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.

该论文提出一种基于多域架构和遗传变异因素优化的多域虚拟网络嵌入算法，通过加权累加法将多目标问题转化为单目标问题，选择相应物理域上的成本最低点作为备选节点，此算法在延迟、成本、综合性指标等方面都优于 MC-VNM、LID-VNE 和其他算法。