Telecommunication networks are currently realizing more-huge-than-ever data demands from subscribers all over the world. Due to the ongoing pandemic, nearly all businesses have adapted working models with remote operations. People engaged with major industries, e.g., academia, health and municipalities are utilizing online platforms to carryout their routine tasks. This indeed shifts the attention from one-to-one (unicast) communication to one-to-many (multicast) and many-to-many (multi-source multi-destination) communications. Network operators are facing increased pressure to provide quick responses in order to satisfy the bandwidth hungry and time sensitive user demands. This can only be done by enhancing deployability as well as manageability of the services. Network Function Virtualization (NFV) provides a transformation of traditional proprietary network designs to a more agile and software based environment in order to achieve flexible deployments, reduced setup costs and less-time-to-market for the new services which is very much needed in the current scenarios. Previous studies on NFV-enabled multicast problem either proposed Integer Linear Program (ILP) models, that are pretty unscalable, or heuristic-based techniques that do not guarantee good quality of the solutions obtained. In this article, we propose an NFV multicast resource optimization model exploiting the use of multiple sources and considering the end-to-end delay and bandwidth requirements. Herein, we propose a novel Dantzig-Wolfe (DW) decomposition model that tackles the complexity of the problem by breaking it down into a master problem and several pricing problems. We compare the DW approach with the ILP and heuristic methods and demonstrate that our approach achieves near to optimal solution (in comparison to heuristic based methods) much faster than ILP. We also study the dynamic admission of NFV-enabled multicast requests by solving the problem in an online manner using the batch processing of requests. We then evaluate the performance of the proposed algorithms through extensive simulations and demonstrate that proposed algorithms are promising and outperform existing solutions.

中文翻译：

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启用NFV的网络中的时延敏感多源组播资源优化：一种列生成方法

电信网络当前正在实现来自世界各地用户的前所未有的巨大数据需求。由于持续的大流行，几乎所有企业都通过远程操作调整了工作模型。从事学术界，卫生和市政等主要行业的人们正在利用在线平台执行其日常任务。这确实将注意力从一对一（单播）通信转移到一对多（多播）和多对多（多源多目标）通信。网络运营商面临越来越大的压力，要求其提供快速响应，以满足对带宽的需求和对时间敏感的用户需求。这只能通过增强服务的可部署性和可管理性来完成。网络功能虚拟化（NFV）提供了将传统专有网络设计转换为更加敏捷和基于软件的环境的功能，以实现灵活的部署，降低的安装成本并缩短新服务的上市时间，这在新功能中是非常必要的。当前方案。以前对启用NFV的多播问题的研究要么提出了不可扩展的整数线性程序（ILP）模型，要么是基于启发式的技术，不能保证所获得解决方案的质量。在本文中，我们提出了利用多个源并考虑端到端延迟和带宽要求的NFV多播资源优化模型。在此处，我们提出了一种新颖的Dantzig-Wolfe（DW）分解模型，该模型通过将问题分解为一个主要问题和几个定价问题来解决该问题的复杂性。我们将DW方法与ILP和启发式方法进行了比较，并证明了我们的方法比基于ILP的方法更快地达到了接近最优的解决方案（与基于启发式的方法相比）。通过使用请求的批处理以在线方式解决问题，我们还研究了启用NFV的多播请求的动态准入。然后，我们通过广泛的仿真评估提出的算法的性能，并证明提出的算法是有前途的并且优于现有的解决方案。我们将DW方法与ILP和启发式方法进行了比较，并证明了我们的方法比基于ILP的方法更快地达到了接近最优的解决方案（与基于启发式的方法相比）。我们还通过使用请求的批处理以在线方式解决问题，研究了启用NFV的多播请求的动态准入。然后，我们通过广泛的仿真评估提出的算法的性能，并证明提出的算法是有前途的并且优于现有的解决方案。我们将DW方法与ILP和启发式方法进行了比较，并证明了我们的方法比基于ILP的方法更快地达到了接近最优的解决方案（与基于启发式的方法相比）。通过使用请求的批处理以在线方式解决问题，我们还研究了启用NFV的多播请求的动态准入。然后，我们通过大量的仿真评估提出的算法的性能，并证明提出的算法是有前途的并且优于现有的解决方案。