# Globus Automation Services: Research process automation across the space-time continuum

arXiv: https://arxiv.org/abs/2208.09513 · HTML: https://mlsanity.com/paper/2208.09513/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 105 references that are in the map.
- First posted on arXiv 19 Aug 2022.

## Builds on (most cited first, 15 of 15)

- [Parsl: Pervasive Parallel Programming in Python](https://mlsanity.com/paper/1905.02158/) (2019)
- [funcX: A Federated Function Serving Fabric for Science](https://mlsanity.com/paper/2005.04215/) (2020)
- [TomoGAN: Low-Dose Synchrotron X-Ray Tomography with Generative Adversarial Networks](https://mlsanity.com/paper/1902.07582/) (2019)
- [Enabling real-time multi-messenger astrophysics discoveries with deep learning](https://mlsanity.com/paper/1911.11779/) (2019)
- [DLHub: Model and Data Serving for Science](https://mlsanity.com/paper/1811.11213/) (2018)
- [Bridging Data Center AI Systems with Edge Computing for Actionable Information Retrieval](https://mlsanity.com/paper/2105.13967/) (2021)
- [How Developers Iterate on Machine Learning Workflows -- A Survey of the Applied Machine Learning Literature](https://mlsanity.com/paper/1803.10311/) (2018)
- [A Community Roadmap for Scientific Workflows Research and Development](https://mlsanity.com/paper/2110.02168/) (2021)
- [DataFed: Towards Reproducible Research via Federated Data Management](https://mlsanity.com/paper/2004.03710/) (2020)
- [High-Performance Ptychographic Reconstruction with Federated Facilities](https://mlsanity.com/paper/2111.11330/) (2021)
- [fairDMS: Rapid Model Training by Data and Model Reuse](https://mlsanity.com/paper/2204.09805/) (2022)
- [Towards Accommodating Real-time Jobs on HPC Platforms](https://mlsanity.com/paper/2103.13130/) (2021)
- [Real-Time XFEL Data Analysis at SLAC and NERSC: a Trial Run of Nascent Exascale Experimental Data Analysis](https://mlsanity.com/paper/2106.11469/) (2021)
- [Ultrafast Focus Detection for Automated Microscopy](https://mlsanity.com/paper/2108.12050/) (2021)
- [The LBNL Superfacility Project Report](https://mlsanity.com/paper/2206.11992/) (2022)

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

- [AI-assisted Automated Workflow for Real-time X-ray Ptychography Data Analysis via Federated Resources](https://mlsanity.com/paper/2304.04297/) (2023)
- [Linking the Dynamic PicoProbe Analytical Electron-Optical Beam Line / Microscope to Supercomputers](https://mlsanity.com/paper/2308.13701/) (2023)
- [Toward Trustworthy Autonomous Science: A Two-Year Community Roadmap](https://mlsanity.com/paper/2607.12113/) (2026)
- [Agency as a New Organizing Abstraction for Computer Science](https://mlsanity.com/paper/2610.17410/) (2026)

## Abstract (arXiv)

Research process automation -- the reliable, efficient, and reproducible execution of linked sets of actions on scientific instruments, computers, data stores, and other resources -- has emerged as an essential element of modern science. We report here on new services within the Globus research data management platform that enable the specification of diverse research processes as reusable sets of actions, flows, and the execution of such flows in heterogeneous research environments. To support flows with broad spatial extent (e.g., from scientific instrument to remote data center) and temporal extent (from seconds to weeks), these Globus automation services feature: 1) cloud hosting for reliable execution of even long-lived flows despite sporadic failures; 2) a simple specification and extensible asynchronous action provider API, for defining and executing a wide variety of actions and flows involving heterogeneous resources; 3) an event-driven execution model for automating execution of flows in response to arbitrary events; and 4) a rich security model enabling authorization delegation mechanisms for secure execution of long-running actions across distributed resources. These services permit researchers to outsource and automate the management of a broad range of research tasks to a reliable, scalable, and secure cloud platform. We present use cases for Globus automation services, describe their design and implementation, present microbenchmark studies, and review experiences applying the services in a range of applications.