Research on Load Optimization of Software-Defined Network Based on Delay and Load

Software-defined network (SDN) is a new network paradigm in recent years. Because of its advantages of global view and network programmability, SDN-based data center load balancing has gradually become a research hotspot in computer domain. Traditional load balancing methods can't effectively obtain the statistical data of network equipment or the factors influencing load balancing are too single, which leads to low efficiency and poor flexibility. Because of its specific network structure, the software definition network has the characteristics of centralized control over the acquisition and distribution of global network resources. The method adopted in this paper is ant colony algorithm, which randomly selects the value in a certain range according to the value setting of the route pheromone, so as to ensure that the ants do not rely solely on pheromones when choosing the route, but have certain “innovation” to prevent the algorithm from falling into local optimum. When the ants have traversed all the nodes, in order to avoid the left pheromones from volatilizing and make them more effective, it is necessary to update the left pheromones in time. The research shows that this algorithm is superior to the traditional algorithm and can be widely used.