Virtual network embedding is one of the key problems of network virtualization. Since virtual network mapping is an NP-hard problem, a lot 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.
翻译: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-硬问题,许多研究都侧重于进化算法的杰作遗传算法。然而,传统方法的参数设置过于依赖经验,其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接映射战略很容易在高流量环境中造成连接阻塞。在涉及医疗、救灾、生命支持和其他设备、网络性能和稳定性的IoT环境中,涉及医疗、救灾、生命支持和其他设备、网络性能和稳定性的IoT环境中,尤其重要。因此,如何在交通量大的多样化网络环境中提供更灵活的虚拟网络映射服务是一个紧迫的问题。针对这一问题,提出了基于混合基因算法的虚拟网络映射战略。该战略使用一种动态计算的交叉概率和基于phloomone的突变基因选择战略来提高算法的灵活性。此外,还引入了基于负载平衡的重量更新机制,以降低制图失败的可能性,同时平衡负荷。模拟结果显示,拟议的方法在一定的时间接受率中保持一定的接受率,包括平均报价。