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Benders decomposition for a node-capacitated Virtual Network Function placement and routing problem

机译:弯曲分解用于节点电容虚拟网络功能放置和路由问题

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In this paper we study a problem faced by network service providers in which a set of Virtual Network Functions (VNFs) has to be installed in a telecommunication network at minimum cost. For each given origin-destination pair of nodes (commodities), a latency-constrained routing path has to be found that visits the required VNFs in a pre-defined order. A limited number of functions can be installed at each node. We first prove that the problem is NP-hard in a strong sense, even for a single commodity and without node-capacity, latency and precedence constraints. We then provide a compact Mixed Integer Linear Programming (MILP) formulation, along with several families of valid inequalities. To tackle the problem from a computational perspective, we provide theoretical results that allow us to derive Benders reformulation of the problem. We also exploit an alternative path-based MILP formulation to derive heuristic solutions. All these ingredients are combined in a Branch-and-Benders-Cut framework and computationally tested on a wide range of realistic instances. Our results are also compared with the Automatic Benders decomposition provided by Cplex. Computational results indicate that our decomposition approach is more efficient compared to the two methods provided by the off-the-shelf solver, both in terms of the CPU time and the overall solution quality. The results also indicate that our MILPheuristic provides high-quality solutions.(c) 2021 Elsevier Ltd. All rights reserved.
机译:在本文中,我们研究了网络服务提供商面临的问题,其中一组虚拟网络功能(VNF)必须至少成本在电信网络中安装。对于每个给定的原始目的地对节点(商品),必须发现延迟约束的路由路径以预定义的顺序访问所需的VNF。每个节点都可以安装有限数量的函数。我们首先证明问题是NP - 难以强烈的感觉,即使是单一的商品,也没有节点容量,延迟和优先约束。然后,我们提供了一种紧凑的混合整数线性编程(MILP)制剂,以及几个有效不等式的家庭。为了从计算的角度解决问题,我们提供了理论结果,使我们能够衍生出问题的重构。我们还利用基于替代路径的MILP配方来推导启发式解决方案。所有这些成分都在分支和弯管框架中组合,并在各种现实情况下计算地测试。我们的结果也与CPLEX提供的自动弯曲分解相比。计算结果表明,与现成的求解器提供的两种方法相比,我们的分解方法更有效,无论是CPU时间和整体解决方案质量。结果还表明,我们的卫星态提供了高质量的解决方案。(c)2021 Elsevier Ltd.保留所有权利。

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