F5 has made upgrades to its cloud-native networking portfolio designed to enable service providers to migrate vital network services from virtualized platforms to Kubernetes and to increase infrastructure efficiency, security, and management visibility.
The refreshed F5 BIG-IP Cloud-Native Edition from the company integrates new eBPF-based observability features with cloud-native network functions. It’s aimed at service providers struggling to keep pace with burgeoning 5G, edge and IT infrastructure requirements, as well as emerging AI-based workloads, all while delivering carrier-grade levels of control and performance.
“Service providers should not have to choose between cloud-native agility and the performance, security, and control required for critical network infrastructure,” said Kunal Anand, Chief Product Officer at F5. “BIG-IP Cloud-Native Edition brings proven F5 BIG-IP capabilities into Kubernetes, giving network and cloud operations teams a more consistent way to deploy, secure, observe, and operate 5G, edge, and AI infrastructure at scale.”
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The new platform enables various services like DNS, CGNAT, network security, subscriber policy enforcement, and traffic management to be deployed in natively supported Kubernetes environments. Additionally, the extended platform now includes support for a variety of protocols, such as TCP, UDP, HTTP/2, Diameter, SCTP, GTP, and SIP, enabling operators to modernize more network functions, and not restricting themselves to traditional web applications only.
F5 will also introduce BIG-IP eBPF Observability that enables observability into application and network behavior across multiple Kubernetes clusters. This tool is expected to help operations teams locate the root cause of performance and connectivity problems through the correlation of application, container, and network telemetry data.
Independent testing by The Tolly Group highlighted the potential infrastructure savings. In a DNS workload, F5 processed 1.9 times more queries per second while using 91.5% less CPU and delivering 48% lower average latency than the tested virtualized network function configuration. In a CGNAT scenario, the platform achieved comparable throughput while reducing CPU consumption by 79.7% and memory usage by 98.5%.
The enhancements give service providers a broader path toward Kubernetes-based infrastructure while helping them manage increasingly distributed 5G, edge and AI environments with greater efficiency and control.