Surviving the Packet Storm: A Deep Dive into Kubernetes Networking & CNI Performance in 2025

Let’s be honest: if you are running default iptables-based networking in Kubernetes in 2025, you are actively choosing to fail. I have spent the last decade watching bright-eyed developers deploy pristine microservices architectures only to see them crumble under the weight of network latency the moment real traffic hits. The abstraction of Kubernetes is beautiful until you realize that every packet is traversing a maze of virtual bridges, NAT tables, and conntrack entries that would make a CCIE weep. In the Nordic region specifically, where we pride ourselves on exceptional connectivity via the Norwegian Internet Exchange (NIX), introducing artificial software bottlenecks at the cluster level is practically criminal. We aren't just talking about milliseconds anymore; in high-frequency trading or real-time data processing workloads, we are fighting for microseconds. This guide isn't for the hobbyist spinning up a Minikube instance; it is for the engineers responsible for keeping services alive when the load balancer goes red. We are going to tear apart the Container Network Interface (CNI) landscape, look at why eBPF has effectively killed legacy routing, and discuss why your underlying infrastructure—specifically the virtualized metal you run on—matters more than your YAML files.

The CNI Battlefield: Why eBPF is the Only Logical Choice

Back in 2020, we argued about Calico versus Flannel. Today, that debate is dead. If you are running high-performance workloads in 2025, you are running Cilium, and you are leveraging eBPF (Extended Berkeley Packet Filter). The old way involved kube-proxy manipulating massive iptables rule sets. At scale, with thousands of services, the kernel spends more time traversing these lists than actually moving packets. I recently audited a cluster for a fintech client in Oslo where service updates took 30 seconds to propagate because the kernel was choking on rule updates. By switching to an eBPF-based data plane, we bypass the host networking stack significantly, allowing the kernel to process packets at XDP (eXpress Data Path) speeds. This isn't just optimization; it is a fundamental architectural shift necessary for modern throughput requirements.

Pro Tip: If you are still seeing high soft-interrupt (SI) CPU usage on your nodes, you are likely suffering from packet processing overhead. Check mpstat -P ALL 1. If one core is pinned at 100% SI, your CNI is the bottleneck, not your application logic.

Configuring Cilium for Zero-Overhead Routing

To truly eliminate overhead, we need to disable kube-proxy entirely and let Cilium handle the service mesh duties. This replaces the slow conntrack tables with eBPF hashmaps. Here is the configuration we use as a baseline for CoolVDS compute nodes to ensure maximum throughput:

# helm-values.yaml for Cilium 1.16+
kubeProxyReplacement: "strict"
k8sServiceHost: "10.96.0.1"
k8sServicePort: "443"
bpf:
  masquerade: true
  tproxy: true
  hostLegacyRouting: false
tunnel: "disabled" # We prefer Direct Routing for raw speed
autoDirectNodeRoutes: true
ipv4:
  enabled: true
ipv6:
  enabled: true # 2025 is the year you finally enable IPv6
loadBalancer:
  mode: "dsr" # Direct Server Return saves bandwidth
  acceleration: "native"

Using Direct Server Return (DSR) is critical here. Without it, the node receiving the request must proxy the response back through the load balancer, doubling the bandwidth usage on your ingress nodes. With DSR, the backend pod responds directly to the client. This requires your underlying network fabric—like the high-performance switching we utilize at CoolVDS—to support asymmetric routing, but the performance gains are non-negotiable for video streaming or heavy API traffic.

Latency, Locality, and the Norwegian Context

You can optimize your CNI until you are blue in the face, but you cannot code your way out of physics. Network latency is the silent killer of microservices. If your database is in Frankfurt but your Kubernetes workers are in a budget cloud provider in Stockholm, and your users are in Oslo, you are introducing 30-40ms of round-trip time (RTT) before your application even processes a single byte. For a complex request chain involving five microservices, that latency compounds into user-perceptible lag. This is why data residency and physical proximity are technical requirements, not just legal ones under GDPR and Schrems II. Hosting within Norway, specifically in data centers connected directly to NIX, ensures RTTs as low as 1-2ms to local ISPs. When we provision NVMe-backed instances on CoolVDS, we aren't just selling