Delivering SKA Science
Peter Quinn, Tim Axelrod, Ian Bird, Richard Dodson, Alex Szalay,, Andreas Wicenec

TL;DR
The paper discusses the massive data challenges of SKA science, emphasizing the need for distributed, cloud-based solutions to manage costs and ensure effective global scientific collaboration.
Contribution
It analyzes SKA data flow and costs, proposing a shift towards distributed, cloud-enabled data systems inspired by other large-scale scientific projects.
Findings
Survey projects may exceed SKA1 budget without new data management strategies.
Shared resources and costs are essential for sustainable SKA science.
Cloud technologies offer promising solutions for scalable SKA data processing.
Abstract
The SKA will be capable of producing a stream of science data products that are Exa-scale in terms of their storage and processing requirements. This Google-scale enterprise is attracting considerable international interest and excitement from within the industrial and academic communities. In this chapter we examine the data flow, storage and processing requirements of a number of key SKA survey science projects to be executed on the baseline SKA1 configuration. Based on a set of conservative assumptions about trends for HPC and storage costs, and the data flow process within the SKA Observatory, it is apparent that survey projects of the scale proposed will potentially drive construction and operations costs beyond the current anticipated SKA1 budget. This implies a sharing of the resources and costs to deliver SKA science between the community and what is contained within the SKA…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
