Global Research Platform for the Scientific Big Data Transmission Built
Date: Sep 15, 2022
Research team of the Computer Network Information Center (CNIC) of the Chinese Academy of Sciences (CAS) announced this week to have built a new Global Research Platform (GRP) for scientific big data transmission.
GRP, in cooperation with Northwestern University of the U.S., adopted advanced network technologies, such as CCN, SDN, network slicing, to establish a worldwide global scientific research and innovation platform.
International scientific research cooperation in many disciplines, such as high-energy physics, astronomy and meteorology, requires massive scientific data transmission through international long-distance networks.
However, it is hard for the traditional TCP/IP network technology to meet the needs of scientific big data transmission, and new transmission technologies and experimental methods are urgently needed to meet the challenge of scientific big data.
Based on the 10Gbps high speed networking infrastructure between the CSTNET (the Chinese research network managed by CNIC) and the StarLight (the American research network managed by Northwestern University), the GRP platform proposed a cross-domain software-defined wide-area interconnection mechanism, and "can greatly improve the long-distance transmission performance of scientific big data", according to LI Jun, Chinese PI of the study.
"The global platform will support international scientific cooperations such as high energy physics LHC experiment, astronomical e-VLB observation, ITER and other scientific projects," said LI.
Relevant study entitled "Multi-Path Forwarding Strategy for Named Data Networking Based on Pending Interests and Available Bandwidth" was published by IEEE International Symposium on Parallel and Distributed Processing with Applications.
CNIC and the Northwestern University will further deepen cooperation in the future, and carry out more applications and demonstrations on big scientific data transmissions in more aspects and domains, according to LI.
(Haibo Wu, Yongmao Ren, Jun Li)