2022/02/07 by Peiying Zhang, Fanglin Liu, Zhang, Peiying +10
Computer Science · Social Sciences · #Advanced Computing and Algorithms #Artificial Intelligence (cs.AI) #Energy Efficient Wireless Sensor Networks #FOS: Computer and information sciences #Networking and Internet Architecture (cs.NI) #Software-Defined Networks and 5G #cs.AI #cs.NI
paper · pdf · doi:10.48550/arxiv.2202.05667
arxiv created 2022/02/07 · openalex publication_date 2022/02/07 · arxiv updated 2022/02/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.