AWS Lambda faced challenges in logging network activity for its ephemeral microVMs, which run for milliseconds and belong to different customers. The previous system struggled to provide a complete and accurately attributed network ledger for these short-lived, high-volume workloads. This lack of detailed logging hindered security investigations, incident response, and accurate metering.
To overcome these limitations, AWS Lambda developed a new network flow capture pipeline utilizing eBPF and Rust. This purpose-built system is designed to operate at Lambda's scale, ensuring that every network flow across thousands of microVMs per host is recorded. The new architecture aims to provide a reliable system of record for network packets.
The primary requirements for the new system were completeness and correct attribution of network records. Each packet and flow must be associated with the specific microVM and tenant that generated it, with no missed packets. This precision is crucial for accurate billing, observability, and monitoring, especially given Lambda's processing of millions of requests per second. The capture process also needed to introduce minimal overhead to both network flows and the platform.
The implementation of this eBPF and Rust-based pipeline enhances AWS Lambda's ability to perform investigations, respond to incidents, and conduct audits. It also feeds into network usage and metering services, which demand high accuracy. The persistent storage of these records ensures compliance with audit requirements, providing a detailed history of network interactions for every tenant workload.
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AWS Lambda has replaced its legacy network flow capture system with a new pipeline built using eBPF and Rust to log network activity across thousands of microVMs per host. This change addresses the need for complete and accurately attributed network records for short-lived workloads, improving incident response, auditing, and metering for Lambda's serverless platform.