知识定义算力网络下的重击流智能流量调度机制

Knowledge-defined Intelligent Traffic Scheduling Mechanism in Computing Network

当前,知识定义网络赋能AI技术发展,算力网络提供AI所需算力资源,二者逐渐趋于融合,形成了知识定义算力网络(Knowledge Defined Computing Networking,KDCN)。KDCN赋能发展了诸多新型网络应用,如元宇宙、AR/VR、东数西算等,这些新型应用对算力资源和网络资源有极大的需求,被称为重击流(Heavy Hitter,HH)。HH流的存在严重加剧了KDCN网络的拥塞情况。针对这一挑战,提出了一种智能流量调度机制,旨在通过深度Q神经网络来解决KDCN中的拥塞问题。相较于离线训练过程,通过流量数据检测与采集、在模型训练和拥塞流调决策之间建立实时闭环,来实现深度Q神经网络模型的在线训练。基于该闭环控制,智能流调模型通过不断学习可以实现持续演化,并用于提供实时决策。实验结果表明,该算法在资源利用率、吞吐量、平均丢包率等方面优于现有方法。

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.