Network optimization under traffic uncertainties based on SDN

Abstract

Software Defined Networking (SDN) is an emerging network architecture that separates the control plane from the data plane to simplify and improve network management with a high degree of flexibility. Network optimization under traffic uncertainties is one of the most challenging topics in communication networks to optimize network performance and traffic delivery. Although the traffic optimization technology has been extensively studied in the industry, a traffic optimization solution different from the traditional network is needed in the SDN network, which can utilize global network information and traffic characteristics to control and manage traffic in a better way. In this paper, a Mixed Linear Geometric Programming Traffic Optimization Algorithm (MLGP-TOA) is proposed for the problem of traffic uncertainties in SDN. Aiming at minimizing the maximum link utilization (MLU), the initial problem is transformed into a convex optimization problem by monomial approximation and variable substitution. Then, the inner point method is used to find the global optimal solution, and the optimal split ratio at each node is obtained. Finally, the configuration information is sent to the data plane. The simulation results show that the algorithm can reduce MLU, so that the traffic can fully utilize network resources and avoid congestion.