In this paper, we consider the network slicing (NS) problem which attempts to map multiple customized virtual network requests to a common shared network infrastructure and allocate network resources to meet diverse service requirements. We propose an efficient decomposition algorithm for solving this NP-hard problem. The proposed algorithm decomposes the large-scale hard NS problem into two relatively easy function placement (FP) and traffic routing (TR) subproblems and iteratively solves them enabling information feedback between each other, which makes it particularly suitable to solve large-scale problems. Specifically, the FP subproblem is to place service functions into cloud nodes in the network, and solving it can return a function placement strategy based on which the TR subproblem is defined; and the TR subproblem is to find paths connecting two nodes hosting two adjacent functions in the network, and solving it can either verify that the solution of the FP subproblem is an optimal solution of the original problem, or return a valid inequality to the FP subproblem that cuts off the current infeasible solution. The proposed algorithm is guaranteed to find the global solution of the NS problem. We demonstrate the effectiveness and efficiency of the proposed algorithm via numerical experiments.

翻译：本文研究网络切片（NS）问题，该问题旨在将多个定制化虚拟网络请求映射至公共共享网络基础设施，并通过分配网络资源满足多样化的服务需求。我们提出了一种高效分解算法来解决这一NP难问题。该算法将大规模复杂NS问题分解为功能放置（FP）与流量路由（TR）两个相对简单的子问题，并通过迭代求解实现二者间的信息反馈，特别适用于处理大规模问题。具体而言，FP子问题负责将服务功能部署至网络中的云节点，其解算结果可返回功能放置策略，并据此定义TR子问题；TR子问题则需寻找网络中承载相邻功能的两个节点间的连接路径，其解算要么验证FP子问题的解为原问题的最优解，要么向FP子问题返回一个有效不等式以剔除当前不可行解。所提算法可保证求解NS问题的全局最优解。通过数值实验验证了该算法的有效性与高效性。