智能网络中基于流量预测的加权公平调度算法

Improved WFQ algorithm combined with traffic prediction in the intelligent network

智能网络中多业务流在时延和带宽方面有不同的传输需求,且自相似流量的突发性加剧了时延和丢包率。针对此问题,提出一种基于流量预测的改进WFQ调度算法(LPR-WFQ),该算法通过TLGP策略基于流量的均值和方差对流量进行分级,在贝叶斯估计思想的基础上通过计算条件转移概率来预测未来流量等级,基于预测结果和到达速率均值动态调整权值,从而降低时延和丢包,提高服务质量,并且优化了虚拟结束时间计算方式。仿真结果表明,与其他调度算法相比,该算法在时延、时延抖动、吞吐率和丢包四种性能方面分别提升6.01%、9.66%、5.37%、38.57%,说明算法可以满足差异化服务需求下的性能要求。

In intelligent networks,multiple service flows have different transmission requirements in terms of delay and bandwidth,and the burstiness of self-similar traffic exacerbates delay and packet loss rate.To address this problem,an improved WFQ scheduling algorithm based on traffic prediction(LPR-WFQ)is proposed.This algorithm uses the TLGP strategy to classify traffic based on the mean and variance of traffic.Based on the Bayesian estimation idea,it predicts future traffic levels by calculating conditional transition probabilities.The weights are dynamically adjusted based on the prediction results and the mean arrival rate,thereby reducing delay and packet loss,improving service quality,and optimizing the calculation method of virtual finish time.Simulation results show that compared with other scheduling algorithms,this algorithm improves the delay,delay jitter,throughput and packet loss by 6.01%,9.66%,5.37%and 38.57%respectively,indicating that the algorithm can meet the performance requirements of differentiated services.