tcp_conn_tuner: move to using reinforcement learing approach #61
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Previously, congestion control algoritm selection was done via a simple method of looking for packet loss on a per-connection basis and applying BBR if a limit was surpassed. This approach has been replaced by using a reinforcement learning based algorithm which seeks to choose the congestion control algorithm that approaches optimal performance for the link; minimum RTT and delivery rate tuned to fill the pipe. See bpftune-tcp-conn(8) for details. The behaviour under loss is similar to before - BBR is selected - but it is selected based upon its optimal behaviour rather than via a hand-coded algorithm. In normal network conditions, different algorithms can be selected; choices available currently are - cubic - BBR - htcp - dctcp Signed-off-by: Alan Maguire <[email protected]>
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Previously, congestion control algoritm selection was done via a simple method of looking for packet loss on a per-connection basis and applying BBR if a limit was surpassed.
This approach has been replaced by using a reinforcement learning based algorithm which seeks to choose the congestion control algorithm that approaches optimal performance for the link; minimum RTT and delivery rate tuned to fill the pipe. See bpftune-tcp-conn(8) for details.
The behaviour under loss is similar to before - BBR is selected - but it is selected based upon its optimal behaviour rather than via a hand-coded algorithm. In normal network conditions, different algorithms can be selected; choices available currently are