How eBPF is Changing the Way We Monitor Kubernetes

The challenge of monitoring containers has been one of their main downsides. It is challenging to efficiently and effectively gather monitoring data from containers due to their ephemeral nature and abstraction from the systems that host them. Thankfully, the problem of container monitoring has a solution in the form of a promising, relatively new technology. Its name is eBPF, and there is a high probability that it will revolutionize current methods for keeping track of Docker and Kubernetes workloads.

In this article, we will cover what eBPF is, how it may help with Kubernetes monitoring, and why it's a better approach for container monitoring.

What is eBPF?

The Extended Berkeley Packet Filter (eBPF) is a cutting-edge Linux kernel feature that allows sandboxed programs to operate within the operating system kernel. It securely and effectively enhances the functionality of the kernel without you having to load kernel modules or modify the source code.

Given the kernel's capability to monitor and manage the whole system, operating systems have typically been the ideal area to incorporate security, observability, and networking capabilities. Because of its critical purpose and stringent security and stability requirements, the operating system kernel is resistant to change. As a result, operating system-level development is lagging behind improvements provided outside through eBPF.

How Does eBPF Help with Kubernetes Monitoring?

In a cluster, services are often distributed as independent units known as containers. eBPF offers a reliable solution for monitoring the services operating in a Kubernetes cluster since these containers are nothing more than processes that continue to operate as long as the application inside them is active.

Additionally, you may utilize eBPF to audit commands that were run within the Kubernetes cluster. eBPF can offer thorough visibility into each command, providing answers to queries about what occurred, when it transpired, who started it, where it occurred, and other details. For instance, it could be feasible to determine that a user used kubectl exec to log into a pod container, but it might be challenging to determine the precise parameters provided, such as the pod name, namespace, and other crucial data. You may quickly find out additional details about these commands using eBPF.

With eBPF, you have additional insight into your system since it gives a uniform foundation for recording operations. Also, it is quite secure. eBPF enables you to run secure code in the operating system kernel, unlike Linux kernel modules. These applications often undergo a number of checks to validate the code and guarantee its security before being loaded into the kernel area.

A Better Approach to Container Monitoring

Container monitoring is the process of continually gathering data and tracking the health of environments for containerized applications and microservices with the goal of enhancing their performance and ensuring smooth operation.

The Traditional Approach

Developers and IT experts have historically experienced the following difficulties when monitoring containers - a single application could include dozens, perhaps even hundreds, of different containers. It takes more time to deploy monitoring agents and get the necessary data from each container because each one must be watched independently.

When containers shut down, the data they contain is lost, and it's frequently difficult to determine with precision when a container would shut down. Because of this, you cannot regularly pull monitoring data, and you need a method for instantly gathering it from each container.

Host-based monitoring techniques don't function effectively because containers are isolated from the operating system of the servers that host them. Because they can migrate across hosts, it is difficult to install an agent on every server and utilize it to keep track of every container you have.

eBPF: The Next Generation of Kubernetes Monitoring

By monitoring each container without using a lot of resources, eBPF offers a solution for square rooting this problem. In contrast to executing programs in userland, where they lack direct access to kernel resources, eBPF is a Linux feature that was introduced in 2015. It enables applications to operate directly in the Linux kernel. eBPF applications use the fewest resources possible since they operate in the kernel. Additionally, they have access to any data generated by processes that are operating on the server where they are installed.

You could then create an eBPF software to monitor containers that intercept the processes related to each container and use it to gather monitoring data. You would have a monitoring system that consumes a lot fewer resources than conventional sidecar containers. So, you wouldn't have to give up any data you could gather for monitoring reasons in the process.

The kernel provides almost all the information you could possibly need about the state and functionality of each container. Using an eBPF-based method to monitor makes deployment and administration easier as well. An eBPF application may be executed on each node in your cluster instead of having to deploy and manage a number of sidecar containers.

How eBPF Simplifies Kubernetes Scaling

Teams can alleviate a number of the problems related to Kubernetes scalability by utilizing eBPF scripts to gather detailed kernel-level data from all the nodes in a Kubernetes cluster. Let us look at some of the advantages of using eBPF for Kubernetes scaling.

Particular Monitoring for Services

It is simple to keep an eye on particular processes with eBPF. The resource trends of each process operating within a cluster may be monitored by teams using eBPF. In addition, since every Kubernetes service is really just a process running on a node somewhere in the cluster, eBPF offers a trustworthy approach to monitor each service throughout the cluster using a single tool set.

Greater Depth Than in Logs

eBPF programs give more depth and insight than conventional logs and metrics because they gather very detailed, process-specific data. With eBPF, Kubernetes administrators can access more in-depth and granular data while also streamlining the collection of the monitoring data they need to grow clusters.

No Specialized Equipment

eBPF operates directly at the OS level and is deployed by default, in contrast to logs, which normally gather in Kubernetes via log agents that run in sidecar containers. This makes deployment simple. Additionally, because eBPF applications require such few resources when they are running, they provide very little resource overhead to the cluster, unlike sidecar containers.

Conclusion

eBPF has the potential to significantly enhance networking, security, and observability in Kubernetes. The process of building a more reliable infrastructure is made simpler by the use of eBPF since you don't need to add modules or update the kernel source code. It can assist in resolving several issues related to observability in a distributed system like Kubernetes by monitoring at the kernel level. Your data will have deeper insight, more context, and higher accuracy as a result, which will improve the management and optimization of your Kubernetes setup.