February 25, 2026
CloudRAN.AI and Firecell Partner to Cut the Cost and Complexity of Private 5G
read now
BREAKING NEWS: Firecell and Accelleran Merge to Deliver Sovereignty-Compliant Industrial Private 5G Learn more
Kubernetes networking is transforming how 5G, edge computing, and IoT work together. It connects containerised applications with 5G infrastructure, meeting the demands of low latency and high bandwidth. Here’s what you need to know:
Kubernetes networking is crucial for ensuring reliable, secure, and efficient 5G edge IoT systems, with ongoing developments addressing existing limitations.
CNI Plugin Comparison for 5G Edge IoT Deployments
Kubernetes uses the Container Networking Interface (CNI) as a standardised framework for networking. This flexibility is particularly useful in edge IoT setups, where different networking solutions must meet specific 5G and IoT needs. As Tigera explains:
"CNI was created to make networking solutions integratable with a range of container orchestration systems and runtimes. Instead of making the networking solutions pluggable, it defines a common interface standard for both the networking and container execution layers."
The choice of CNI plugin can have a noticeable effect on performance. For instance, Calico supports high-performance Layer 3 networking and security using BGP. Cilium, on the other hand, uses eBPF and XDP for efficient packet processing and observability. Flannel offers a simpler VXLAN overlay for straightforward deployments. If you need multiple network interfaces – like separating control traffic from high-speed data traffic in 5G environments – Multus CNI allows pods to connect to multiple networks simultaneously.
CNI plugins bridge Kubernetes overlay networks with 5G underlay networks. In March 2024, Ericsson Research showcased a traffic priority CNI plugin on an Nvidia Jetson NX edge node running a Simultaneous Localisation and Mapping (SLAM) application. This system used Linux fwmark to map pod traffic to 5G QoS flows, enforcing a 10-millisecond latency requirement. Under these conditions, SLAM achieved an Absolute Position Error (APE) with a Root Mean Square Error (RMSE) of 10.08 ± 0.12 centimetres, compared to 9.24 ± 0.14 centimetres in an unrestricted environment.
For high-bandwidth 5G workloads, plugins like SR-IOV (Single Root I/O Virtualisation) allow pods to directly interact with physical network hardware, delivering near-line-rate performance. Kubernetes also supports traffic shaping at the pod level through the CNI bandwidth plugin, which uses Token Bucket Filter (TBF) to prevent IoT devices from overwhelming edge bandwidth.
| CNI Plugin | Primary Use Case in Edge/5G | Key Technology |
|---|---|---|
| Calico | High-performance L3 networking and security | BGP / eBPF / Network Policies |
| Cilium | Advanced security and observability | eBPF / XDP |
| Flannel | Simple, easy-to-configure overlay | VXLAN |
| Multus | Multi-homed pods (e.g., separate signalling/data planes) | Meta-plugin for multiple CNIs |
Kubernetes ensures efficient inter-pod communication, a critical feature for edge IoT applications.
Kubernetes employs a flat network structure, allowing every pod to communicate with every other pod without Network Address Translation (NAT). This design is essential for meeting the low-latency, high-bandwidth requirements of 5G edge IoT systems.
Service discovery is managed through internal DNS and stable Service IPs. These tools dynamically register new IoT application instances with the cluster DNS, enabling other pods to locate them by name instead of IP address. This simplifies scaling, as communication paths remain intact even as edge nodes are added or removed.
The choice between encapsulated and unencapsulated networking models also matters. Unencapsulated Layer 3 routing (used by Calico in BGP mode or Kube-Router) minimises latency by avoiding the overhead of VXLAN or IPsec tunnels. However, this requires BGP-capable infrastructure. Encapsulated models, while easier to set up, come with higher processing overhead.
Network policies act as application-centric firewalls, controlling traffic at the IP and port levels (OSI Layer 3 or 4) for TCP, UDP, and SCTP protocols. In edge IoT environments, these policies enable microsegmentation, which isolates workloads based on policies rather than physical network boundaries. This helps maintain strict security for IoT systems while keeping the network manageable.
Policies independently manage inbound (ingress) and outbound (egress) traffic. By default, pods are non-isolated. Once a policy is applied, the pod becomes isolated, and only explicitly allowed traffic is permitted. This approach supports a "default deny" strategy, ensuring that all traffic is blocked unless explicitly authorised – a critical requirement for securing sensitive IoT data.
Traffic rules are defined using three identifiers: pod labels, namespaces, and IP blocks (CIDR ranges). Since IoT devices often exist outside the Kubernetes cluster and pod IPs are temporary, ipBlock selectors are used to define communication ranges for external sensors or gateways. However, not all CNI plugins support policy enforcement. For example, Flannel offers limited policy features compared to Calico or Cilium, making it essential to verify CNI compatibility before deployment.
Deploying Kubernetes at the edge brings its own set of networking hurdles. Edge nodes often operate with limited compute and storage resources, making it impractical to run full Kubernetes distributions. To tackle this, operators frequently turn to lightweight alternatives like K3s or MicroK8s, which reduce resource demands while still aligning with standard Kubernetes capabilities. Below are some of the key challenges in balancing performance and reliability for 5G edge environments.
Achieving ultra-low latency in 5G edge environments requires more than just fast networks – it demands precise scheduling. However, standard Kubernetes scheduling focuses on CPU usage rather than latency, which can lead to unpredictable performance. Tools like CPU pinning, NUMA-aware scheduling, and Topology Managers are used to ensure consistent pod response times.
For high-performance networking, techniques such as SR-IOV, DPDK, and PCI-passthrough bypass the standard Docker networking stack, enabling near line-rate performance. Additionally, advanced setups use Linux fwmark to tag packets for integration with 5G QoS flows. But here’s the catch: many 32-bit fwmark fields are already reserved by plugins like Calico, Cilium, or Weave Net, leaving limited space for QoS tagging in complex deployments. These constraints highlight the need for sophisticated networking approaches in 5G edge IoT systems.
The Multus CNI plugin allows pods to connect to multiple network interfaces simultaneously, which is crucial in 5G setups for separating management, signalling, and high-speed data traffic. For instance, real-world deployments use separate interfaces to isolate signalling traffic from high-speed data, ensuring both compliance and performance.
However, Multus comes with its own complexities. It relies on unstructured annotations for configuration, lacks built-in support for policies, load balancing, and health checks on secondary interfaces, and doesn’t allow dynamic changes to interfaces. These limitations can affect overall system efficiency, making it challenging to manage 5G edge IoT deployments effectively.
Industrial IoT applications require high availability, as even brief network disruptions can have significant consequences. While Kubernetes includes self-healing features like automatic container restarts, health checks, and rolling updates, these must be supported by a robust architecture. For example, using a Raft-replicated architecture for Resource Lead Agents ensures a fault-tolerant control plane. This enables automatic leader re-election and seamless workload migration during node or agent failures.
To further minimise disruptions, applications should be designed to function independently during cloud disconnections, reducing the impact of unstable edge networks. As Bill Clark, Principal Product Manager at Spirent, puts it:
"Instead of preventing every failure, the focus is on rapid recovery and service continuity".
This approach of assuming failures and focusing on recovery is becoming a standard for Cloud-Native Network Functions (CNFs) in 5G deployments, underlining the critical role Kubernetes networking plays in maintaining reliable 5G edge IoT systems.
Firecell has tackled challenges like latency, multi-network management, and security by integrating its solutions with Kubernetes networking. With private 5G offerings built on open-source OpenAirInterface (OAI) software, which aligns with 3GPP and O-RAN standards, Firecell enables Kubernetes-based edge IoT deployments that are both interoperable and efficient. The integration relies on Canonical‘s Ubuntu operating system, providing a solid platform for telecom networking software and Kubernetes workloads, ensuring enterprise-level performance and security.
Firecell’s Network-in-a-box (NIB) solutions are designed to quickly establish private 5G infrastructure in areas without public networks. These solutions support containerised network functions (CNFs) and edge applications across devices, RAN, and core clouds. By integrating with Ubuntu Pro, Firecell delivers real-time processing capabilities and Enhanced Platform Awareness (EPA), which are crucial for achieving low-latency communication within Kubernetes environments.
As Claude Seyrat, CEO of Firecell, puts it:
"By pooling our resources together and supporting the OpenAirInterface initiative, we are setting a new standard in the industry… ensuring that open-source becomes the cornerstone of 5G development".
This dedication to open standards allows businesses to deploy Kubernetes clusters that effectively manage cellular IoT technologies like LTE-M, NB-IoT, and RedCap, supporting a wide array of connected sensors and smart devices.
Firecell’s private 5G networks leverage Kubernetes’ CNI framework to deliver secure, low-latency traffic management tailored for industrial IoT needs. Dedicated network slices ensure Kubernetes-based IoT applications receive the necessary QoS and resource allocation. This setup is particularly useful when CNI plugins are employed to map pod-specific QoS between overlay networks and the 5G underlay. Additionally, the architecture incorporates encrypted communication channels and strict segmentation, safeguarding sensitive industrial data from unauthorised access.
Cédric Gégout, VP of Product Management at Canonical, highlights:
"Our commitment to deliver robust, secure open source solutions with more than 10-years of support with Ubuntu Pro guarantees production-grade performance and security to all the 5G industries".
Firecell’s solutions are tailored for real-time Kubernetes workloads in industries like manufacturing, logistics, and autonomous systems. The portability of its tactical 5G networks allows for rapid deployment in defence and industrial settings. These networks can be configured to allocate optimal resources to critical operations through dedicated slices, enabling independent functionality for various applications and user groups.
As a member of the 5G Open Innovation Lab, Firecell collaborates with partners like Deloitte, SK Telecom, and Microsoft to advance edge computing for enterprise use. The company’s mission to make "Private 5G as simple and affordable as WiFi" has simplified the deployment of Kubernetes-orchestrated IoT systems, removing much of the complexity typically associated with cellular networks. With seamless integration into existing enterprise LANs, Firecell’s solutions are an excellent fit for organisations shifting to containerised edge architectures. These practical deployments are paving the way for further advancements in Kubernetes networking for 5G edge IoT.
The convergence of Kubernetes, 5G, and edge computing is reshaping industrial IoT. By 2027, research predicts that deep learning will feature in over 65% of edge use cases, a sharp rise from under 10% in 2021. Building on established Container Networking Interface (CNI) practices and recent experimental breakthroughs, the future promises to redefine resource allocation and network orchestration at the edge, enabling tighter integration of AI and real-time networking.
The established CNI model is evolving into a more advanced Kubernetes Network Driver (KND) architecture, treating network interfaces as primary resources. This shift is critical for AI workloads, which require precise alignment between GPUs and network interfaces to avoid performance issues.
In June 2025, Antonio Ojea demonstrated the DraNet implementation using NVIDIA B200 GPUs and Mellanox RoCE NICs. By aligning these components on the same PCI root through Dynamic Resource Allocation (DRA), the system achieved a 59.6% increase in bus bandwidth for all_gather operations and a 58.1% improvement in all_reduce tests, with pod startup latency reduced to 1.8 seconds.
Michele Polese and colleagues have proposed an AI-RAN Orchestrator that extends Open RAN Service Management and Orchestration (SMO) to manage both telecom and AI workloads on shared edge infrastructure. This system uses topology-aware scheduling, which exposes attributes like NUMA nodes and PCI root addresses, enabling intelligent resource placement. The result? Consistent resource performance in high-performance clusters.
Standardised network integration is advancing alongside AI-driven innovations. The integration of Open RAN technologies with Kubernetes is being enabled through modular architectures and standardised interfaces. The Unified Network and Cloud Orchestration (UNCO) Framework, proposed in IETF draft-li-unco-framework-02 (June 2025), introduces interfaces (IN1.1, IN1.2, IN2.1, IN2.2) that allow real-time telemetry exchange between Cloud Managers and Network Controllers.
Xueting Li and Cong Li from China Telecom highlight a key challenge:
"The lack of standardised interfaces and models for exchanging cloud telemetry across the network domain remains a key obstacle".
The UNCO framework addresses this by integrating real-time metrics like CPU/GPU load and memory availability into orchestration logic, reducing the risk of SLA violations in 5G slice traffic.
A practical example of this convergence came in February 2025, when Red Hat, Druid Software, and Napatech collaborated to integrate Red Hat Device Edge with the Raemis cellular platform and Napatech NT200 SmartNICs. This solution offloaded 5G User Plane Function (UPF) data plane processing to the SmartNIC, achieving latency under 4 microseconds while supporting up to 140 million concurrent flows at wire-speed operation of 2×100G.
Specialised Kubernetes drivers are also being developed to manage carrier-grade protocols like MPLS and SRv6, as well as 5G RAN slices through BGP peering and route leaking. This evolution enables Kubernetes to orchestrate not just applications but also the underlying network infrastructure, directly boosting edge IoT performance in 5G environments.
Standardisation is essential to ensure that solutions from different vendors work together seamlessly. The Kubernetes community is actively working on this through initiatives like KEP-4817, which aims to standardise how network data is reported in ResourceClaim status. This ensures that various network drivers can interoperate within a network-aware, cloud-native ecosystem.
Antonio Ojea explains the shift:
"The KND model shifts away from these solutions towards a system of independent, composable drivers that leverage first-class Kubernetes APIs".
Tools like Terraform and Ansible are also playing a key role by automating the provisioning of Kubernetes clusters and 5G edge components, ensuring consistent deployments. As of 2024, around 90% of businesses worldwide have adopted cloud computing, driving demand for integrated orchestration solutions. However, challenges remain – 72% of Kubernetes users still report difficulties in deploying and managing Kubernetes on edge devices. This underscores the need for ongoing efforts to simplify and standardise edge IoT deployments.
Kubernetes networking plays a critical role in enabling 5G edge IoT deployments, connecting containerised applications with cellular infrastructure. By integrating Container Networking Interface (CNI) plugins with 5G Network Exposure Functions, organisations can now enforce application-specific Quality of Service (QoS) requirements that were previously hidden from the network. Demonstrations have already shown that these integrations can meet stringent QoS demands, such as achieving latency as low as 10 milliseconds in high-performance 5G edge scenarios.
However, challenges remain. Issues like hidden QoS parameters, intermittent edge connectivity, and operational complexities still pose hurdles. That said, advancements such as K3s and eBPF-based networking are delivering noticeable performance improvements. Lightweight Kubernetes distributions help reduce resource usage, while advanced edge management systems ensure containers can restart in under a second during network interruptions.
These advancements are paving the way for robust solutions. Firecell’s private 5G networks tackle these challenges head-on, offering secure and reliable connectivity designed for industrial IoT needs. Their turnkey systems integrate seamlessly with Kubernetes orchestration, ensuring guaranteed QoS and enterprise-level security without the complications of public cellular networks. For industries deploying autonomous robots, manufacturing systems, or logistics operations, Firecell provides scalable options – from the £11,900 Orion Labkit for development to the enterprise-ready Pegasus Networks – making Kubernetes-managed edge IoT deployments more accessible.
Looking ahead, the convergence of AI workloads and Open RAN technologies will further enhance Kubernetes capabilities in 5G edge environments. Despite these advancements, challenges in deploying Kubernetes on edge devices persist, driving ongoing efforts to simplify and standardise these solutions for broader adoption. Kubernetes networking remains a cornerstone in orchestrating secure and efficient industrial IoT deployments across 5G edge landscapes.
When choosing the best CNI plugin for 5G edge IoT, it all comes down to your specific needs and priorities. Some of the top contenders in this space include Cilium, Kube-OVN, and Calico. These plugins are well-regarded for their ability to handle Kubernetes networking in edge environments.
To find the right fit, consider key factors like scalability, security features, and how easily the plugin integrates with your existing setup. Each option offers distinct advantages, so evaluate them carefully to match your requirements.
Kubernetes plays a key role in meeting the latency and quality-of-service (QoS) demands of 5G at the edge. By supporting QoS-aware deployments, dynamic resource allocation, and policy-driven scheduling, it ensures efficient performance. Techniques such as optimised microservice placement and load balancing are crucial for achieving low latency and consistent reliability, particularly in systems tailored for edge environments. These features position Kubernetes as a reliable solution for maintaining performance in 5G-powered edge IoT applications.
When Kubernetes pods need to connect to multiple networks at the same time, Multus is the go-to solution. This is especially important in intricate setups like 5G edge IoT environments, where various network segments or interfaces are essential for optimising performance, ensuring security, or managing routing.
Multus works by chaining multiple CNI (Container Network Interface) plugins. This allows pods to connect to both a primary network and additional auxiliary networks. For instance, pods can simultaneously access a local edge network and a broader enterprise network, providing the flexibility needed for complex deployments.
