eBPF for high-performance networking and security in cloud-native environments

Originally designed as a packet filtering framework, the extended Berkeley Packet Filter (EBPF) has since become a flexible and powerful networking, security, and observability tool at high performance in the cloud native ecosystem. EBPF is explored as this transformative technology has been partly adopted by cloud-native platforms such as Kubernetes to take advantage of the ability of EBPF in network optimization, security enforcement, and real-time monitoring without affecting system latency and employing its inefficient, kernel-level packet processing, which bypasses the bottlenecks of user space networking tools for better network throughput, lower latency, and better load balancing. EBPF-based security frameworks like Falco, Tetragon, and Tracee provide great capabilities for real-time threat detection and enforcement of security policy in kernel environments, making them suitable for dynamic cloud environments. These EBPF solutions are compared to traditional mechanisms such as firewalls or intrusion detection systems in terms of their effectiveness. It further explores the practices for deploying EBPF in Kubernetes environments, including micro-segmentation and zero-trust security policies. Besides that, the research compares some EBPF-based security frameworks and discusses their potential usage in real-world scenarios. This study examines the technical and practical implications of EBPF towards performance and security improvement for cloud-native infrastructures geared at cloud architects, security engineers, and DevOps engineers for EBPF. The findings indicate how EBPF can help tackle the scalability and performance concerns in contemporary distributed systems and ensure that EBPF will play an integral part in the future of cloud-native networking and security.