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| Main Author: | |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.23628 |
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| _version_ | 1866911029822226432 |
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| author | Ojea, Antonio |
| author_facet | Ojea, Antonio |
| contents | Traditional Kubernetes networking struggles to meet the escalating demands of AI/ML and evolving Telco infrastructure. This paper introduces Kubernetes Network Drivers (KNDs), a transformative, modular, and declarative architecture designed to overcome current imperative provisioning and API limitations. KNDs integrate network resource management into Kubernetes' core by utilizing Dynamic Resource Allocation (DRA), Node Resource Interface (NRI) improvements, and upcoming OCI Runtime Specification changes. Our DraNet implementation demonstrates declarative attachment of network interfaces, including Remote Direct Memory Access (RDMA) devices, significantly boosting high-performance AI/ML workloads. This capability enables sophisticated cloud-native applications and lays crucial groundwork for future Telco solutions, fostering a "galaxy" of specialized KNDs for enhanced application delivery and reduced operational complexity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23628 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Kubernetes Network Driver Model: A Composable Architecture for High-Performance Networking Ojea, Antonio Networking and Internet Architecture Artificial Intelligence Traditional Kubernetes networking struggles to meet the escalating demands of AI/ML and evolving Telco infrastructure. This paper introduces Kubernetes Network Drivers (KNDs), a transformative, modular, and declarative architecture designed to overcome current imperative provisioning and API limitations. KNDs integrate network resource management into Kubernetes' core by utilizing Dynamic Resource Allocation (DRA), Node Resource Interface (NRI) improvements, and upcoming OCI Runtime Specification changes. Our DraNet implementation demonstrates declarative attachment of network interfaces, including Remote Direct Memory Access (RDMA) devices, significantly boosting high-performance AI/ML workloads. This capability enables sophisticated cloud-native applications and lays crucial groundwork for future Telco solutions, fostering a "galaxy" of specialized KNDs for enhanced application delivery and reduced operational complexity. |
| title | The Kubernetes Network Driver Model: A Composable Architecture for High-Performance Networking |
| topic | Networking and Internet Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2506.23628 |