Zero-Retransmission Transport with Proactive FEC

Abstract

Upcoming fourth industries demand low latency in many ways. One of them is the remote control industry combined with the 5G infrastructure, which already has a lot in place, including remote surgery or remote heavy machine control to work day and night alternatively. Intercontinental remote control industries have a lot of safety concerns, so there is a particular need for stronger latency bound. Many latest low-latency works mostly aim to keep queuing low and ensure low latencies of 99.0% to 99.9% percent packets, but has weaknesses on 0.1% packets tail latencies due to retransmission which trigger RTT-sensitive delay and RTO. To prevent further delays due to this retransmission problem, many researchers have been working on FEC that can recover packet loss in half RTT without retransmission. Packet level FEC is basically a method of protecting traffic from loss, by encoding packets with adding redundancies. Many XOR, Block, or sliding window code based FEC were studies before, but they could not overcome the basic limitations of their code due such as low recovery ratio or high overhead by poor adaptiveness. In order to achieve higher FEC performance on state-varying channels, near real time level adaptability was essential, so we designed a method to learn future loss pattern on deep learning model to determine FEC parameters. As a result of increasing FEC adaptability through fine tracking channel status, we found that recovery ratio became higher even though the throughput overhead got smaller than other codes. Our 5G trace driven experiments showed that even in highly volatile 5g situations, zero-retransmission is possible in certain situations. It was also shown that using adaptive FEC was effective to bound the end-to-end delay in both static and dynamic situations.