MDPF: An NDN Probabilistic Forwarding Strategy Based on Maximizing Deviation Method

Forwarding strategy is the key feature of Named Data Networking (NDN) to realize dynamic, adaptive and intelligent forwarding, but work in this area is still at a very preliminary stage. In this paper, selecting which forwarding interface among multiple alternatives in NDN is defined as a multiple attribute decision making (MADM) problem and a maximizing deviation based probabilistic forwarding (MDPF) strategy is proposed to select forwarding interface on probability. Since multiple network metrics such as interface status, pending Interest numbers are considered together, each alternative interface´s availability is obtained more accurately. Thus, better content delivery efficiency can be achieved. In addition, MDPF provides good extensibility, as any appropriate metric can be added to enhance or customize it. We implement the proposal in ndnSIM and compare it with BestRoute and PI-based strategies under various topologies and scenarios. Experimental results show that MDPF strategy is more responsive and sensitive to network changes, and can realize higher throughput, lower drop rate as well as better load balance.