Abstract: At present,the knowledge-defined network empowers the development of AI technology,and the computing power network provides the computing power resources required by AI.The two gradually tend to integrate to form the knowledge defined computing networking(KDCN).KDCN has empowered the development of many new network applications,such as the metaverse,AR/VR,east-west computing.These new applications have a great demand for computing power resources and network resources,and are called heavy hitter(HH).The existence of HH flow seriously aggravates the congestion of KDCN network.In response to this challenge,this paper proposes an intelligent traffic scheduling mechanism,which aims to solve the congestion problem in KDCN through deep Q neural networks.Compared with the offline training process,a real-time closed loop is established between traffic data detection and acquisition,model training,and congestion flow modulation decision-making to realize the online training of the deep Q neural network model.Based on this closed-loop control,the intelligent flow modulation model can realize continuous evolution through continuous learning,and is used to provide real-time decision-making.Experimental results show that the proposed algorithm is superior to the existing methods in resource utilization,throughput,average packet loss rate and so on.

Key words: Knowledge definition computing networking, Deep Q neural networks, Intelligent congestion flow modulation, Heavy hitter flow, Closed loop control