IoT-MEC网络中服务功能链主动重构方法OA

In the Mobile Edge Computing-enabled Internet of Things Networks(IoT-MEC),the high mobility of IoT terminals,the random arrival of service requests,and the real-time changes of network traffic result in resource allocation and service deployment no longer fully matching in the original application scenarios.How to effectively utilize the resources provided by the network to achieve real-time deployment and reconstruction of Service Function Chain(SFC)is a great challenge.Considering the mismatch between performance requirement of SFC and the allocated network resources caused by the high mobility of users and the real-time variation of network traffic in IoT-MEC,the user mobility and resource demand prediction based SFC reconfiguration strategy is investigated.First,an integer linear programming model is established with the objective of minimizing the end-to-end latency and reconfiguration cost of SFC.Second,an attention mechanism-based encoder-decoder mobile user trajectory prediction model and a Long Short-Term Memory(LSTM)network based Virtual Network Function(VNF)instance resource demand prediction model are designed to accurately predict user mobile trajectory and node load,respectively.Finally,according to the prediction results,a heuristic algorithm,Predict based SFC Active Reconfiguration(PSAR)is developed to implement the migration of VNFs and update routing in advance,ensuring proactive reconfiguration and seamless migration of SFCs before Quality of Service(QoS)degrades,and guaranteeing consistency and high-quality services of the network.Experimental results show that the proposed algorithm can efficiently reduce the end-to-end delay and reconfiguration cost of SFC.