Named Data Networking in Climate Research and HEP Applications
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.
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.Attached Files
Published - jpconf15_664_052033.pdf
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Additional details
- Eprint ID
- 66499
- Resolver ID
- CaltechAUTHORS:20160427-083724095
- Department of Energy (DOE)
- DE-SC0007346
- NSF
- OCI-1341024
- NSF
- CNS-1205562
- NSF
- 1246133
- NSF
- 13410999
- Cisco Research Grant
- 2014-128271
- Created
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2016-04-27Created from EPrint's datestamp field
- Updated
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2022-07-12Created from EPrint's last_modified field