As high-performance computing becomes increasingly data-intensive and the demand for shorter turnaround times grows, data transfer speed becomes an ever more important bottleneck. Now, in an article published in IEEE Transactions on Network and Service Management, researchers from Samsara University and the University of Missouri have announced the development of an algorithm that increases data transmission speeds in powerful data processing centers by up to one and half times.

The algorithm relies on a special routing method. First, the user enters four basic parameters – bandwidth, data transfer speed in Kbps, cloud storage and price. The algorithm then uses a shortest path finder algorithm to deliver the data transmission at the necessary quality and speed. This “Neighborhoods Method,” as they call it, is an approach that the scientists have successfully developed and published before in the context of network virtualization.

The scientists claim that the data transfer method is particularly relevant for high-precision calculations in fundamental science and applied research. One of the scientists behind the research, Andrey Sukhov, outlines how the algorithm could provide value to major projects like the International Thermonuclear Experimental Reactor in France – or even CERN’s Large Hadron Collider.

“They calculate tasks in the laboratories scattered all over the world, make inquiries to the computer centers,” said Sukhov. “They also need to exchange both textual information and high-resolution streaming video online. The technology that we offer will help them with this.”

The researchers hope to use this algorithm in the near future to create an experimental application for studying combustion reactions. If successful, the implementation would allow scientists worldwide to access the project remotely. More broadly, it would also allow remote access to the powerful Sergey Korolev supercomputer.

About the paper

The paper discussed in this article is titled “A Constrained Path Scheme for Virtual Network Service Management.” It was published in the August 2018 by IEEE Transactions on Network and Service Management and written by Dmitrii Chemodanov, Flavio Esposito, Prasad Calyam, and Andrei Sukhov.