Section 1: Introduction 📌

Key Points

 Cloud-native platforms (like Kubernetes) have become the foundation for modern
mobile networks.
 eBPF (Extended Berkeley Packet Filter) is transforming cloud-native networking,
security, and observability by enabling high-performance, in-kernel
instrumentation.
 eBPF provides:
o Dynamic kernel programmability without modifying kernel source code.
o Safe execution (sandboxed programs that can't crash the kernel).
o High-performance monitoring (low overhead, real-time observability).
 The paper introduces "Sauron," an eBPF-based platform for:
o Energy consumption monitoring in cloud-native functions.
o Performance monitoring for 5G applications.
o Security enforcement against unauthorized access.

Discussion

 eBPF is crucial for Kubernetes because it allows monitoring without modifying

applications.
 The introduction hints that Sauron aims to replace traditional, high-overhead
observability tools in cloud-native telecom networks.

Section 2: The Powers of eBPF 🚀

This section outlines the fundamentals of eBPF and its transformative impact on cloud-
native systems and telecommunications networks.

📌 Key Points

1️⃣ eBPF Project Layout

The eBPF ecosystem consists of three main components:

1. eBPF Programs (Kernel-level)

o These programs run in the Linux kernel and are triggered by system or network
events (e.g., packet arrival, syscall execution).
2. User-space Programs
o These programs load eBPF code into the kernel and interact with eBPF data.
3. BPF Maps
o These serve as storage and communication between kernel and user space, allowing
real-time data sharing.
2️⃣ How eBPF Works

The development & runtime workflow of an eBPF program:

1. Developers write eBPF code in C or Rust.

2. LLVM compiles the code into eBPF bytecode.
3. User-space loaders (e.g., libbpf, BCC) inject the program into the kernel.
4. The eBPF verifier checks the program for safety (prevents infinite loops, memory
corruption).
5. Just-In-Time (JIT) compiler converts bytecode into machine code.
6. The eBPF program runs inside the kernel, triggered by events.
7. BPF Maps enable data sharing with user-space applications.

This modular approach ensures portability across different Linux kernel versions.

3️⃣ eBPF Hooks and Use Cases

eBPF integrates with various hook points in the Linux kernel:

Hook Type Functionality

XDP (eXpress Data Path) Fast packet filtering & DDoS mitigation at the NIC level

Traffic Control (TC) Ingress/Egress packet processing

Socket Hooks Observing socket-level traffic (L4 monitoring)

System Calls (Tracepoints & Kprobes) Security monitoring, syscall tracing

LSM (Linux Security Module) Hooks Enforce security policies (introduced in Kernel 5.7)

These hooks allow eBPF to be used for:

 Network Security (firewalling, DDoS mitigation)

 Performance Monitoring (latency tracking, packet tracing)
 Cloud-Native Observability (monitoring Kubernetes workloads)
 Runtime Security (intrusion detection, file access monitoring)

4️⃣ Why eBPF?

📊 Comparison with Traditional Methods

Feature iptables / tc eBPF

Performance Slower, high CPU usage Faster, low CPU overhead

Flexibility Static rules, hard to modify Dynamic, real-time programmability

Visibility Limited logging & tracing Full system observability

 Feature iptables / tc eBPF

Security High attack surface Sandboxed execution, reduced risk

🔥 Advantages of eBPF in Cloud-Native Environments

1. Minimal Overhead 🏎️→ Runs directly in the kernel, avoiding expensive user-space context
switches.
2. High Performance 📊 → Enables real-time observability and security with low latency.
3. Extensibility 🔧 → New eBPF programs can be dynamically loaded and updated without
rebooting.
4. Portability 🖥️→ Works across different kernel versions (via BPF CO-RE technology).
5. Security 🔐 → Verifier prevents kernel crashes, unsafe memory access.

5️⃣ eBPF for Kubernetes

 In a Kubernetes cluster, every containerized application shares the host kernel.

 eBPF allows observability across all pods and containers without modifying applications.
 Use Cases in Kubernetes:
o eBPF-based Container Networking (Replacing Kube-Proxy with better performance)
o eBPF-based Security Monitoring (Detecting malicious processes, enforcing policies)
o eBPF-based Observability (Measuring latency, packet drops, DNS resolution times)

📌 Key Takeaways

 eBPF is revolutionizing cloud-native networking, security, and observability.

 It replaces older, inefficient tools like iptables, tcpdump, and kernel modules.
 The verifier ensures safety, making it an ideal tool for high-performance monitoring.
 Kubernetes benefits significantly from eBPF, providing deep observability without
modifying workloads.

Deep Dive: eBPF for Kubernetes 🏗️🚀

This section explores how eBPF enhances Kubernetes networking, observability, and
security by leveraging its in-kernel programmability. Kubernetes (K8s) operates differently
than traditional server architectures, and eBPF enables better control over the complex
networking and security challenges that arise in cloud-native environments.

1️⃣ Why is eBPF Important for Kubernetes?

In Kubernetes, multiple workloads run on the same host kernel but remain isolated in pods
and namespaces. Each pod requires networking, security policies, and observability tools.
Traditional methods, such as iptables, Kube-proxy, and Service Meshes, have limitations:
🔴 Problems with Traditional Approaches

1. Performance Issues ⚡
o iptables rules grow exponentially with more services, leading to slow packet
processing.
o Kube-Proxy uses iptables (or IPVS), introducing latency in service discovery and
load balancing.

2. Limited Observability 👀
o Traditional tools (e.g., tcpdump, cAdvisor, Prometheus) require user-space
instrumentation.
o Sidecar-based service meshes (e.g., Istio, Linkerd) introduce latency and high
resource overhead.

3. Complex Security Policies 🔐

o Network policies (CNI plugins) rely on iptables, which makes policy enforcement
resource-intensive.
o Traditional security tools cannot efficiently track system calls, network activity, or
filesystem access.

2️⃣ eBPF’s Role in Kubernetes

eBPF enhances Kubernetes by replacing iptables-based networking, enabling lightweight

observability, and improving security enforcement.

🔹 Networking: Replacing Kube-Proxy with eBPF

By default, Kubernetes uses Kube-Proxy to handle service-to-service communication. Kube-

Proxy relies on iptables/IPVS, which slows down packet forwarding as the number of
services grows.

✅ eBPF-based Kube-Proxy Replacement

Instead of using iptables, eBPF can:

 Attach to XDP and Traffic Control (TC) hooks to process packets directly in the kernel.
 Bypass iptables and redirect packets to the correct pod immediately.
 Enable direct pod-to-pod communication without NAT overhead.

Example: Cilium as an eBPF-based CNI Plugin

 Cilium uses eBPF to:

o Replace Kube-Proxy and speed up service discovery.
o Enable high-performance load balancing at the kernel level.
o Provide per-pod network policies without using iptables.
📌 Benefits: ✔ Faster packet processing (reduces latency). ✔ No need for iptables (removes
complexity). ✔ Direct Service-to-Service Communication (improves performance).

🔹 Service Mesh: Replacing Sidecars with eBPF

Traditional Service Meshes (e.g., Istio, Linkerd) use sidecars (Envoy proxies) to:

 Perform load balancing and mutual TLS encryption.

 Collect tracing, logging, and metrics.
 Enforce security policies (e.g., API gateway rules).

However, sidecars have serious drawbacks:

 High latency: Traffic must traverse multiple layers (app → sidecar → network → sidecar →
app).
 High resource usage: Each pod runs an additional sidecar container.
 Complex configuration: Managing service mesh policies requires extra effort.

✅ eBPF-based Service Mesh

 eBPF removes the need for sidecars by:

o Intercepting traffic in the kernel before it reaches the network stack.
o Applying security policies (TLS, authentication) without user-space proxies.
o Reducing CPU and memory usage by eliminating redundant proxy layers.

📌 Example: Cilium Service Mesh

 Moves service mesh functions into the kernel using eBPF.

 Enables mTLS encryption directly at the socket level.
 Only routes L7 traffic to the proxy when necessary.

✔ Lower latency (bypasses user-space proxies). ✔ Better resource efficiency (no extra
sidecar containers). ✔ Improved security (kernel-level policy enforcement).

🔹 Security: eBPF for Runtime Observability & Policy Enforcement

Problem: Kubernetes Lacks Deep Security Visibility

 Kubernetes Network Policies only control L3/L4 traffic (IP, ports) but not process-level
activity.
 Traditional security tools lack kernel-level insights into system calls, file access, and network
behavior.

✅ eBPF-based Security (Replacing Host-Based IDS)

eBPF can:
 1. Monitor system calls (execve, open, read, write) to detect malicious behavior.
2. Enforce security policies at the kernel level (before a syscall executes).
3. Detect network anomalies (e.g., DNS hijacking, cryptojacking).
4. Prevent unauthorized file access (open, chmod, unlink).

📌 Example: Falco & Cilium Tetragon

 Falco uses eBPF to detect unexpected system behavior in Kubernetes workloads.

 Tetragon extends eBPF to enforce runtime security policies and block malicious activities.

✔ No need for intrusive kernel modules. ✔ Real-time attack detection & response. ✔
Better visibility into Kubernetes processes.

3️⃣ eBPF Use Cases in Kubernetes

Feature Traditional Approach eBPF-Based Approach

Packet Filtering iptables (slow) XDP (high-speed filtering)

Load Balancing IPVS/Kube-Proxy eBPF (direct pod communication)

Network Observability tcpdump, Prometheus eBPF (low-overhead tracing)

Service Mesh Sidecars (Envoy) eBPF-based Service Mesh

Security Policies Network Policies eBPF LSM (real-time blocking)

Syscall Monitoring Strace, AuditD eBPF-based Runtime Security

4️⃣ Summary: Why eBPF is the Future of Kubernetes Monitoring

✔ Replaces inefficient tools like iptables, Kube-Proxy, and Sidecars. ✔ Delivers real-
time observability with minimal overhead. ✔ Provides kernel-level security for
Kubernetes workloads. ✔ Reduces latency and resource usage in cloud-native
applications.

Slidezzzz :

Optimized eBPF Presentation (Slides 2-31)

🔹 Introduction to eBPF
Why eBPF Matters in Cloud-Native Environments

 Cloud-native platforms (e.g., Kubernetes) are the foundation for modern applications.
 Traditional monitoring tools introduce overhead and lack deep kernel-level insights.
 eBPF (Extended Berkeley Packet Filter) enables:
o In-kernel programmability without modifying the kernel source code.
o Safe execution via sandboxed environments.
o High-performance monitoring with low overhead.
 eBPF is essential in Kubernetes for security, observability, and networking.

🔹 eBPF Architecture & Components

Core Components of eBPF

1️⃣ eBPF Programs (Kernel-Level Code)

 Small programs executed within the Linux kernel, triggered by system events (e.g.,
packet arrival, syscalls).
 Written in C or Rust, compiled into eBPF bytecode.

2️⃣ User-Space Programs

 Load and manage eBPF programs.

 Interact with kernel-level eBPF using tools like bpftool, BCC, or libbpf.

3️⃣ BPF Maps (Kernel-User Communication)

 Data-sharing mechanism between kernel and user space.

 Enables real-time metrics collection and analysis.

4️⃣ eBPF Verifier

 Ensures safety before execution:

o ✅ Memory safety (no invalid access)
o ✅ Loop constraints (bounded execution)
o ✅ Security rules (no unauthorized modifications)
o ✅ Stack/register limits (ensures efficient execution)
 If verification fails, the program is rejected.

5️⃣ Just-In-Time (JIT) Compiler

 Converts eBPF bytecode into native machine code.

 Enables faster execution by running compiled machine code instead of interpreting
bytecode.
🔹 eBPF Execution Flow
How an eBPF Program Runs (Step-by-Step)

1️⃣ Developer writes an eBPF program (C/Rust). 2️⃣ Compiled into eBPF bytecode using
Clang + LLVM. 3️⃣ Passed through eBPF Verifier for safety checks. 4️⃣ If verified, JIT
compiles it to native machine code. 5️⃣ Program is loaded into the kernel and attached to an
event hook. 6️⃣ Triggered by events like network packets, syscalls, or tracepoints. 7️⃣ eBPF
program collects & processes data, storing results in BPF Maps. 8️⃣ User-space applications
read data from BPF Maps for monitoring & analysis.

🔹 eBPF Hook Points & Use Cases

Where Can eBPF Programs Attach?

✔ System Calls (syscalls) → Monitor or modify process behavior. ✔ Networking (XDP,

TC, Sockets) → Packet filtering, load balancing, firewalls. ✔ Kernel Tracepoints &
Functions (kprobes, uprobes) → Debugging & observability. ✔ Scheduling &
Performance Events → Process scheduling, CPU performance monitoring.

Real-World Applications of eBPF

1️⃣ Network Security

 DDoS Mitigation → Drop malicious packets before they reach user space.
 Firewall Policies → Enforce security at the kernel level.
 Traffic Filtering → Optimize routing decisions dynamically.

2️⃣ Performance Monitoring & Tracing

 Latency Tracking → Identify slow system calls & bottlenecks.

 Packet Tracing → Monitor real-time network traffic.
 CPU & Memory Profiling → Analyze resource usage with low overhead.

3️⃣ Observability in Cloud-Native Workloads

 Monitor Kubernetes Pods without modifying applications.

 Collect real-time metrics for troubleshooting and performance tuning.
 Reduce observability overhead compared to traditional logging/tracing solutions.

🔹 eBPF in Kubernetes
Replacing iptables-Based Networking
🔸 Traditional Kubernetes Networking (Kube-Proxy Limitations)

 Uses iptables/IPVS, which slows down as the number of services grows.

 Packet forwarding overhead increases, leading to latency issues.

🔹 eBPF-Based Kube-Proxy Replacement

 Processes packets in the kernel using XDP & TC hooks.

 Bypasses iptables, reducing overhead & improving performance.
 Enables direct Pod-to-Pod communication without NAT delays.

eBPF in Kubernetes Service Mesh

🔸 Problems with Traditional Sidecar-Based Service Meshes

🚫 High latency → Traffic traverses multiple layers (app → sidecar → network → sidecar →
app).
🚫 High resource consumption → Sidecars require additional CPU & memory per pod.
🚫 Complexity → Managing service mesh policies requires extra configuration.

🔹 eBPF-Based Service Mesh

✅ Intercepts traffic in the kernel before reaching user space.

✅ Applies security policies (TLS, authentication) dynamically.
✅ Reduces CPU & memory overhead (no extra sidecar containers).

✔ Example: Cilium Service Mesh

 Moves service mesh logic inside the kernel using eBPF.

 Enables mTLS encryption at the socket level.
 Only routes L7 traffic to proxies when necessary.

🔹 Challenges & Limitations of eBPF

🔸 Complexity → Requires deep system knowledge (tools like BCC simplify it).
🔸 Kernel Compatibility → Some Linux distributions lack full support.
🔸 Security Restrictions → Must pass strict verification before execution.

✔ Key Takeaway: eBPF is powerful but requires careful design to avoid potential pitfalls.

🔹 Conclusion
 eBPF is revolutionizing security, observability, and networking in cloud-native
environments.
 It enables low-overhead, high-performance monitoring directly inside the kernel.
  In Kubernetes, eBPF replaces inefficient tools like iptables & sidecars, improving
performance & security.
 Despite challenges, eBPF is the future of cloud-native monitoring.

🚀 Mastering eBPF will unlock advanced capabilities for next-gen cloud infrastructure.