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| Dynamic deployment algorithm of 5G core network user plane using Benders decomposition |
Jun-jie CHEN1,2( ),Hong-jun LI1,Xiao-jun ZHU1 |
1. School of Information Science and Technology, Nantong University, Nantong 226019, China
2. Nantong Research Institute for Advanced Communication Technologies, Nantong 226019, China |
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Abstract For the dynamic deployment problem of 5G core network user plane in the edge network, a multi-stage optimization algorithm for user plane function (UPF) deployment and traffic scheduling based on Benders decomposition was proposed to cope with the time-varying traffic in 5G networks and support 5G low-latency services. First, considering the delayed effect of the deployment decision, a multi-stage optimization model for UPF deployment and traffic scheduling was proposed for minimizing the energy consumption of edge servers, the UPF deployment cost and the user plane latency. Then, using Benders decomposition, the model was decomposed into a UPF deployment master problem and a set of traffic scheduling subproblems. Last, the master problem and the subproblems were solved alternatively and iteratively to obtain the optimal UPF deployment and traffic scheduling. Simulation results show that the proposed algorithm has a fast convergence speed while ensuring the accuracy of the solution; compared with the stage-by-stage solution method and the heuristic algorithm based on Markov decision process (MDP), the algorithm saves 10.4% and 5.1% of the operational cost, respectively.
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Received: 21 March 2022
Published: 31 March 2023
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| Fund: 国家自然科学基金资助项目（61971245）；南通市科技计划项目（JC2018025） |
采用Benders分解的5G核心网用户面动态部署算法
为了应对5G网络时变的数据流量负载，同时满足5G低时延业务需求，提出基于Benders分解的用户面功能(UPF)部署与流量调度多阶段规划算法，以实现边缘网络环境下5G核心网用户面的动态部署. 以最小化边缘服务器能耗、UPF部署成本及用户面数据时延为目标，考虑部署决策的延迟影响，建立UPF部署和流量调度多阶段规划模型. 用Benders分解算法，将模型分解为UPF部署主问题和一系列流量调度子问题，交替迭代求解主问题和子问题，以获得最优的UPF部署和流量调度. 仿真结果表明，所提算法在保证求解精度的同时具有较快的收敛速度；与逐阶段求解方法和基于马尔可夫决策过程(MDP)的启发式算法相比，所提算法分别节省了10.4%和5.1%的总运营成本.
关键词：
核心网,
用户面功能(UPF)部署,
能耗,
时延,
Benders分解
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