A Multi-Domain VNE Algorithm based on Load Balancing in the IoT networks

TL;DR

This paper proposes a load-balanced, hybrid genetic algorithm for virtual network embedding in IoT networks, improving flexibility and performance in complex, high-traffic environments.

Contribution

It introduces a novel hybrid genetic algorithm with load balancing mechanisms for more adaptable virtual network mapping in heterogeneous IoT networks.

Findings

Improved load balancing and mapping success rate.

Enhanced algorithm flexibility and adaptability.

Better performance metrics in simulations.

Abstract

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.