Context-aware Execution Migration Tool for Data Science Jupyter Notebooks on Hybrid Clouds
This addresses performance issues for data scientists using Jupyter notebooks on hybrid clouds, though it is incremental as it builds on existing migration concepts.
The paper tackles the problem of inefficient execution in Jupyter notebooks by developing a tool that automatically migrates cells to optimal platforms, achieving up to 55x reduction in notebook state and up to 3.25x performance gains.
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.