The coordinated development of big data, Internet of Things, cloud computing and other technologies has led to an exponential growth in Internet business. However, the traditional Internet architecture gradually shows a rigid phenomenon due to the binding of the network structure and the hardware. In a high-traffic environment, it has been insufficient to meet people’s increasing service quality requirements. Network virtualization is considered to be an effective method to solve the rigidity of the Internet. Among them, virtual network embedding is one of the key problems of network virtualization. Since virtual network mapping is an NP-hard problem, a large number of research has focused on the evolutionary algorithm’s masterpiece genetic algorithm. However, the parameter setting in the traditional method is too dependent on experience, and its low flexibility makes it unable to adapt to increasingly complex network environments. In addition, link-mapping strategies that do not consider load balancing can easily cause link blocking in high-traffic environments. In the IoT environment involving medical, disaster relief, life support and other equipment, network performance and stability are particularly important. Therefore, how to provide a more flexible virtual network mapping service in a heterogeneous network environment with large traffic is an urgent problem. Aiming at this problem, a virtual network mapping strategy based on hybrid genetic algorithm is proposed. This strategy uses a dynamically calculated cross-probability and pheromone-based mutation gene selection strategy to improve the flexibility of the algorithm. In addition, a weight update mechanism based on load balancing is introduced to reduce the probability of mapping failure while balancing the load. Simulation results show that the proposed method performs well in a number of performance metrics including mapping average quotation, link load balancing, mapping cost-benefit ratio, acceptance rate and running time.

大数据，物联网，云计算和其他技术的协调发展导致了互联网业务的指数级增长。但是，由于网络结构和硬件的结合，传统的Internet体系结构逐渐显示出僵化的现象。在交通繁忙的环境中，它不足以满足人们不断增长的服务质量要求。网络虚拟化被认为是解决互联网僵化的有效方法。其中，虚拟网络嵌入是网络虚拟化的关键问题之一。由于虚拟网络映射是一个NP难题，因此大量研究集中在进化算法的杰作遗传算法上。但是，传统方法中的参数设置过于依赖经验，灵活性低，使其无法适应日益复杂的网络环境。此外，不考虑负载平衡的链路映射策略很容易在高流量环境中导致链路阻塞。在涉及医疗，救灾，生命支持和其他设备的物联网环境中，网络性能和稳定性尤为重要。因此，如何在流量大的异构网络环境中提供更加灵活的虚拟网络映射服务是当务之急。针对该问题，提出了一种基于混合遗传算法的虚拟网络映射策略。该策略使用动态计算的交叉概率和基于信息素的突变基因选择策略来提高算法的灵活性。此外，引入了基于负载平衡的权重更新机制，以降低平衡负载时映射失败的可能性。仿真结果表明，该方法在映射平均报价，链路负载均衡，映射成本效益比，接受率和运行时间等多个性能指标上表现良好。