Monitoring Kubernetes clusters has traditionally been challenging due to Kubernetes' networked nature. However, with eBPF it has become easier for teams to set up new clusters and collect precise information at the kernel level across the Kubernetes cluster.
Originally developed for network packet filtering, eBPF has now evolved into a powerful framework for performing dynamic tracing, security monitoring, and performance analysis within the kernel. It allows custom applications to operate in the kernel without altering the source code or reloading kernel modules. In this article, we will explore how eBPF works and can be used to improve Kubernetes monitoring, along with its advantages.
What is eBPF?
Let's first understand what is eBPF, and why it is important. 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 requiring the loading of kernel modules or modifying the source code.
Given the kernel's capability to monitor and manage the whole system, operating systems have typically been the optimal 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 Work?
In Kubernetes clusters, containers are frequently used to install services that continue to operate as long as the application is up and running, and eBPF offers a dependable method for monitoring services in the cluster. eBPF may also be used to audit Kubernetes cluster commands, offering a granular view of each command. It provides information on the initiator, timing, location, and other relevant facts.
Event-driven eBPF applications are linked to a code route. Hooks are particular triggers in the code path that run any associated eBPF programs when they are provided. Hooks can be found in network events and system calls.
When the code is activated, it is first compiled to BPF bytecode. In turn, before execution, the bytecode is examined to ensure that it does not produce a loop. This step protects the software from inadvertently or intentionally compromising the Linux kernel.
When a program is called at a hook, it performs assistance calls. These utility calls are functions that provide eBPF with several memory-accessing functionalities. The kernel must specify helper calls ahead of time, yet the number of accessible functions is continually growing.
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How to Use eBPF to Enhance Kubernetes Monitoring
eBPF applications are directly executed in Linux kernels. This allows them to easily track nearly any part of the OS's subsystems, including the CPU scheduler, networking, system calls, and so on. eBPF apps enable you to monitor and track virtually everything that occurs in the operating system.
Cluster services are typically provided in self-contained units called containers. Because containers are nothing more than processes that continue for as long as the application running within them is active, eBPF provides a consistent method for tracking the services operating in a Kubernetes cluster.
Furthermore, eBPF applications are independent of any external modules or dependencies. As a result, the performance overhead introduced by such applications to the usual workload of Linux programs is insignificant. Running a monitoring system on it adds little impact to your deployed workload. eBPF also enhances system visibility by providing a unified framework for recording processes, along with being quite safe.
When is It Suitable to Use eBPF for Monitoring?
The Kubernetes cluster is not scaled by eBPF. It delivers information that can assist administrators in managing growing operations. There are a few situations when employing eBPF for monitoring is an excellent choice.
Kernel Tracing
Custom code can be executed in the kernel using eBPF. Because everything is done in the kernel, tracking the actions and stages of the program becomes faster and more precise. Furthermore, because the programs are performed when an event occurs, the majority of actions are logged.
Common Monitoring Strategies May Be Ineffective
A monitoring tool may fail because of a bug. Even when typical monitoring tools fail, eBPF continues to provide visibility into requests and HTTP traffic.
To Optimize Packet Traffic Control
Packets normally go from source to destination in a conventional Linux networking route. They are, nevertheless, unaware of intricate routes. With eBPF and proper context, you can write kernel programs that guarantee the packet avoids difficult pathways and takes the shortest and best route to its destination.
Advantages of Using eBPF to Improve Monitoring
eBPF was initially used by organizations to increase security and observability while filtering network traffic. However, it has evolved into a more secure, easy, and high-performance method of implementing user-supplied code. Using eBPF for Kubernetes service offers several benefits. Here are some benefits of using eBPF.
Security
You can easily maintain your access control rules for code-level modifications since eBPF applications are not permitted to alter the kernel in any way. Otherwise, a kernel module would have raised a slew of security problems.
Convenience
It makes your monitoring efforts more convenient for you. You receive more granular information and kernel context than with any other Linux monitoring solution. You may also export the monitoring data and import it into third-party visualization tools.
Programmability
When compared to traditional instrumentation approaches, it allows you to perform actions on the logged data before dumping it. You have the option of storing data between eBPF events or selectively storing data as needed. This functionality enhances the overall usability of your monitoring system without introducing new dependencies.
Conclusion
The eBPF mechanism perfectly complements the standard BPF mechanism as it enhances security, while allowing for the deep observability features of standard BPF initiatives. It is an important asset for businesses seeking to enhance data protection and elevate their Kubernetes monitoring strategies. By harnessing the potential of eBPF, organizations can strengthen their data security posture and maintain a secure data environment.
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