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Multipurpose Transaction Protocol: A New Data Transport Model

The real advantages, however, come when the bits hit the wire. The flow control and error recovery algorithms of MTP explicitly assume that the network may be congested and lossy, and that bandwidth and latency may vary wildly from one moment to the next.

MTP takes advantage of the typically half-duplex nature of most data transfers by positioning its core flow-control algorithms at the receiving, rather than the sending end. This gives it a much more immediate and realistic view of what’s going on in the network. Better input means faster adaptation to the network conditions of the moment, both at startup and throughout the transaction. As a result, MTP is able to ensure that the data pipe stays full, but not overflowing.

Figure 2. Illustration of TCP data flow across a congested WAN. Flow oscillation, scaling issues, and congestion result in wasted bandwidth.
For many MTP users, this translates into seven times faster data transfers over high-speed WANs, and ten times improved reliability on any network, when compared to even modern TCP implementations. This holds true even for data that is already compressed and completely unique, where compression and caching would break down. Most importantly, the results are scalable; the adaptive nature of the protocol means no setup tuning or special equipment is needed.

A case in point was Motorola Corporation’s adoption of ExpeDat, an MTP/IP based file transfer product also produced by Data Expedition Inc. (DEI). Motorola has offices and engineers all over the world who perform daily engineering simulations each involving gigabytes of inputs and outputs. Building and maintaining a high-performance computing cluster in every office would be prohibitively expensive, so engineers had to transfer jobs across the corporate WAN, a task that could take hours. Once the data was loaded, they often had to wait in a queue for their local cluster to become available because transferring data to idle but more distant clusters took even longer.



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