To solve the problem of low routing efficiency and limited link utilization when Service Function Chain (SFC) deployment adopts the traditional shortest path algorithm in the face of large-scale network topology due to the expansion of the path search scope and only considering the shortest path each time, this paper proposes a routing strategy based on meta-heuristic algorithm to achieve the optimization of SFC routing. Firstly, the inertia weights in the original PSO algorithm are dynamically processed to adapt to the dynamic characteristics of Software Defined Networking (SDN) network topology and improve the optimization ability and convergence speed of the original algorithm. Secondly, the crossover and mutation of genetic algorithm are introduced to improve the ability of the algorithm to find the optimal path. Simulation results show that compared with the traditional k-shortest path algorithm, the proposed method can effectively improve link utilization during routing, reduce the routing time of large-scale network topology, and improve the SFC routing efficiency.
Network traffic prediction is the key to network security management and improving network operation speed. This paper proposes a network traffic chaotic prediction method based on the improved beetle swarm algorithm and optimized support vector machine. Firstly, the new network traffic time series is obtained by the phase space reconstruction method. Then the SVM is optimized by the improved beetle swarm algorithm, and the optimized SVM is used to predict the chaos of the network traffic. Finally, the improved method is compared with the experimental results of network traffic chaos prediction based on particle swarm optimization support vector machine. The results show that the algorithm proposed in this paper has better results in terms of convergence effect and prediction accuracy.
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