Using eBPF in Kubernetes for Improved Observability and Security

eBPF Kubernetes integration represents a revolutionary approach to monitoring and securing containerized environments. Extended Berkeley Packet Filter (eBPF) allows running isolated programs within the operating system kernel, extending OS abilities without loading new modules or modifying its source code. This kernel-level access provides unprecedented visibility into your Kubernetes clusters, enabling comprehensive monitoring of network traffic, system calls and application behavior without traditional instrumentation overhead. This makes eBPF essential for organizations seeking optimal performance while gaining deeper insights into their dynamic containerized infrastructure.

What is eBPF for Kubernetes?

eBPF for Kubernetes is a powerful technology that leverages kernel-level programming to provide deep observability and security insights within containerized environments. This technology operates as a lightweight virtual machine inside the Linux kernel. It enables safe monitoring of system events, including function calls, system calls and network traffic.

In Kubernetes environments, eBPF programs can monitor container behavior and track network communications between pods. This approach eliminates the need for manual instrumentation while providing comprehensive visibility across your cluster. The integration of eBPF with modern Kubernetes observability platforms creates a powerful foundation for understanding complex containerized systems.

Unlike traditional monitoring solutions, eBPF programs execute directly within the kernel space. This provides unfiltered access to system events and network communications. The result is real-time analysis without the performance overhead of conventional monitoring approaches.

Where did eBPF come from?

eBPF evolved from the original Berkeley Packet Filter (BPF), which was designed for network packet filtering. eBPF expanded these capabilities far beyond networking to include system call monitoring, performance analysis and security enforcement.

This shift has unlocked innovation in full-stack observability, performance troubleshooting and security. The adoption of eBPF in K8s environments has accelerated due to its ability to provide granular insights without affecting application performance.

The evolution from BPF to eBPF represents a significant leap in kernel programming capabilities. Originally, BPF was limited to simple packet filtering with a restricted instruction set. eBPF transformed this into a general-purpose virtual machine capable of running complex programs safely within kernel space.

How does eBPF for Kubernetes monitoring work?

eBPF for Kubernetes monitoring operates through a sophisticated system of kernel-level programs that capture and analyze system events in real-time. Programs built on eBPF effortlessly connect with different system events, including function calls in libraries, system calls and network traffic. This architecture enables comprehensive monitoring without requiring changes to your application code.

The monitoring process begins when eBPF programs are loaded into the kernel through a verification process that ensures safety and security. Once loaded, these programs attach to specific kernel hooks or events, allowing them to intercept and analyze data flows as they occur. This approach provides immediate access to system-level information that would otherwise require complex instrumentation or external monitoring agents.

Data collection at the kernel level

One of eBPF’s standout features is that it helps SUSE Cloud Observability track all key metrics without the need for manual instrumentation. Plus, the ability to process data at the kernel level drastically reduces the overhead of transferring data between kernel and user space. This approach delivers faster insights while minimizing the performance impact on your Kubernetes workloads.

The kernel-level data collection process operates through a series of optimized data structures and communication channels. eBPF programs utilize maps and ring buffers to efficiently store and transfer collected data to user-space applications. This design minimizes memory usage while ensuring that critical monitoring data reaches analysis systems with minimal latency.

Network analysis capabilities

Our primary focus with eBPF is on comprehensive network analysis. By examining the data flow between processes, even across clusters and clouds, we gain insights into service interactions that were previously hidden. This includes real-time metrics on throughput, latency and error rates for protocols like HTTP, HTTPS, MongoDB and Kafka, even when the connections are encrypted.

Advanced network analysis through eBPF extends beyond simple packet inspection to include deep protocol analysis and service discovery. The technology can automatically identify service dependencies, track request flows across microservice boundaries and detect anomalous communication patterns that might indicate security threats or performance issues. This capability proves particularly valuable in complex Kubernetes monitoring scenarios where traditional approaches struggle to maintain visibility.

Multi-cluster observability

For complex environments spanning multiple clusters and clouds, we’ve developed innovative techniques to maintain observability. By injecting trace headers, we correlate data across various setups, offering a comprehensive view of your entire infrastructure.

Multi-cluster observability requires sophisticated data correlation techniques that can maintain context across distributed systems. eBPF programs can inject custom metadata into network communications, enabling tracking of requests as they traverse cluster boundaries, cloud regions and different infrastructure providers. This approach ensures that organizations maintain complete visibility even in the most complex hybrid and multi-cloud deployments.

What are the benefits of using eBPF technology?

eBPF technology delivers numerous advantages for organizations running Kubernetes environments, particularly in areas of observability, security and performance optimization. 

Enhanced observability without instrumentation

eBPF eliminates the traditional burden of manual instrumentation while providing deeper visibility than conventional monitoring approaches. Our eBPF-based solution doesn’t just track network traffic, it decodes and distills essential information. This means you get actionable insights into request paths, status codes and topic names for optimizing system performance without requiring code modifications or deployment changes.

Reduced performance overhead

Traditional monitoring solutions often introduce significant performance overhead due to data collection and processing requirements. eBPF minimizes this impact by processing data directly within the kernel space, eliminating frequent context switches between user and kernel spaces and significantly reducing CPU usage, memory consumption and network bandwidth requirements for monitoring infrastructure.

Real-time security monitoring

eBPF enables real-time detection of security threats and anomalies at the kernel level. This allows security teams to identify and respond to potential threats before they compromise system integrity. The technology provides visibility into system calls, file access patterns and network communications with microsecond-level granularity, ensuring that even advanced persistent threats cannot easily hide their activities.

Comprehensive protocol support

This includes real-time metrics on throughput, latency and error rates for protocols like HTTP, HTTPS, MongoDB and Kafka, even when the connections are encrypted. This broad protocol support ensures visibility across diverse application architectures and communication patterns, enabling organizations to monitor proprietary communication systems alongside common protocols without compromising security or requiring key management systems.

Popular eBPF use cases

eBPF technology supports numerous use cases within Kubernetes environments, ranging from performance monitoring to security enforcement and network analysis.

• Performance monitoring and optimization: Organizations use eBPF to monitor application performance metrics, including CPU usage, memory consumption and network latency. Various Kubernetes eBPF tools enable teams to optimize resource allocation and identify performance bottlenecks before they impact user experience.
• Security monitoring and threat detection: eBPF programs can monitor system calls and network traffic to detect suspicious activities such as unauthorized access attempts, privilege escalation or unusual network communications. This capability supports proactive security measures and rapid incident response.

• Network policy enforcement: eBPF enables dynamic enforcement of network policies within Kubernetes clusters. Organizations can implement fine-grained traffic control and access restrictions based on real-time analysis of network communications.
• Application dependency mapping: Teams leverage eBPF to automatically discover and map application dependencies within Kubernetes environments. This visibility helps with troubleshooting, capacity planning and understanding service interactions across complex microservice architectures.
• Resource utilization analysis: eBPF provides granular insights into resource utilization patterns, enabling teams to optimize cluster resource allocation and reduce infrastructure costs through better capacity planning.

How to implement eBPF in Kubernetes

Implementing Kubernetes eBPF requires careful planning and the right tooling to ensure successful deployment and operation.

Prerequisites and requirements

Before implementing eBPF in your K8s environment, ensure your cluster nodes run compatible Linux kernel versions (typically 4.4 or later for basic eBPF support, with newer kernels providing enhanced features). Verify that your container runtime supports eBPF programs and that the necessary kernel modules are loaded. Select appropriate Kubernetes eBPF tools that match your monitoring and security requirements.

Deployment strategies

Deploy eBPF programs using DaemonSets to ensure consistent monitoring across all cluster nodes. This approach guarantees that every node in your cluster has the necessary eBPF programs loaded and running. Consider using init containers to handle eBPF program loading and verification before main application containers start.

Modern deployment strategies for eBPF include automated rollout mechanisms that can validate program functionality before full deployment. These strategies often incorporate health checks and rollback capabilities to ensure that eBPF program deployment doesn’t disrupt cluster operations. Organizations can implement canary deployments for eBPF programs, testing new monitoring capabilities on a subset of nodes before cluster-wide deployment.

Configuration and management

Configure eBPF programs with appropriate resource limits and security contexts to prevent interference with other cluster workloads. Implement proper logging and monitoring for the eBPF programs themselves to ensure they operate correctly and don’t consume excessive resources.

Effective configuration management requires understanding the specific requirements of your Kubernetes environment, including node capabilities, security policies and performance constraints. Teams should establish configuration baselines that can be automatically applied across different cluster environments while allowing for environment-specific customizations when necessary.

Integration with observability platforms

Integrate eBPF data collection with your existing observability infrastructure. Tools like Kubernetes security platforms can consume eBPF-generated metrics and logs to provide comprehensive cluster insights.

Integration strategies should consider data format compatibility, ingestion rates and storage requirements for eBPF-generated data. Organizations often implement data transformation layers that can normalize eBPF data for consumption by multiple observability platforms simultaneously, maximizing the value of collected information across different operational teams.

Are there drawbacks to using eBPF in Kubernetes?

While eBPF offers significant advantages, organizations should consider potential challenges and limitations before implementation.

Kernel compatibility requirements

eBPF programs require specific kernel versions and configurations to function properly. The eBPF program failed to load on Google’s Container-optimized OS (COS) due to a compilation error, not a verification issue. Organizations may encounter compatibility issues when deploying across diverse node configurations or older kernel versions.

Complexity in debugging and troubleshooting

When eBPF programs fail to load or operate correctly, debugging can be challenging due to limited visibility into kernel-level operations. The issue was traced to the JIT compiler, which was unable to compile the code. Teams need specialized knowledge and tools to effectively troubleshoot eBPF-related issues.

Security configuration considerations

Different security configurations can affect eBPF program loading and execution. COS sets net.core.bpf_jit_harden to 2 (enabled for all users), whereas Ubuntu has it set to 0 (disabled). These variations can cause unexpected behavior across different node types within the same cluster.

Resource consumption monitoring

While eBPF programs are designed to be lightweight, improper implementation or configuration can lead to increased CPU and memory usage. Organizations need to monitor eBPF program performance to ensure they don’t negatively impact cluster operations.

Best practices for using eBPF in Kubernetes

Successful eBPF implementation in Kubernetes requires following established best practices to maximize benefits while minimizing risks and operational complexity.

Start with proven solutions

Begin your eBPF journey using established, well-tested solutions rather than developing custom programs from scratch. Leverage existing eBPF-based observability tools that have been validated in production environments and provide comprehensive documentation and support.

Implement comprehensive testing

Test eBPF programs across different kernel versions and node configurations before production deployment. Create automated testing pipelines that verify eBPF program functionality across your entire cluster infrastructure to prevent compatibility issues.

Monitor eBPF program performance

Establish monitoring for eBPF programs themselves to track resource consumption, loading success rates and operational health. This monitoring helps identify potential issues before they impact cluster performance or stability.

Maintain kernel compatibility awareness

Keep track of kernel versions and configurations across your cluster nodes. Document any specific requirements or limitations for your eBPF programs and establish procedures for handling kernel updates or node replacements.

Implement gradual rollouts

Deploy eBPF programs using gradual rollout strategies such as canary deployments or blue-green deployments. This approach allows you to validate functionality and performance in limited environments before full cluster deployment.

Establish security controls

Implement appropriate security controls and access restrictions for eBPF program deployment and management. Ensure that only authorized personnel can load or modify eBPF programs within your cluster environment.

SUSE and eBPF for Kubernetes

SUSE Cloud Observability provides a comprehensive platform for Kubernetes workloads, combining open standards like OpenTelemetry, out of the box dashboards, many SRE practices and guided remediation. All at an affordable price for any scale. The platform leverages eBPF technology to deliver unprecedented visibility into Kubernetes environments without requiring manual instrumentation.

Full OpenTelemetry and eBPF-based monitoring with automatic instrumentation for all major protocols, including topology and throughput detection. This ensures deep, low-overhead insights into Kubernetes workloads, delivering unparalleled observability across any platform. SUSE Cloud Observability combines the power of eBPF with OpenTelemetry standards to provide comprehensive monitoring capabilities.

The platform offers cost-effective pricing with a pay-as-you-go model, starting at $9.99 per host monthly for 10 to 100 hosts. For organizations with larger deployments, pricing scales to $8.99 per host monthly for configurations over 100 hosts.

Ready to experience the power of eBPF for Kubernetes observability? Try SUSE Cloud Observability free for 30 days on the AWS Marketplace and discover how eBPF technology can transform your Kubernetes monitoring capabilities.

eBPF for Kubernetes: Final thoughts

eBPF technology represents a fundamental shift in how organizations approach Kubernetes observability and Kubernetes security. By operating at the kernel level, eBPF provides granular insights that traditional monitoring approaches can’t match. The combination of reduced performance overhead, comprehensive protocol support and real-time data processing makes eBPF an essential technology for modern Kubernetes environments.

Organizations implementing eBPF in their Kubernetes clusters gain significant advantages in performance monitoring, security threat detection and operational efficiency. However, success requires careful attention to implementation best practices, kernel compatibility requirements and ongoing monitoring of eBPF program performance.

For teams managing complex, multi-cluster environments, eBPF technology offers the scalability and flexibility needed to maintain operational excellence while supporting rapid application development and deployment cycles.

SUSE Cloud Observability demonstrates the potential of eBPF technology when properly implemented and integrated with comprehensive observability platforms. Transform your Kubernetes monitoring strategy today with enterprise-grade eBPF capabilities. Get started with your free trial and unlock the power of kernel-level insights for your infrastructure. The combination of eBPF’s kernel-level insights with OpenTelemetry standards provides organizations with the visibility they need to maintain high-performing, secure Kubernetes environments.

eBPF for Kubernetes FAQs

What is the difference between BPF and eBPF?

BPF (Berkeley Packet Filter) was originally designed for network packet filtering in Unix systems. eBPF (Extended Berkeley Packet Filter) evolved from BPF but expanded far beyond network filtering to include system call monitoring, performance analysis, security enforcement and comprehensive observability capabilities. While BPF focuses primarily on network traffic, eBPF operates as a general-purpose kernel programming platform that supports diverse use cases including Kubernetes monitoring and security.

Is using eBPF in Kubernetes a security risk?

eBPF programs undergo rigorous verification by the kernel before execution, ensuring they can’t compromise system stability or security. When an eBPF program is loaded into the kernel using the bpf() syscall, the verifier ensures that it is safe to run within kernel space. Among other things, the verifier checks that the program terminates within a bounded number of CPU cycles and doesn’t access arbitrary memory addresses. This verification process makes eBPF programs inherently safe when properly implemented and deployed through trusted sources.

Does eBPF have other uses besides observability?

Yes, eBPF supports numerous use cases beyond observability, including network policy enforcement, security monitoring, performance optimization and system call filtering. In Kubernetes environments, eBPF can implement dynamic load balancing, enforce Kubernetes security policies, optimize network routing and provide real-time threat detection capabilities. The technology’s versatility makes it valuable for multiple operational domains within modern container platforms.

How can eBPF help with Kubernetes resource management?

eBPF provides granular insights into resource utilization patterns across Kubernetes clusters, enabling teams to optimize CPU, memory and network resource allocation. By monitoring resource consumption at the kernel level, eBPF helps identify underutilized or overprovisioned resources, detect resource bottlenecks and support automated scaling decisions. This visibility enables more efficient resource management and can significantly reduce infrastructure costs through better capacity planning and workload optimization.

Teams can leverage eBPF data to implement intelligent resource allocation strategies that automatically adjust based on real-time usage patterns. The technology provides detailed metrics about container resource consumption, enabling precise capacity planning and cost optimization across large-scale deployments. Advanced Kubernetes monitoring implementations using eBPF can trigger automated scaling decisions based on kernel-level resource utilization data, ensuring optimal performance while minimizing infrastructure costs.