# Context-aware Execution Migration Tool for Data Science Jupyter Notebooks on Hybrid Clouds

arXiv: https://arxiv.org/abs/2107.00187 · HTML: https://mlsanity.com/paper/2107.00187/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 1 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [Better Code, Better Sharing:On the Need of Analyzing Jupyter Notebooks](https://mlsanity.com/paper/1906.05234/) (2019)
- [Workflow Provenance in the Lifecycle of Scientific Machine Learning](https://mlsanity.com/paper/2010.00330/) (2020)
- [Checkpoint, Restore, and Live Migration for Science Platforms](https://mlsanity.com/paper/2101.05782/) (2021)
- [Deploying Jupyter Notebooks at scale on XSEDE resources for Science Gateways and workshops](https://mlsanity.com/paper/1805.04781/) (2018)

## Built on by (most cited first, 2 of 2)

- [Towards Lightweight Data Integration using Multi-workflow Provenance and Data Observability](https://mlsanity.com/paper/2308.09004/) (2023)
- [JupOtter: Cell-Level Bug Detection in Jupyter Notebooks](https://mlsanity.com/paper/2606.23877/) (2026)

## Abstract (arXiv)

Interactive computing notebooks, such as Jupyter notebooks, have become a popular tool for developing and improving data-driven models. Such notebooks tend to be executed either in the user's own machine or in a cloud environment, having drawbacks and benefits in both approaches. This paper presents a solution developed as a Jupyter extension that automatically selects which cells, as well as in which scenarios, such cells should be migrated to a more suitable platform for execution. We describe how we reduce the execution state of the notebook to decrease migration time and we explore the knowledge of user interactivity patterns with the notebook to determine which blocks of cells should be migrated. Using notebooks from Earth science (remote sensing), image recognition, and hand written digit identification (machine learning), our experiments show notebook state reductions of up to 55x and migration decisions leading to performance gains of up to 3.25x when the user interactivity with the notebook is taken into consideration.