Bringing Computation to the data: Interoperable serverless function execution for astrophysical data analysis in the SRCNet
Manuel Parra-Roy\'on, Juli\'an Garrido-S\'anchez, Susana S\'anchez-Exp\'osito, Mar\'ia \'Angeles Mendoza, Rob Barnsley, Anthony Moraghan, Jes\'us S\'anchez, Laura Darriba, Carlos Ru\'iz-Monje, Edgar Joao, Javier Mold\'on, Jes\'us Salgado, Lourdes Verdes-Montenegro

TL;DR
This paper explores the application of serverless FaaS computing to astrophysical data analysis within the SRCNet, demonstrating scalable, efficient, and data-proximate computation for large-scale radio astronomy data.
Contribution
It develops and deploys representative serverless functions, including a Gaussian convolution, integrated into SRCNet to enable computation close to data sources in radio astronomy.
Findings
FaaS functions can be embedded into SRCNet services.
Reduces latency and data transfer in astrophysical workflows.
Scalable solution for SKA-era data volumes.
Abstract
Serverless computing is a paradigm in which the underlying infrastructure is fully managed by the provider, enabling applications and services to be executed with elastic resource provisioning and minimal operational overhead. A core model within this paradigm is Function-as-a-Service (FaaS), where lightweight functions are deployed and triggered on demand, scaling seamlessly with workload. FaaS offers flexibility, cost-effectiveness, and fine-grained scalability, qualities particularly relevant for large-scale scientific infrastructures where data volumes are too large to centralise and computation must increasingly occur close to the data. The Square Kilometre Array Observatory (SKAO) exemplifies this challenge. Once operational, it will generate about 700~PB of data products annually, distributed across the SKA Regional Centre Network (SRCNet), a federation of international centres…
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Taxonomy
TopicsRadio Astronomy Observations and Technology · Distributed systems and fault tolerance · Advanced Data Storage Technologies
