Version 1
: Received: 12 August 2024 / Approved: 13 August 2024 / Online: 14 August 2024 (00:31:27 CEST)
How to cite:
Dakić, V.; Redžepagić, J.; Bašić, M.; Žgrablić, L. Performance And Latency Efficiency Evaluation Of Kubernetes Container Network Interfaces For Built-In And Custom Tuned Profiles. Preprints2024, 2024080970. https://doi.org/10.20944/preprints202408.0970.v1
Dakić, V.; Redžepagić, J.; Bašić, M.; Žgrablić, L. Performance And Latency Efficiency Evaluation Of Kubernetes Container Network Interfaces For Built-In And Custom Tuned Profiles. Preprints 2024, 2024080970. https://doi.org/10.20944/preprints202408.0970.v1
Dakić, V.; Redžepagić, J.; Bašić, M.; Žgrablić, L. Performance And Latency Efficiency Evaluation Of Kubernetes Container Network Interfaces For Built-In And Custom Tuned Profiles. Preprints2024, 2024080970. https://doi.org/10.20944/preprints202408.0970.v1
APA Style
Dakić, V., Redžepagić, J., Bašić, M., & Žgrablić, L. (2024). Performance And Latency Efficiency Evaluation Of Kubernetes Container Network Interfaces For Built-In And Custom Tuned Profiles. Preprints. https://doi.org/10.20944/preprints202408.0970.v1
Chicago/Turabian Style
Dakić, V., Matej Bašić and Luka Žgrablić. 2024 "Performance And Latency Efficiency Evaluation Of Kubernetes Container Network Interfaces For Built-In And Custom Tuned Profiles" Preprints. https://doi.org/10.20944/preprints202408.0970.v1
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 methodology, which involves managing numerous components, dependencies, and configurations, requires high expertise and precision. Our performance evaluations across network interfaces with different theoretical bandwidths gave us a comprehensive understanding of CNI performance and overall efficiency. Our research confirms that certain CNIs are better suited for specific use cases, mainly when tuning our environment for smaller or larger network packages 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).
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
