# Eudoxia: a FaaS scheduling simulator for the composable lakehouse

arXiv: https://arxiv.org/abs/2505.13750 · HTML: https://mlsanity.com/paper/2505.13750/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 May 2025.

## Built on by (most cited first, 4 of 4)

- [Safe, Untrusted, "Proof-Carrying" AI Agents: toward the agentic lakehouse](https://mlsanity.com/paper/2510.09567/) (2025)
- [Trustworthy AI in the Agentic Lakehouse: from Concurrency to Governance](https://mlsanity.com/paper/2511.16402/) (2025)
- [Building a Correct-by-Design Lakehouse. Data Contracts, Versioning, and Transactional Pipelines for Humans and Agents](https://mlsanity.com/paper/2602.02335/) (2026)
- [AI for Distributed Systems Design: Scalable Cloud Optimization Through Repeated LLMs Sampling And Simulators](https://mlsanity.com/paper/2510.18897/) (2025)

## Abstract (arXiv)

Due to the variety of its target use cases and the large API surface area to cover, a data lakehouse (DLH) is a natural candidate for a composable data system. Bauplan is a composable DLH built on "spare data parts" and a unified Function-as-a-Service (FaaS) runtime for SQL queries and Python pipelines. While FaaS simplifies both building and using the system, it introduces novel challenges in scheduling and optimization of data workloads. In this work, starting from the programming model of the composable DLH, we characterize the underlying scheduling problem and motivate simulations as an effective tools to iterate on the DLH. We then introduce and release to the community Eudoxia, a deterministic simulator for scheduling data workloads as cloud functions. We show that Eudoxia can simulate a wide range of workloads and enables highly customizable user implementations of scheduling algorithms, providing a cheap mechanism for developers to evaluate different scheduling algorithms against their infrastructure.