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Named Data Networking in Climate Research and HEP Applications

Shannigrahi, Susmit and Papadopoulos, Christos and Yeh, Edmund and Newman, Harvey and Barczyk, Artur Jerzy and Liu, Ran and Sim, Alex and Mughal, Azher and Monga, Inder and Vlimant, Jean-Roch and Wu, John (2015) Named Data Networking in Climate Research and HEP Applications. Journal of Physics: Conference Series, 664 . Art. No. 052033. ISSN 1742-6596. https://resolver.caltech.edu/CaltechAUTHORS:20160427-083724095

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Abstract

The Computing Models of the LHC experiments continue to evolve from the simple hierarchical MONARC[2] model towards more agile models where data is exchanged among many Tier2 and Tier3 sites, relying on both large scale file transfers with strategic data placement, and an increased use of remote access to object collections with caching through CMS's AAA, ATLAS' FAX and ALICE's AliEn projects, for example. The challenges presented by expanding needs for CPU, storage and network capacity as well as rapid handling of large datasets of file and object collections have pointed the way towards future more agile pervasive models that make best use of highly distributed heterogeneous resources. In this paper, we explore the use of Named Data Networking (NDN), a new Internet architecture focusing on content rather than the location of the data collections. As NDN has shown considerable promise in another data intensive field, Climate Science, we discuss the similarities and differences between the Climate and HEP use cases, along with specific issues HEP faces and will face during LHC Run2 and beyond, which NDN could address.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1088/1742-6596/664/5/052033DOIArticle
http://iopscience.iop.org/article/10.1088/1742-6596/664/5/052033/metaPublisherArticle
http://chep2015.kek.jp/OrganizationConference Website
ORCID:
AuthorORCID
Newman, Harvey0000-0003-0964-1480
Additional Information:© 2015 Published under licence by IOP Publishing Ltd. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. This work is supported in part by grants from DOE Offices of HEP and Advanced Scientific Computing (DE-SC0007346), NSF Grants (OCI-1341024, CNS-1205562, NSF- 1246133, NSF- 13410999), and Cisco Research Grants (Microgrant-2014-128271) to Caltech and Northeastern University. We thank Julian Bunn, Dorian Kcira and Samir Cury for their feedback and help.
Funders:
Funding AgencyGrant Number
Department of Energy (DOE)DE-SC0007346
NSFOCI-1341024
NSFCNS-1205562
NSF1246133
NSF13410999
Cisco Research Grant2014-128271
Record Number:CaltechAUTHORS:20160427-083724095
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20160427-083724095
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:66499
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:27 Apr 2016 18:25
Last Modified:24 Feb 2020 10:30

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