Efficient traffic engineering for 5g core and backhaul networks

• Published in: Journal of communications and networks 2017, 19(1), , pp.80-92
• Main Authors: Wang, Gang, Feng, Gang, Qin, Shuang, Wen, Ruihan
• Format: Article
• Language: English
• Subjects: 5G mobile communication; Computational complexity; Computing time; Data gateway (D-GW) selection; Elasticity; Heuristic methods; Linear programming; Logic gates; mobile cellularnetwork; Mobile computing; multicommodity flow problem; Performance degradation; Relaxation method (mathematics); Resource management; Routing; Signaling; Traffic engineering; traffic engineering (TE); Wireless networks; 전자/정보통신공학
• ISSN: 1229-2370; 1976-5541
• DOI: 10.1109/JCN.2017.000010

The next generation mobile networks (5G) should be efficient and elastic to accommodate numerous and diverse ser- vices. 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 effi- cient 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 informa- tion of both core and backhaul networks for SDN-based 5G net- works. 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 sig- nalling between D-GWs, we propose multiple BSs to one D-GW association (MBODA) and multiple flows to one D-GW associa- tion (MFODA) strategy, and formulate the corresponding TE prob- lems as mixed integer linear programs (MILPs) which are NP- hard. To efficiently solve the IFDA-TE problem, we design an im- proved version of fully polynomial time approximation scheme (i- FPTAS). Moreover, we propose a heuristic method and an LP re- laxation 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.