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Joint Virtual Network Topology Design and Embedding for Cybertwin-Enabled 6G Core Networks
IEEE Internet of Things Journal ( IF 10.6 ) Pub Date : 2021-07-14 , DOI: 10.1109/jiot.2021.3097053
Junling Li , Weisen Shi , Qiang Ye , Shan Zhang , Weihua Zhuang , Xuemin Shen

To efficiently allocate heterogeneous resources for customized services, in this article, we propose a network virtualization (NV)-based network architecture in cybertwin-enabled 6G core networks. In particular, we investigate how to optimize the virtual network (VN) topology (which consists of several virtual nodes and a set of intermediate virtual links) and determine the resultant VN embedding in a joint way over a cybertwin-enabled substrate network. To this end, we formulate an optimization problem whose objective is to minimize the embedding cost, while ensuring that the end-to-end (E2E) packet delay requirements are satisfied. The queueing network theory is utilized to evaluate each service’s E2E packet delay, which is a function of the resources assigned to the virtual nodes and virtual links for the embedded VN. We reveal that the problem under consideration is formally a mixed-integer nonlinear program (MINLP) and propose an improved brute-force search algorithm to find its optimal solutions. To enhance the algorithm’s scalability and reduce the computational complexity, we further propose an adaptively weighted heuristic algorithm to obtain near-optimal solutions to the problem for large-scale networks. Simulations are conducted to show that the proposed algorithms can effectively improve network performance compared to other benchmark algorithms.

中文翻译:

支持 Cyber​​twin 的 6G 核心网络的联合虚拟网络拓扑设计和嵌入

为了有效地为定制服务分配异构资源,在本文中,我们在支持网络孪生的 6G 核心网络中提出了一种基于网络虚拟化 (NV) 的网络架构。特别是,我们研究了如何优化虚拟网络 (VN) 拓扑(由几个虚拟节点和一组中间虚拟链接组成),并确定在启用了网络孪生的基板网络上以联合方式嵌入的结果 VN。为此,我们制定了一个优化问题,其目标是最小化嵌入成本,同时确保满足端到端 (E2E) 数据包延迟要求。排队网络理论用于评估每个服务的 E2E 数据包延迟,它是分配给嵌入式 VN 的虚拟节点和虚拟链路的资源的函数。我们揭示了所考虑的问题在形式上是一个混合整数非线性程序 (MINLP),并提出了一种改进的蛮力搜索算法来找到其最优解。为了增强算法的可扩展性并降低计算复杂度,我们进一步提出了一种自适应加权启发式算法,以获得大规模网络问题的近最优解。进行仿真表明,与其他基准算法相比,所提出的算法可以有效地提高网络性能。我们进一步提出了一种自适应加权启发式算法,以获得大规模网络问题的近乎最优解。进行仿真表明,与其他基准算法相比,所提出的算法可以有效地提高网络性能。我们进一步提出了一种自适应加权启发式算法,以获得大规模网络问题的近乎最优解。进行仿真表明,与其他基准算法相比,所提出的算法可以有效地提高网络性能。
更新日期:2021-07-14
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