Managed Network Services for Exascale Data Movement Across Large Global Scientific Collaborations

TL;DR

This paper proposes a managed network service solution for large-scale scientific collaborations to optimize data movement at exascale, demonstrated with CERN's LHC, aiming to reduce storage over-provisioning and improve workflow efficiency.

Contribution

It introduces a novel integrated co-scheduling approach for networks within high-level workflows, addressing uncontrolled bandwidth competition in global scientific data infrastructures.

Findings

Co-scheduling reduces storage needs by over an order of magnitude.

Demonstrated functionality in the context of CERN's LHC.

Next steps outlined for production deployment.

Abstract

Unique scientific instruments designed and operated by large global collaborations are expected to produce Exabyte-scale data volumes per year by 2030. These collaborations depend on globally distributed storage and compute to turn raw data into science. While all of these infrastructures have batch scheduling capabilities to share compute, Research and Education networks lack those capabilities. There is thus uncontrolled competition for bandwidth between and within collaborations. As a result, data "hogs" disk space at processing facilities for much longer than it takes to process, leading to vastly over-provisioned storage infrastructures. Integrated co-scheduling of networks as part of high-level managed workflows might reduce these storage needs by more than an order of magnitude. This paper describes such a solution, demonstrates its functionality in the context of the Large Hadron Collider (LHC) at CERN, and presents the next-steps towards its use in production.