According to the Cloud Native 2024 report, Kubernetes adoption has continued to surge, with more than 90% of organizations leveraging it in some capacity, whether for production or evaluation purposes. This significant growth has increased demand for tools that are not just effective, but also remarkably efficient and low-overhead to manage increasingly complex infrastructure. The Extended Berkeley Packet Filter (eBPF) is the kernel technology that answers that call.
The Extended Berkeley Packet Filter (eBPF) began its modern evolution in 2014, with the introduction of the bpf() system call in Linux kernel 3.18, answering the call for more efficient infrastructure. Today, its impact extends throughout the Kubernetes operational stack, fundamentally changing how we approach our network security, performance optimization, and runtime enforcement.
Understanding the eBPF Advantage
eBPF safely runs small, sandboxed programs within the Linux kernel, giving developers the power to extend its core capabilities. Because it bypasses the need for source code changes or unstable modules, and delivers deep, context-specific insight into system activity right at the source, eBPF significantly outperforms resource-intensive user-space agents and sidecar proxies.
Key Benefits of Kernel-Level Operation
- Minimal Overhead: eBPF minimizes the need for context switching between kernel and user space by allowing data to be processed directly within the kernel. This results in a significant reduction in CPU usage.
- Deep Context and Granularity: It grants operators deep visibility into container internals, detailing system calls, network activity, and process behaviors. Furthermore, this kernel-level access enriches every event with crucial Kubernetes metadata, such as the pod name, namespace, and container ID.
- Safety and Stability: Before execution, all eBPF programs undergo a stringent verification process. This ensures that they cannot crash the kernel or create infinite loops, which is crucial for maintaining the system’s stability and security.
Reshaping Kubernetes Operations Beyond Monitoring
The emergence of eBPF-powered tools for performance monitoring and tracing has become quite common, and their impact is now extending into two of the most critical and challenging areas in large-scale Kubernetes operations: networking and security.
High-Performance Networking and Service Mesh
eBPF has brought major changes to the networking layer in Kubernetes, which has helped in getting rid of the performance limitations found in traditional methods like iptables.
In traditional Kubernetes service routing, which typically uses kube-proxy and iptables rules, performance tends to decline as the cluster grows. The problem arises from the linear manner in which rules are processed, which becomes more pronounced in larger environments. The time taken increases linearly with each additional rule, and the performance strain becomes evident as more services and endpoints are added. A packet must go through each rule to find a match, which leads to increased latency and potential stability issues.
eBPF-based Container Network Interface (CNI) solutions harness kernel programmability to execute efficient, hash-based lookups. This means that traffic can be directed straight to the appropriate destination pod, significantly cutting down on latency compared to sifting through a long list of iptables rules.
Additionally, the performance improvements gained by processing network functions within the kernel lead to better K8s cost optimization. By utilizing eXpress Data Path (XDP), these solutions attach eBPF programs directly to the network driver. This allows for packet processing, filtering, and load balancing to take place right at the kernel’s entry point, which reduces resource consumption and enhances overall throughput.
Autonomous and Context-Aware Security
Leveraging its deep kernel access, eBPF significantly enhances security in containerized environments where traditional tooling often lacks visibility. Security teams can build lightweight Intrusion Prevention Systems (IPS) directly within the kernel, monitoring every system call for suspicious activities like unauthorized file modification or unapproved application execution. This capability is validated by industry leaders: Cloudflare uses eBPF extensively to mitigate large-scale Distributed Denial of Service (DDoS) attacks, a topic detailed in their public talks and technical blogs.
A major advantage is that operators can set and enforce specific behavior expectations for containers. If, for example, a web server unexpectedly tries to open a shell or make an unapproved network connection, the eBPF program can intervene immediately, preventing the action and stopping potential threats right away.
Verifying eBPF’s Operational Shift in Kubernetes
Cilium is one of the most well-known use cases of how eBPF has revolutionized Kubernetes operations. Cilium is an eBPF-based Container Network Interface (CNI) for Kubernetes. Its primary function is to establish the network and security infrastructure for the cluster, utilizing the advanced eBPF features of the Linux kernel to perform essential tasks such as load balancing, enforcing network policies, and ensuring observability. By doing so, Cilium can replace the traditional, resource-intensive iptables-based Kubernetes networking component (kube-proxy), resulting in significantly improved performance and more in-depth context.
The following sections will present direct command outputs that demonstrate how Cilium employs these eBPF kernel maps and programs to effectively manage service routing and monitor cluster health. You can find the instructions here to set up Cilium in your Kubernetes cluster.
Inspection of eBPF Load Balancing Maps
Instead of relying on the slow, iptables-based kube-proxy, Cilium uses eBPF maps to handle load balancing. This enables routing decisions to be made instantaneously within the kernel, achieving speeds that are unattainable by traditional networking solutions.
The following shows how we can inspect the actual eBPF Load Balancer map residing in the kernel using a Cilium command:
| magnus@linuxnode:~$ kubectl exec -n kube-system cilium-mb2qj — cilium bpf lb listDefaulted container “cilium-agent” out of: cilium-agent, config (init), mount-cgroup (init), apply-sysctl-overwrites (init), mount-bpf-fs (init), clean-cilium-state (init), install-cni-binaries (init)SERVICE ADDRESS BACKEND ADDRESS (REVNAT_ID) (SLOT)10.96.46.141:443/TCP (2) 10.0.1.211:8443/TCP (100) (2)10.108.75.146:443/TCP (1) 10.0.3.118:9443/TCP (8) (1)10.106.106.254:6379/TCP (1) 10.0.3.152:6379/TCP (40) (1)10.102.29.252:8793/TCP (2) 10.0.2.112:8793/TCP (61) (2)10.99.125.149:3000/TCP (1) 10.0.3.203:3000/TCP (67) (1)10.104.186.211:9090/TCP (1) 10.0.3.133:9090/TCP (63) (1)10.110.132.226:5000/TCP (1) 10.0.3.83:8180/TCP (95) (1)10.107.215.201:9097/TCP (1) 10.0.2.59:5000/TCP (79) (1)10.106.75.248:80/TCP (1) 10.0.3.80:8080/TCP (38) (1)10.97.109.115:443/TCP (1) 192.168.10.67:4244/TCP (2) (1)10.102.29.252:8080/TCP (4) 10.0.1.105:8080/TCP (62) (4)10.110.7.91:8001/TCP (1) 10.0.3.7:8001/TCP (91) (1)10.111.159.178:7000/TCP (1) 10.0.3.195:7000/TCP (35) (1)[……….] |
This output is a direct dump of the BPF map data structure used by Cilium for service routing. Each line represents an active Kubernetes Service:
- The SERVICE ADDRESS column shows the ClusterIP and port of a Kubernetes Service (e.g., 10.96.46.141:443/TCP).
- The BACKEND ADDRESS shows the corresponding Pod IP and port (e.g., 10.0.1.211:8443/TCP) to which the traffic is actively being routed.
Verifying Endpoint Identity and Security Context
The following shows a fundamental architectural shift enabled by eBPF: moving security enforcement from mutable IP addresses to stable Security Identities. This list details every Pod and container (referred to as a network “endpoint”) running on the current node, along with its unique security context.
| magnus@linuxnode:~$ kubectl exec -n kube-system cilium-56v7z — cilium bpf endpoint listDefaulted container “cilium-agent” out of: cilium-agent, config (init), mount-cgroup (init), apply-sysctl-overwrites (init), mount-bpf-fs (init), clean-cilium-state (init), install-cni-binaries (init)IP ADDRESS LOCAL ENDPOINT INFO10.0.2.236:0 (localhost)10.0.2.101:0 id=2109 sec_id=4 flags=0x0000 ifindex=1366 mac=02:32:99:98:B8:B6 nodemac=12:92:BC:B6:CB:F6 parent_ifindex=010.0.2.112:0 id=1048 sec_id=9056 flags=0x0000 ifindex=1352 mac=2E:58:6B:A1:03:7E nodemac=4E:9E:54:40:41:34 parent_ifindex=010.0.2.3:0 id=3193 sec_id=2369 flags=0x0000 ifindex=26 mac=EA:0A:49:C0:61:B3 nodemac=CA:2B:2E:E9:33:11 parent_ifindex=010.0.2.130:0 id=322 sec_id=25144 flags=0x0000 ifindex=22 mac=1A:DB:CF:41:5A:18 nodemac=06:B1:1E:1E:41:C7 parent_ifindex=010.0.2.14:0 id=409 sec_id=36899 flags=0x0000 ifindex=1278 mac=5A:7F:3A:9D:AD:3A nodemac=82:14:19:BE:2C:41 parent_ifindex=0 tokio = { version = “1”, features = [“full”] }[……….] |
Inspecting the eBPF Connection Tracking Table
This command reveals the internal kernel table that tracks every active TCP and UDP network flow (Connection Tracking). Unlike simple routing, Connection Tracking maintains the state of a connection, which is fundamental for stateful security policies and proper Service handling.
| magnus@linuxnode:~$ kubectl exec -n kube-system cilium-56v7z — cilium bpf ct list globalTCP OUT 10.0.2.183:48100 -> 10.0.3.245:6379 expires=6771427 Packets=0 Bytes=0 RxFlagsSeen=0x1a LastRxReport=6763422 TxFlagsSeen=0x1a LastTxReport=6763422 Flags=0x0010 [ SeenNonSyn ] RevNAT=21 SourceSecurityID=9056 IfIndex=0 BackendID=0TCP OUT 10.0.2.112:40524 -> 192.168.10.232:443 expires=6763393 Packets=0 Bytes=0 RxFlagsSeen=0x1b LastRxReport=6763383 TxFlagsSeen=0x1e LastTxReport=6763383 Flags=0x0013 [ RxClosing TxClosing SeenNonSyn ] RevNAT=0 SourceSecurityID=9056 IfIndex=0 BackendID=0TCP OUT 10.0.2.112:37844 -> 192.168.10.231:9993 expires=6763429 Packets=0 Bytes=0 RxFlagsSeen=0x1b LastRxReport=6763419 TxFlagsSeen=0x1e LastTxReport=6763419 Flags=0x0013 [ RxClosing TxClosing SeenNonSyn ] RevNAT=0 SourceSecurityID=9056 IfIndex=0 BackendID=0TCP OUT 10.0.2.112:50424 -> 10.0.3.245:6379 expires=6763361 Packets=0 Bytes=0 RxFlagsSeen=0x1a LastRxReport=6763350 TxFlagsSeen=0x1e LastTxReport=6763351 Flags=0x0013 [ RxClosing TxClosing SeenNonSyn ] RevNAT=21 SourceSecurityID=9056 IfIndex=0 BackendID=0[……….] |
Inspecting the BPF Datapath Traffic Metrics
The following shows the raw packet and byte counts tracked by the various eBPF programs attached to the network data path. This information is the foundation for the cluster’s network observability, often consumed by monitoring tools like Prometheus and Grafana.
| magnus@linuxnode:~$ kubectl exec -n kube-system cilium-56v7z — cilium bpf metrics listDefaulted container “cilium-agent” out of: cilium-agent, config (init), mount-cgroup (init), apply-sysctl-overwrites (init), mount-bpf-fs (init), clean-cilium-state (init), install-cni-binaries (init)REASON DIRECTION PACKETS BYTES LINE FILEInterface INGRESS 1577909674 1273267519975 1201 bpf_host.cInterface INGRESS 2695 269326 1217 bpf_host.cInterface INGRESS 413353237 924747123464 694 bpf_overlay.cInterface INGRESS 7890675 473440500 1120 bpf_host.cPolicy denied INGRESS 2 108 2110 bpf_lxc.cSuccess EGRESS 2252186 182426599 1766 bpf_host.cSuccess EGRESS 369728109 113135110713 53 encap.hSuccess EGRESS 5418 4706601 75 l3.hSuccess EGRESS 594038567 65878675134 1343 bpf_lxc.cSuccess INGRESS 518461634 118405270099 75 l3.hSuccess INGRESS 929568290 1042974689115 255 trace.h[……….] |
Conclusion
eBPF is now the foundational layer driving the next wave of Kubernetes efficiency and security. By offering deep context, minimal resource overhead, and guaranteed kernel stability, eBPF solves critical scaling and performance issues inherent to traditional tooling. It enables sidecar-free service meshes and real-time Intrusion Prevention Systems, moving operators Beyond Observability to achieve genuine K8s cost optimization and high performance. The Linux kernel, powered by eBPF, is cemented as the definitive, efficient, and programmable control point for modern cloud-native infrastructure.