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공공누리This item is licensed Korea Open Government License

NDN Construction for Big Science: Lessons Learned from Establishing a Testbed
임헌국Alexander NiChristos PapadopoulosSusmit Shannigrahi고영배김다빈
Publication Year
NDN is one instance of ICN, which is a cleanslate
approach that promises to reduce inefficiencies
in the current Internet. NDN provides
intelligent data retrieval using the principles of
name-based symmetrical forwarding of Interest/
Data packets and in-network caching. The continually
increasing demand for the rapid dissemination
of large-scale scientific data is driving
the use of NDN in big science experiments. In
this article, we establish the first intercontinental
NDN testbed to offer complete insight into
NDN construction for big science. In the testbed,
an NDN-based application that targets climate
science as an example big-science application is
designed and implemented with differentiated
features compared to previous works on NDNbased
application design for big science. We first
attempt to systematically address detailed analysis
of why or how NDN benefits fit in big science
and issues that must be resolved to improve each
advantage, mostly based on lessons learned from
establishing the NDN testbed for climate science.
We extensively justify the needs of using NDN for
large-scale scientific data in the intercontinental
network, through experimental performance comparisons
between classical deliveries and NDNbased
climate data delivery, and detailed analysis
of why or how NDN benefits fit in big science.
Named Data Networking; big science; intercontinental NDN testbed; NDN-based application; climate science; large-scale scientific data
Journal Title
IEEE Network
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