Abstract

The Large Hadron Collider (LHC) experiments distribute data by leveraging a diverse array of National Research and Education Networks (NRENs), where experiment data management systems treat networks as a “blackbox” resource. After the High Luminosity upgrade, the Compact Muon Solenoid (CMS) experiment alone will produce roughly 0.5 exabytes of data per year. NREN Networks are a critical part of the success of CMS and other LHC experiments. However, during data movement, NRENs are unaware of data priorities, importance, or need for quality of service, and this poses a challenge for operators to coordinate the movement of data and have predictable data flows across multi-domain networks. The overarching goal of SENSE (The Software-defined network for End-to-end Networked Science at Exascale) is to enable National Labs and universities to request and provision end-to-end intelligent network services for their application workflows leveraging SDN (Software-Defined Networking) capabilities. This work aims to allow LHC Experiments and Rucio, the data management software used by CMS Experiment, to allocate and prioritize certain data transfers over the wide area network. In this paper, we will present the current progress of the integration of SENSE, Multi-domain end-to-end SDN Orchestration with QoS (Quality of Service) capabilities, with Rucio, the data management software used by CMS Experiment.

Details

Title
Automated Network Services for Exascale Data Movement
Author
Balcas, Justas; Newman, Harvey; Bhat, Preeti P; Würthwein, Frank; Guiang, Jonathan; Arora, Aashay; Davila, Diego; Graham, John; Hutton, Thomas; Lehman, Tom; Yang, Xi; Chin Guok; Mason, David Alexander; Gutsche, Oliver; DeMar, Phil; Huang, Chih-Hao; Shah, Syed Asif; Litvintsev, Dmitry; Heath, Ryan; Andrew Malone Melo
Section
Data and Metadata Organization, Management and Access
Publication year
2024
Publication date
2024
Publisher
EDP Sciences
ISSN
21016275
e-ISSN
2100014X
Source type
Conference Paper
Language of publication
English
ProQuest document ID
3057081792
Copyright
© 2024. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and conditions, you may use this content in accordance with the terms of the License.