Efficient Traffic Engineering for 5G Core and Backhaul Networks

The next generation mobile networks (5G) should be efficient and elastic to accommodate numerous and diverse services. By explicitly assigning bandwidth to service flows, traffic engineering (TE) is effective to improve network efficiency and elasticity. Unfortunately, existing mobile network TE schemes are mostly focused on core network only, which is inadequate for efficient end-to-end traffic delivery in mobile networks. In this paper, we propose a TE framework that incorporates the data gateway (D-GW) selection and exploits the topology and traffic information of both core and backhaul networks for SDN-based 5G networks. With ideal flow to D-GW association (IFDA) strategy, we formulate the TE problem as a multicommodity flow problem to achieve network load balancing. Considering the cooperation signalling between D-GWs, we propose multiple BSs to one D-GW association (MBODA) and multiple flows to one D-GW association (MFODA) strategy, and formulate the corresponding TE problems as mixed integer linear programs (MILPs) which are NP-hard. To efficiently solve the IFDA-TE problem, we design an improved version of fully polynomial time approximation scheme (i-FPTAS). Moreover, we propose a heuristic method and an LP relaxation method that both use i-FPTAS to solve the MBODA-TE and MFODA-TE problems respectively. Numerical results show that i-FPTAS achieves close-optimal solution with significantly lower computational complexity, compared with FPTAS, and the performance of MFODA-TE is very close to that of the IFDA-TE, while there is a small performance degradation for MBODA-TE as the cost of computational efficiency.