Signiant To Introduce New Patent-Pending Intelligent Transport Architecture

Signiant is to introduce its new patent-pending intelligent transport architecture.

The architecture replaces standard TCP with a proprietary UDP-based acceleration protocol or will automatically deploy multiple TCP connections in parallel, depending on network conditions. The technology determines the fastest way to get data through the network utilizing a patent-pending mechanism that leverages machine learning to make decisions on the fly. The transport is capable of multiple Gbps transfer speeds for both site-to-site transfers as well as transfers to and from the cloud.

The new architecture is already deployed in two of Signiant’s cloud-native SaaS products:

Flight which accelerates file transfers to and from the cloud
Jet for simple, fast, automated system-to-system file transfers
"As networks evolve and increase in bandwidth, many incorrectly assume that acceleration technology is no longer required," said Signiant CTO, Ian Hamilton. "Network bandwidth and network throughput are not the same and Signiant is able to provide even more value on higher bandwidth connections. Our new intelligent architecture supports all types of transfers including site-to-site, site-to-cloud and cloud-to-cloud transfers of both files and streams with no constraints on file size or type."

In order to take full advantage of available bandwidth, Signiant’s machine learning algorithm examines past history and optimally configures application and transport-level transfer parameters for both file-based and live media transfers. Not only does this ensure the best result without expensive and error prone manual tuning and tooling, but results improve over time as the system learns. In addition to Signiant’s patent-pending intelligent transport, Signiant’s core UDP-acceleration protocol offers distinct advantages. Signiant isolates different sources of congestion by looking at latency and packet loss, and also by constantly examining the rate of change in these observations. As such it can differentiate between edge and core network congestion and react accordingly.