论文标题

一种基于对IOD架构的多目标优化的多域VNE算法4.0

A multi-domain VNE algorithm based on multi-objective optimization for IoD architecture in Industry 4.0

论文作者

Zhang, Peiying, Wang, Chao, Qin, Zeyu, Cao, Haotong

论文摘要

未来,无人机(UAV)的应用前景广泛,尤其是在行业4.0。无人机互联网(IOD)的开发使无人机操作更加自治。网络虚拟化技术是支持IOD的有前途的技术,因此虚拟资源的分配成为IOD的关键问题。如何合理地分配潜在的物质资源已成为要解决的紧迫问题。本文的主要工作如下:(1)为了提高优化性能并减少计算时间,我们提出了一个多域虚拟网络嵌入算法(MP-VNE),该虚拟网络(MP-VNE)采用了集中的层次层次多组构建体系结构。提出的算法可以通过将遗传变异因子纳入传统的粒子群优化过程来避免局部最佳。 (2)为了简化多目标优化问题,我们通过加权求和方法将多目标问题转化为单目标问题。结果证明所提出的算法可以快速收敛到最佳解决方案。 (3)为了降低映射成本,我们提出了一种基于估计的映射成本选择候选节点的算法。每个物理域根据估计的映射成本的公式计算所有节点的估计映射成本,并选择最低估计的映射成本作为候选节点的节点。仿真结果表明,就延迟,成本和全面指标而言,所提出的MP-VNE算法的性能比MC-VNM,Lid-VNE和其他算法更好。

Unmanned aerial vehicle (UAV) has a broad application prospect in the future, especially in the Industry 4.0. The development of Internet of Drones (IoD) makes UAV operation more autonomous. Network virtualization technology is a promising technology to support IoD, so the allocation of virtual resources becomes a crucial issue in IoD. How to rationally allocate potential material resources has become an urgent problem to be solved. The main work of this paper is presented as follows: (1) In order to improve the optimization performance and reduce the computation time, we propose a multi-domain virtual network embedding algorithm (MP-VNE) adopting the centralized hierarchical multi-domain architecture. The proposed algorithm can avoid the local optimum through incorporating the genetic variation factor into the traditional particle swarm optimization process. (2) In order to simplify the multi-objective optimization problem, we transform the multi-objective problem into a single-objective problem through weighted summation method. The results prove that the proposed algorithm can rapidly converge to the optimal solution. (3) In order to reduce the mapping cost, we propose an algorithm for selecting candidate nodes based on the estimated mapping cost. Each physical domain calculates the estimated mapping cost of all nodes according to the formula of the estimated mapping cost, and chooses the node with the lowest estimated mapping cost as the candidate node. The simulation results show that the proposed MP-VNE algorithm has better performance than MC-VNM, LID-VNE and other algorithms in terms of delay, cost and comprehensive indicators.

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