Coflow completion time minimization is one of the challenges of traffic management in data center networks.Inspired by the newest research progress in deep reinforcement learning

which is one direction of artificial intelligence

this paper proposes a novel coflow scheduling mechanism.It translates the coflow scheduling problem with bandwidth constraint into a continuous learning process.By learning the previous decisions

the best scheduling is obtained.By introducing back filling and limited multiplexing mechanisms

the system is work-conserving and starvation-free.Simulation results show that

under different network load

compared with other scheduling mechanisms

the average coflow completion time is reduced.Especially when the network load is heavy

the proposed mechanism achieves about 50% performance improvement than the state-of-the-art scheduling mechanism.