Exploring eBPF Use Cases in Public Cloud: Unlocking Performance, Security, and Observability.

Introduction

As cloud environments become more complex, dynamic, and distributed, traditional networking, security, and observability tools often fail to deliver the fine-grained control and performance that modern cloud-native applications demand. Enter eBPF (Extended Berkeley Packet Filter), a revolutionary technology that extends the Linux kernel with custom programs that can run safely and efficiently. With eBPF, cloud providers and users gain potent capabilities for improving network performance, enhancing security, and gaining deep observability into their workloads without modifying the underlying kernel.

This article will explore how eBPF is used in public cloud environments, showcasing real-world use cases and examples highlighting its potential for transforming cloud-native applications.

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What is eBPF?

eBPF is a technology that allows developers to run custom, sandboxed programs directly in the Linux kernel. These programs can be dynamically attached to various events in the kernel, such as system calls, network packets, or tracepoints, and can inspect or modify data in real time. Unlike traditional kernel modules, eBPF programs are verified for safety before execution, ensuring they cannot crash the system or introduce security risks.

Initially designed for network packet filtering, eBPF has become a versatile tool for various use cases, including networking, performance profiling, observability, and security.

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Few Use Cases of eBPF in Public Cloud

1. Networking Optimization

2. Security and Compliance

3. Observability and Monitoring

4. Load Balancing and Traffic Management

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1. Networking Optimization with eBPF

In public cloud environments, networking is one of the most critical components, as services must communicate efficiently across virtual machines, containers, and regions. Traditional networking approaches, such as tables for packet filtering or kube-proxy for service discovery in Kubernetes, often introduce significant overhead and latency. However, eBPF efficiently addresses these issues, providing reassurance about its performance benefits.

Example: eBPF-Powered Cilium for Kubernetes Networking

One of the leading projects that leverages eBPF for networking in public clouds is Cilium. Cilium replaces traditional networking tools in Kubernetes with eBPF-powered programs that run directly in the kernel, enabling faster packet processing and lower latency.

• Scenario: In a cloud-based Kubernetes environment, services communicate using an overlay network. Traditional proxies, such as kube-proxy, introduce latency as they process each packet at the user level. This overhead becomes significant as the cluster grows, affecting the overall performance.

• Solution with eBPF: Cilium uses eBPF to replace the kube-proxy and handle service discovery and load balancing directly within the kernel. This drastically reduces latency by processing network packets closer to the network interface without involving the user space. It also allows for more sophisticated load balancing and network security policies enforced in real-time.

This approach is widely used in public cloud Kubernetes environments such as Google Kubernetes Engine (GKE), Amazon Elastic Kubernetes Service (EKS), and Azure Kubernetes Service (AKS), where organizations need scalable, high-performance networking.

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2. Security and Compliance

In multi-tenant public cloud environments, security is paramount. Isolating workloads, detecting anomalies, and enforcing security policies in real-time are significant challenges, especially when dealing with distributed microservices architectures.

Example: Falco for Runtime Security Monitoring

Falco, an open-source runtime security tool, uses eBPF to monitor system calls and detect suspicious behaviour in real time. Falco is instrumental in cloud-native environments where containers and microservices interact dynamically.

• Scenario: In a public cloud environment, an attacker can gain unauthorized access to a web application container. Detecting such intrusions can be difficult without visibility into system-level behaviour until too late.

• Solution with eBPF: Falco attaches eBPF programs to system calls within the containerized environment. It monitors unusual activity, such as unexpected file changes, privilege escalations, or suspicious network connections. For instance, if the attacker tries to modify critical files or execute shell commands in a way that violates predefined security rules, Falco will immediately generate an alert and allow security teams to take action.

This real-time detection capability is crucial in public cloud settings, where workloads are often short-lived and distributed across multiple nodes. It provides a proactive monitoring solution, ensuring the audience feels secure about their cloud environments.

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3. Observability and Monitoring

One of the most powerful use cases of eBPF in public cloud environments is observability. Cloud-native applications are complex, distributed, and often opaque to traditional monitoring tools. eBPF provides deep, real-time visibility into both system- and application-level events without adding significant overhead, giving the audience confidence in its comprehensive insights.

Example: Pixie for Kubernetes Observability

Pixie, an observability platform for Kubernetes, leverages eBPF to automatically collect detailed telemetry data without requiring manual instrumentation. It captures real-time information such as network traffic, CPU usage, memory consumption, and application traces.

• Scenario: A development team deploys a new microservice to their Kubernetes cluster in a public cloud (e.g., AWS or GCP). Soon after, they notice performance issues and failures but need more visibility into where the problem lies.

• Solution with eBPF: Pixie automatically attaches eBPF programs to collect granular data from the microservice without modifying its source code. Developers can use Pixie to trace network requests, observe latency spikes, and monitor the performance of system resources such as CPU and memory. For example, Pixie might reveal that the microservice overloads the CPU due to inefficient network handling, leading to faster troubleshooting and resolution.

Pixie's use of eBPF makes it ideal for public cloud environments where services are temporary and manual instrumentation is impractical.

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4. Load Balancing and Traffic Management

Load balancing is critical for maintaining performance and availability in cloud applications. Traditional load balancers operate at the user level, introducing overhead and latency that can degrade performance at scale. eBPF can implement high-performance, kernel-level load balancers that optimize traffic handling directly at the network interface.

Example: Katran, Facebook's eBPF-based Load Balancer

Katran, an open-source load balancer developed by Facebook (Meta), uses eBPF to distribute traffic across backend servers. Unlike traditional load balancers, Katran operates at the kernel level, allowing it to handle traffic with minimal overhead.

• Scenario: A large-scale application running in the public cloud must handle thousands of incoming connections per second. Traditional load balancers need help to distribute traffic efficiently as the system scales, leading to increased latency and lower throughput.

• Solution with eBPF: Katran uses eBPF to handle packet processing directly in the kernel. It balances traffic across backend servers without needing a dedicated user-space proxy. The eBPF program inspects incoming packets and directs them to the appropriate backend server, bypassing the overhead of traditional user-space load balancers. This results in higher throughput and lower latency, making Katran a perfect fit for high-performance cloud applications.

Facebook?and other large-scale cloud users use Katran to optimize their load-balancing infrastructure.

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Conclusion

The rise of eBPF in public cloud environments transforms how networking, security, observability, and traffic management are handled. By allowing developers to run custom programs directly in the kernel, eBPF opens up new possibilities for optimizing performance, enhancing security, and gaining deep visibility into distributed systems.

From Kubernetes networking with Cilium?to real-time security monitoring with Falcoto efficient load balancing with Katran, eBPF enables cloud users to operate at a new level of efficiency and control. As public clouds continue to evolve, the role of eBPF will only grow, providing even more powerful tools to manage and optimize cloud-native workloads.

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References:

- Cilium GitHub: [https://github.com/cilium/cilium](https://github.com/cilium/cilium)

- Falco GitHub: [https://github.com/falcosecurity/falco](https://github.com/falcosecurity/falco)

- Pixie GitHub: [https://github.com/pixie-io/pixie](https://github.com/pixie-io/pixie)

- Katran GitHub: [https://github.com/facebookincubator/katran](https://github.com/facebookincubator/katran)

This article explores a few ways eBPF is leveraged in the public cloud. With its ability to extend the Linux kernel safely and efficiently, eBPF is poised to become a cornerstone of modern cloud infrastructure.