  Various congestion control protocols have been designed to achieve high
performance in different network environments. Modern online learning solutions
that delegate the congestion control actions to a machine cannot properly
converge in the stringent time scales of data centers. We leverage multiagent
reinforcement learning to design a system for dynamic tuning of congestion
control parameters at end-hosts in a data center. The system includes agents at
the end-hosts to monitor and report the network and traffic states, and agents
to run the reinforcement learning algorithm given the states. Based on the
state of the environment, the system generates congestion control parameters
that optimize network performance metrics such as throughput and latency. As a
case study, we examine BBR, an example of a prominent recently-developed
congestion control protocol. Our experiments demonstrate that the proposed
system has the potential to mitigate the problems of static parameters.
