A NEURAL NETWORK BASED TRAFFIC-AWARE FORWARDING STRATEGY IN NAMED DATA NETWORKING

Named Data Networking (NDN) is a new Internet architecture which has been proposed to eliminate TCP/IP Internet architecture restrictions. This architecture is abstracting away the notions of both hosting and operation based on naming datagrams. However, one of the major challenges of NDN is supporting a QoS-aware forwarding strategy so as to forward Interest packets intelligently over multiple paths based on the current network condition. In this paper, Neural Network (NN) based Traffic-aware Forwarding strategy (NNTF) is introduced in order to determine an optimal path for Interest forwarding. NN is embedded in NDN routers to dynamically select the next hop based on the path overload probability achieved from the NN. This solution is characterized by load balancing and QoS-awareness in terms of delay and packet drop via monitoring the available path and forwarding data on the traffic-aware shortest path. The performance of NNTF is evaluated using ndnSIM which is a NS-3 module that implements the NDN communication model. The simulation results show the efficiency of this scheme in terms of network QoS improvement of 17.5% and 72% reduction in network delay and packet drop respectively.