernetes; Container Network Interfaces; network performance evaluation; Ansible; bandwidth measurement; latency measurement; DevOps; cloud-native applications +
Footnote †: journal: Computer Physics  
0000-0001-4000-0001-4000]Dakic  
0000-0001-4000-0001-4000]Dakic  
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superior performance in dynamic and scalable Kubernetes environments. This can also be used for application deployment procedures, although this approach is not as common when using Kubernetes (or necessary). The operational impact of creating Ansible playbooks for this purpose is significant. Organizations can utilize these playbooks to obtain a more profound understanding of their network performance, rapidly detect and resolve bottlenecks, and enhance the efficiency of their Kubernetes deployments. This goes against using tools like Kickstart or Autoyast, which are not meant to be used as automation or orchestration tools--they are system configuration utilities for pre- and post-first boot scenarios. As a result, there is an increase in application performance, better user experience, and improved operational efficiency. Therefore, implementing automated and coordinated network performance evaluation enhances the durability and strength of containerized applications, enabling organizations to accomplish their business goals confidently. This approach would be well-suited as an additional resource for performance evaluation on other platforms, like HPC or cloud environments. Before putting Ansible playbooks into production, we must also be aware that copying them from the Internet might bring additional problems--for example, security issues. Hardcoded passwords can become a standard within an organization and negatively impact the overall security posture. This also leads to different discussions about the quality of Ansible code, as there are a lot of Ansible examples available online that are not usable without a deep understanding of what the code does. There are three main scientific contributions of this paper:  
* Completely automated approach--this paper offers a set of publicly available, easy-to-use Ansible playbooks to be used in any Kubernetes environment where the most used CNIs need to be evaluated before being used in production;
* Custom-tuned profiles--this paper emphasizes using built-in and custom-tuned performance profiles that we developed for this paper, focused on kernel and network interface card optimizations, especially for bandwidth.