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An Efficient Linear Programming Rounding-and-Refinement Algorithm for Large-Scale Network Slicing Problem

机译:一种高效的大型网络切片问题的线性编程圆形和细化算法

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In this paper, we consider the network slicing problem which attempts to map multiple customized virtual network requests (also called services) to a common shared network infrastructure and allocate network resources to meet diverse service requirements, and propose an efficient two-stage algorithm for solving this NP-hard problem. In the first stage, the proposed algorithm uses an iterative linear programming (LP) rounding procedure to place the virtual network functions of all services into cloud nodes while taking traffic routing of all services into consideration; in the second stage, the proposed algorithm uses an iterative LP refinement procedure to obtain a solution for traffic routing of all services with their end-to-end delay constraints being satisfied. Compared with the existing algorithms which either have an exponential complexity or return a low-quality solution, our proposed algorithm achieves a better trade-off between solution quality and computational complexity. In particular, the worst-case complexity of our proposed algorithm is polynomial, which makes it suitable for solving large-scale problems. Numerical results demonstrate the effectiveness and efficiency of our proposed algorithm.
机译:在本文中,我们考虑了网络切片问题,该问题试图将多个定制的虚拟网络请求(也称为服务)映射到公共共享网络基础设施并分配网络资源以满足不同的服务要求,并提出了一种用于解决的有效的两阶段算法这个np难的问题。在第一阶段,所提出的算法使用迭代线性编程(LP)舍入过程,将所有服务的虚拟网络功能放入云节点,同时考虑所有服务的流量路由;在第二阶段,所提出的算法使用迭代LP细化过程来获得所有服务的所有服务的流量路由的解决方案,其结束延迟约束满足。与现有算法相比,具有指数复杂度或返回低质量解决方案,我们所提出的算法在解决方案质量和计算复杂性之间实现了更好的权衡。特别是,我们所提出的算法的最坏情况复杂性是多项式,这使得适合于解决大规模问题。数值结果证明了我们所提出的算法的有效性和效率。

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