# WORKSWORLD: A Domain for Integrated Numeric Planning and Scheduling of Distributed Pipelined Workflows

arXiv: https://arxiv.org/abs/2603.12214 · HTML: https://mlsanity.com/paper/2603.12214/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 12 Mar 2026.

## Builds on (most cited first, 7 of 7)

- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [Data Mesh: a Systematic Gray Literature Review](https://mlsanity.com/paper/2304.01062/) (2023)
- [Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows](https://mlsanity.com/paper/2410.14943/) (2024)
- [What About the Data? A Mapping Study on Data Engineering for AI Systems](https://mlsanity.com/paper/2402.05156/) (2024)
- [Towards a Dynamic Composability Approach for using Heterogeneous Systems in Remote Sensing](https://mlsanity.com/paper/2211.06918/) (2022)
- [Accelerating the Operation of Complex Workflows through Standard Data Interfaces](https://mlsanity.com/paper/2412.13339/) (2024)
- [Automated Planning for Optimal Data Pipeline Instantiation](https://mlsanity.com/paper/2503.12626/) (2025)

## Abstract (arXiv)

This work pursues automated planning and scheduling of distributed data pipelines, or workflows. We develop a general workflow and resource graph representation that includes both data processing and sharing components with corresponding network interfaces for scheduling. Leveraging these graphs, we introduce WORKSWORLD, a new domain for numeric domain-independent planners designed for permanently scheduled workflows, like ingest pipelines. Our framework permits users to define data sources, available workflow components, and desired data destinations and formats without explicitly declaring the entire workflow graph as a goal. The planner solves a joint planning and scheduling problem, producing a plan that both builds the workflow graph and schedules its components on the resource graph. We empirically show that a state-of-the-art numeric planner running on commodity hardware with one hour of CPU time and 30GB of memory can solve linear-chain workflows of up to 14 components across eight sites.