A research team from the Computer Network Information Center (CNIC) of the Chinese Academy of Sciences announced this week that it has built a new Global Research Platform (GRP) for scientific big data transmission.
The team, in cooperation with Northwestern University of the U.S., adopted advanced network technologies, such as content centric networking (CCN), software-defined networking (SDN), network slicing, to establish a global platform for scientific research and innovation.
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 demand of scientific big data transmission. 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 China Since & Technology Network (a Chinese research network managed by CNIC) and the StarLight (an American research network managed by Northwestern University), the GRP platform proposes a cross-domain software-defined wide-area interconnection mechanism, which can greatly improve the long-distance transmission performance of scientific big data, according to LI Jun, a Chinese researcher of the study.
"The global platform will support international scientific cooperations such as high energy physics large hadron collider experiment, astronomical e-VLB (very long baseline interferometry) observation, International Thermonuclear Experimental Reactor (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 on 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.
