Although network function virtualization (NFV) is a promising approach for providing elastic network functions, it faces several challenges in terms of adaptation to diverse network appliances and reduction of the capital and operational expenses of the service providers. In particular, to deploy service chains, providers must consider different objectives, such as minimizing the network latency or the operational cost, which are coupled objectives that have traditionally been addressed separately. In this paper, the problem of virtual network function (vNF) placement for service chains is studied for the purpose of energy and traffic-aware cost minimization. This problem is formulated as an optimization problem named the joint operational and network traffic cost ($\mathsf {OPNET}$OPNET) problem. First, a sampling-based Markov approximation (MA) approach is proposed to solve the combinatorial NP-hard problem, $\mathsf {OPNET}$OPNET. Even though the MA approach can yield a near-optimal solution, it requires a long convergence time that can hinder its practical deployment. To overcome this issue, a novel approach that combines the MA with matching theory, named as $\mathsf {SAMA}$SAMA, is proposed to find an efficient solution for the original problem $\mathsf {OPNET}$OPNET. Simulation results show that the proposed framework can reduce the total incurred cost by up to 19 percent compared to the existing non-coordinated approach.

中文翻译：

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用于服务链的流量感知和节能 vNF 放置：联合采样和匹配方法

尽管网络功能虚拟化 (NFV) 是一种提供弹性网络功能的有前途的方法，但它在适应各种网络设备以及减少服务提供商的资本和运营费用方面面临着一些挑战。特别是，为了部署服务链，供应商必须考虑不同的目标，例如最小化网络延迟或运营成本，这些目标是传统上单独解决的耦合目标。在本文中，为了最小化能源和流量感知成本，研究了服务链的虚拟网络功能（vNF）放置问题。这个问题被表述为一个优化问题，称为联合运营和网络流量成本（$\mathsf {OPNET}$开放网络） 问题。首先，提出了一种基于采样的马尔可夫逼近 (MA) 方法来解决组合 NP-hard 问题，$\mathsf {OPNET}$开放网络. 尽管 MA 方法可以产生接近最优的解决方案，但它需要很长的收敛时间，这可能会阻碍其实际部署。为了克服这个问题，一种将 MA 与匹配理论相结合的新方法，命名为$\mathsf {SAMA}$萨玛, 被提议为原始问题找到一个有效的解决方案 $\mathsf {OPNET}$开放网络. 仿真结果表明，与现有的非协调方法相比，所提出的框架可以将总发生成本降低多达 19%。