ASON BoD业务在线预测与带宽调整研究

Study on online prediction of BoD services and bandwidth adjustment in ASON

文章提出了微粒群优化的3层BP神经网络在线3步预测智能光网络带宽按需分配(BoD)业务流量模型,并改进了微粒群优化算法。对适应值较差的一部分微粒施加随机扰动,然后评估扰动效果,接受进化同时以一定概率接受退化。按3步预测中的最大值分配带宽,降低了突发业务阻塞率。仿真结果表明,该预测模型适应突发性、多样性BoD业务流量的在线预测。

In this paper, a 3-layer PSO-trained BP Artificial Neural Networks （ANN） model is proposed for the on-line prediction of Bandwidth on Demand （BoD） ASON services and the Particle Swarm Optimization （PSO） algorithm improved. Particles with poor fitness are imposed with disturbances at random and then the disturbance effect is evaluated. The improved particles are accepted and at the same time the degenerated ones are also accepted in a certain probability. The maximum of the three predicted data is considered as the future bandwidth distribution, thus reducing the blocking rate of the bursting service. The simulation results show that this prediction model is appropriate for the on-line prediction of bursting and diversified BoD services.