eBPF for DevOps Engineers: Observability, Security, and Networking Without Kernel Modules

eBPF (extended Berkeley Packet Filter) is the most significant Linux kernel technology of the decade. It lets you run sandboxed programs inside the kernel without modifying kernel source code or loading kernel modules. This means you can add custom observability, security monitoring, and networking logic to a running production system — safely, efficiently, and without rebooting. It's the technology behind Cilium (Kubernetes networking), Falco (runtime security), Pixie (observability), and dozens of other tools that are reshaping infrastructure engineering.

For DevOps engineers, eBPF is important not because you'll write eBPF programs (though you might), but because an increasing number of production tools are built on eBPF, and understanding how it works helps you deploy, debug, and optimize them. This guide covers what eBPF is, how it works, and the practical tools you can deploy today.

What eBPF Actually Is (Without the Hype)

Think of eBPF as a safe, programmable extension point in the Linux kernel. Traditionally, if you wanted to do something the kernel didn't support — custom packet filtering, syscall tracing, performance profiling — you had two options: (1) modify the kernel source and recompile (impractical for production), or (2) write a kernel module (risky — a bug crashes the entire system). eBPF provides a third option: write a small program in a restricted C-like language, compile it to eBPF bytecode, and load it into the kernel. The kernel's eBPF verifier checks the program for safety (no infinite loops, no invalid memory access, bounded execution time) before allowing it to run.

eBPF programs attach to specific kernel events (called "hooks"): network packet arrival, syscall entry/exit, function calls, tracepoints, and more. When the event occurs, the eBPF program runs, can inspect data, collect metrics, modify packets, or make security decisions — all at kernel speed (nanoseconds, not milliseconds).

eBPF for Networking: Cilium

Cilium has become the standard networking solution for Kubernetes. It replaces kube-proxy and iptables with eBPF programs that handle load balancing, network policy enforcement, and service mesh features directly in the kernel. The result: dramatically better performance and scalability compared to iptables-based networking.

Why this matters for DevOps:

Performance: iptables rules are evaluated linearly — if you have 10,000 services, every packet traverses 10,000+ rules. Cilium's eBPF programs use hash maps for O(1) lookups regardless of the number of services. Organizations with large clusters report 50-80% latency reduction after switching to Cilium.

Visibility: Cilium provides Hubble, an eBPF-powered observability layer that gives you real-time visibility into every network flow in your cluster — without injecting sidecars or modifying applications. You can see which pods are communicating, the protocols they're using, DNS queries, HTTP request/response details, and latency metrics.

# Install Cilium on a Kubernetes cluster
helm repo add cilium https://helm.cilium.io/
helm install cilium cilium/cilium --version 1.16.0   --namespace kube-system   --set kubeProxyReplacement=true   --set hubble.enabled=true   --set hubble.relay.enabled=true   --set hubble.ui.enabled=true

# Verify installation
cilium status

# View real-time network flows with Hubble
hubble observe --all
hubble observe --pod my-namespace/my-pod --protocol http
hubble observe --verdict DROPPED  # See blocked traffic

# Network Policy using Cilium (more powerful than Kubernetes NetworkPolicy)
apiVersion: cilium.io/v2
kind: CiliumNetworkPolicy
metadata:
  name: backend-policy
  namespace: production
spec:
  endpointSelector:
    matchLabels:
      app: backend-api
  ingress:
    - fromEndpoints:
        - matchLabels:
            app: frontend
      toPorts:
        - ports:
            - port: "8080"
              protocol: TCP
          rules:
            http:
              - method: "GET"
                path: "/api/v1/.*"
              - method: "POST"
                path: "/api/v1/orders"

eBPF for Security: Runtime Threat Detection

Traditional security tools monitor at the application level (web application firewalls, log analysis) or the network level (IDS/IPS). eBPF enables kernel-level security monitoring that can detect threats that application-level tools miss: container escapes, privilege escalation, rootkit installation, cryptominer deployment, and unauthorized file access.

Falco (CNCF project, originally from Sysdig) uses eBPF to monitor syscalls and detect suspicious behavior in real-time. Tetragon (from the Cilium team) provides security observability and runtime enforcement using eBPF.

# Falco rules for Kubernetes security monitoring

# Detect container escape attempts
- rule: Container Escape via mount
  desc: Detect attempts to mount the host filesystem from within a container
  condition: >
    evt.type = mount and container and
    (evt.arg.source startswith /proc or evt.arg.source startswith /sys)
  output: >
    Container escape attempt via mount
    (user=%user.name command=%proc.cmdline container=%container.name
     image=%container.image.repository source=%evt.arg.source)
  priority: CRITICAL

# Detect cryptominer processes
- rule: Detect Cryptominer
  desc: Detect processes commonly associated with cryptocurrency mining
  condition: >
    spawned_process and container and
    (proc.name in (xmrig, minerd, minergate, cpuminer) or
     proc.cmdline contains "stratum+tcp" or
     proc.cmdline contains "pool.minexmr")
  output: >
    Cryptominer detected (user=%user.name command=%proc.cmdline
     container=%container.name image=%container.image.repository)
  priority: CRITICAL

# Detect sensitive file access
- rule: Read Sensitive File
  desc: Detect read of sensitive files in containers
  condition: >
    open_read and container and
    (fd.name = /etc/shadow or fd.name = /etc/passwd or
     fd.name startswith /run/secrets)
  output: >
    Sensitive file read (user=%user.name file=%fd.name
     container=%container.name image=%container.image.repository)
  priority: WARNING

eBPF for Observability: Zero-Instrumentation Monitoring

Traditional application monitoring requires instrumenting your code with metrics libraries, tracing SDKs, and logging statements. eBPF-based observability tools can extract the same information without any code changes — they observe the application from the kernel level.

Tools like Grafana Beyla and Pixie automatically detect and monitor HTTP requests, gRPC calls, database queries, DNS lookups, and TLS handshakes by attaching eBPF programs to the relevant kernel hooks. You get latency distributions, error rates, throughput metrics, and distributed traces without adding a single line of instrumentation code.

This is particularly valuable for polyglot environments (Go, Python, Java, Node.js services all monitored the same way), legacy applications that can't be easily modified, and third-party services where you don't have source code access.

Practical eBPF Tools You Can Deploy Today

bpftrace: The "awk for eBPF." A high-level scripting language for creating eBPF programs on the fly. Perfect for ad-hoc performance investigation:

# Count syscalls by process
sudo bpftrace -e 'tracepoint:raw_syscalls:sys_enter { @[comm] = count(); }'

# Trace file opens with latency
sudo bpftrace -e '
tracepoint:syscalls:sys_enter_openat {
  @start[tid] = nsecs;
  @filename[tid] = str(args->filename);
}
tracepoint:syscalls:sys_exit_openat /@start[tid]/ {
  $latency = (nsecs - @start[tid]) / 1000;
  printf("%-20s %-6d %10d us  %s\n", comm, pid, $latency, @filename[tid]);
  delete(@start[tid]);
  delete(@filename[tid]);
}'

# Histogram of disk I/O latency
sudo bpftrace -e '
tracepoint:block:block_rq_issue { @start[args->dev, args->sector] = nsecs; }
tracepoint:block:block_rq_complete /@start[args->dev, args->sector]/ {
  @usecs = hist((nsecs - @start[args->dev, args->sector]) / 1000);
  delete(@start[args->dev, args->sector]);
}'

kubectl-trace: Run bpftrace programs on Kubernetes nodes from kubectl. No need to SSH into nodes:

# Trace all TCP connections from a specific pod
kubectl trace run node/worker-1 -e '
kretprobe:tcp_v4_connect /retval == 0/ {
  @[comm, ntop(args->saddr), ntop(args->daddr)] = count();
}'

When to Use eBPF-Based Tools

eBPF tools are not a replacement for application-level instrumentation — they're complementary. Use eBPF-based tools when: you need visibility without code changes, you're debugging kernel-level performance issues (syscall latency, network packet drops, disk I/O bottlenecks), you need security monitoring that can't be bypassed by application-level techniques, or you're working with applications you can't modify (third-party, legacy, compiled binaries).

Stick with application-level instrumentation for: business-level metrics (orders per minute, conversion rates), custom application-specific tracing, and structured logging with business context.

Kernel version requirements: Most eBPF features require Linux kernel 5.4+. For full functionality (including BTF — BPF Type Format for portable programs), use kernel 5.8+. Check your kernel version with uname -r. All major cloud providers' latest OS images support eBPF.

ZeonEdge helps organizations deploy eBPF-based networking, security, and observability tools on Kubernetes and bare-metal infrastructure. Contact our infrastructure team for a consultation.