arXiv subjects

Marc Brooker

Publications and source records attributed to Marc Brooker.

3 recordsLinked to original sources

arXiv2026

Aurora DSQL: Scalable, Multi-Region OLTP

Aurora DSQL is a serverless SQL database designed for cloud-scale transaction processing with multi-region active-active capabilities. Built on a disaggregated architecture, DSQL separates compute, storage, and transaction coordination into independent, horizontally scalable services. Query processors run in Firecracker MicroVMs executing PostgreSQL-compatible SQL without local state. The system uses multiversion concurrency control with precision timestamps for coordination-free reads and optimistic concurrency control for writes, deferring coordination to commit time through distributed adjudicators and the Journal replication system. This minimizes cross-region latency by requiring coordination only during commits, not individual statements. DSQL enables elastic scaling from zero to millions of transactions per second while providing strong consistency, ACID transactions, and continuous availability during availability zone or region failures.

cs.DB↗

arXiv2023

On-demand Container Loading in AWS Lambda

AWS Lambda is a serverless event-driven compute service, part of a category of cloud compute offerings sometimes called Function-as-a-service (FaaS). When we first released AWS Lambda, functions were limited to 250MB of code and dependencies, packaged as a simple compressed archive. In 2020, we released support for deploying container images as large as 10GiB as Lambda functions, allowing customers to bring much larger code bases and sets of dependencies to Lambda. Supporting larger packages, while still meeting Lambda's goals of rapid scale (adding up to 15,000 new containers per second for a single customer, and much more in aggregate), high request rate (millions of requests per second), high scale (millions of unique workloads), and low start-up times (as low as 50ms) presented a significant challenge. We describe the storage and caching system we built, optimized for delivering container images on-demand, and our experiences designing, building, and operating it at scale. We focus on challenges around security, efficiency, latency, and cost, and how we addressed these challenges in a system that combines caching, deduplication, convergent encryption, erasure coding, and block-level demand loading. Since building this system, it has reliably processed hundreds of trillions of Lambda invocations for over a million AWS customers, and has shown excellent resilience to load and infrastructure failures.

cs.DC↗

arXiv2021

Restoring Uniqueness in MicroVM Snapshots

Code initialization -- the step of loading code, executing static code, filling caches, and forming re-used connections -- tends to dominate cold-start time in serverless compute systems such as AWS Lambda. Post-initialization memory snapshots, cloned and restored on start, have emerged as a viable solution to this problem, with incremental snapshot and fast restore support in VMMs like Firecracker. Saving memory introduces the challenge of managing high-value memory contents, such as cryptographic secrets. Cloning introduces the challenge of restoring the uniqueness of the VMs, to allow them to do unique things like generate UUIDs, secrets, and nonces. This paper examines solutions to these problems in the every microsecond counts context of serverless cold-start, and discusses the state-of-the-art of available solutions. We present two new interfaces aimed at solving this problem -- MADV\_WIPEONSUSPEND and SysGenId -- and compare them to alternative solutions.

cs.CR↗