November 2023

Book Chapter

Policy Considerations When Federating Facilities for Experimental and Observational Data Analysis

By:
Shankar, Mallikarjun ; Somnath, Suhas ; Alam, Sadaf; Feichtinger, Derek; Sala, Leonardo; Wells, Jack C
Page Number:
387-409
Volume:
2
Book Title:
Handbook on Big Data and Machine Learning in the Physical Sciences, Volume 2: Advanced Analysis Solutions for Leading Experimental Techniques
Publication Date:
November 9, 2023
Publisher Location:
World Scientific, Singapore, Singapore
View DOI Listing:
https://doi.org/10.1142/9789811204579_0018

Abstract

Today’s computational, experimental, and observational facilities afford us tremendous opportunities to couple theory and experiment at increasingly large scales. Empirical sensing capabilities are growing dramatically with beam line and detector improvements, and with advances in our ability to deploy large-scale data gathering observations of the natural world. The coupling of computational simulations and analysis to process the data from experimental and observational facilities is giving rise to cross-facility workflows. Such federations of facilities are in fact becoming an explicit requirement for large-scale scientific discovery. As we scale up these pipelines of scientific discovery, each participating facility needs to establish and align policies so that the federation can work seamlessly in an end-to-end manner. This chapter outlines specific policy considerations in enabling the federation of facilities for data analysis. Design choices and vital policy decisions cover the areas of data acquisition and storage, data transfer, computational resource allocation and co-scheduling, seamless federated user access, and cross-cutting governance. By highlighting the explicit and implicit interdependencies between facilities, we aim to provide facility designers and policymakers the information on policy issues to address early in a facility’s operations, thus enabling successful cross-facility federation and improved experimental and observational data analysis outcomes.


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