What is eBPF & what it means for Kubernetes monitoring

eBPF is a Linux kernel technology that allows programmable tracing and monitoring of system events. For Kubernetes monitoring, eBPF can collect detailed metrics about network traffic and resource usage within containers. This can provide valuable insights into application performance and help with troubleshooting issues. Additionally, eBPF can enforce security policies by monitoring and filtering network traffic at the kernel level. 

In this blog post, we will explore the use of eBPF in Kubernetes monitoring and how it can provide deep visibility into the network and system behavior of Kubernetes clusters.

What is eBPF?

eBPF is a game-changing technology that enables application developers to add new capabilities to an operating system at runtime by running sandboxed programs within it. The operating system ensures the safety and efficiency of the execution as if it were natively compiled. This has led to a multitude of eBPF-based projects, ranging from networking and observability to security functionality.

Today, eBPF is extensively used in improving network performance and load-balancing in modern data centers and cloud-native environments, extracting security data with minimal overhead, tracing applications, troubleshooting performance issues, enforcing preventive application, and container runtime security. 

There are endless possibilities for eBPF, and the technology is unlocking new innovations in the field.

How eBPF Works

Containers are commonly used to deploy services in a Kubernetes cluster which operate as long as the application is operational, and eBPF offers a reliable method for monitoring services in the cluster. eBPF can also be used for auditing commands executed in the Kubernetes cluster, thus providing a granular view of each command. It answers questions about the initiator, timing, location, and other pertinent details. 

For instance, eBPF can reveal specific parameters like the pod name and namespace that may not be apparent otherwise. eBPF offers a comprehensive framework for tracking processes, leading to more system visibility. It is highly secure, allowing for the execution of secure code within the operating system kernel. eBPF programs undergo rigorous security checks before being loaded into the kernel space, ensuring the code is secure.

The security checks for eBPF programs involve validating the code and ensuring its safety before loading it into the kernel space. Unlike Linux kernel modules, eBPF provides a secure method for executing code, reducing the likelihood of vulnerabilities and unauthorized access.

5 Reasons why eBPF is useful for Kubernetes Monitoring

1. Lightweight Performance Monitoring: eBPF provides low-overhead performance monitoring and observability, enabling developers to track performance metrics, diagnose issues, and debug code in real time without significant performance penalties.
2. Tracking Kubernetes Services: With eBPF, developers can track services deployed as containers in a Kubernetes cluster. Since containers are simply processes that run as long as the application in them is operational, eBPF provides a consistent and reliable tool for monitoring services running in a cluster.
3. Auditing Commands: You can also use eBPF for auditing commands executed in a Kubernetes cluster. It provides detailed visibility into each command, including who initiated it, when it happened, where it occurred, and other relevant information. This helps track down issues, identify unauthorized access attempts, and debug security incidents.
4. Increased System Visibility: eBPF also offers a unified framework for tracking processes, resulting in increased system visibility. It also enables the extraction of valuable metrics from the kernel, providing developers with insights into kernel-level behavior that can be used for performance tuning, troubleshooting, and security auditing.
5. Enhanced Security: eBPF is a secure option for executing code within the operating system kernel. Unlike Linux kernel modules, eBPF programs undergo several security checks before being loaded into the kernel space to ensure that the code is safe and secure. 

Use cases of eBPF

• Networking

One of the significant areas where it is useful is networking. It facilitates advanced packet processing solutions by running eBPF programs that enhance the networking process at a packet level. Additionally, eBPF can be used for load balancing and improving network performance at a lower level than containers and network address translation.

• Observability

Another crucial domain where eBPF can be applied is Kubernetes observability. As the Kubernetes architecture comprises multiple underlying components, visibility for security and performance is crucial. eBPF can provide deeper insight into events and collect custom metrics from various system sources without incurring additional overhead. Detailed event capturing helps monitor container attributes and cluster components closely.

• Distributed Tracing

Distributed tracing is another use case for eBPF. As Kubernetes runs distributed container workloads at scale, tracking network requests becomes more complex. eBPF programs can be run in the kernel and attached to kernel tracepoints to trace events for unique requests at a detailed level.

• Security

eBPF can also be used for enhanced security. To monitor system events closely and leverage native capabilities for protective measures, eBPF can capture desired security observability events using kernel-native technology to obtain container attributes and enforce network security policies. This is a safer alternative to implementing kernel modules, which can be risky if something goes wrong.

Conclusion

eBPF is a technology that can greatly improve observability, networking, and security in Kubernetes. With eBPF, you don’t need to change kernel source code or add modules, which simplifies the process of creating a more robust infrastructure. By monitoring at the kernel level, eBPF can help solve many of the challenges associated with observability in a distributed system like Kubernetes. This provides deeper visibility, more context, and greater accuracy in your data, helping you to better manage and optimize your Kubernetes environment.