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http://dx.doi.org/10.6109/jkiice.2020.24.7.904

Analysis on NDN Testbeds for Large-scale Scientific Data: Status, Applications, Features, and Issues  

Lim, Huhnkuk (Division of Computer and Information Engineering, Hoseo University)
Sin, Gwangcheon (Department of Information and security, Hoseo University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.7, 2020 , pp. 904-913 More about this Journal
Abstract
As the data volumes and complexity rapidly increase, data-intensive science handling large-scale scientific data needs to investigate new techniques for intelligent storage and data distribution over networks. Recently, Named Data Networking (NDN) and data-intensive science communities have inspired innovative changes in distribution and management for large-scale experimental data. In this article, analysis on NDN testbeds for large-scale scientific data such as climate science data and High Energy Physics (HEP) data is presented. This article is the first attempt to analyze existing NDN testbeds for large-scale scientific data. NDN testbeds for large-scale scientific data are described and discussed in terms of status, NDN-based application, and features, which are NDN testbed instance for climate science, NDN testbed instance for both climate science and HEP, and the NDN testbed in SANDIE project. Finally various issues to prevent pitfalls in NDN testbed establishment for large-scale scientific data are analyzed and discussed, which are drawn from the descriptions of NDN testbeds and features on them.
Keywords
Data-intensive Science; Name-Data Networking; NDN Testbed; Large-scale Scientific Data; NDN-based Application;
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