利用多维统计方法预测网络拥塞状态  

Network congestion status prediction with multidimensional statistical methods

作　　者：邬平[1,2] 吴斌[1] 李鑫[1] 李俊[1] 黄红伟[1] 

机构地区：[1]云南省科学技术情报研究院网络管理中心,昆明650051 [2]昆明理工大学云南省计算机技术应用重点实验室,昆明650051

出　　处：《计算机应用》2012年第5期1251-1254,共4页journal of Computer Applications

摘　　要：为在具有优先级调度网络环境下较准确地预估平均队列长度和队列等待时间这两个拥塞控制核心指标值,通过综合帕雷托分布、泊松随机过程和平均加权三种统计方法,设计一个包括数据到达过程、数据离开过程、数据优先级调度的计算模型,同时利用矩阵方法推导曲线性参数计算方程。通过仿真,实验结果与模型计算结果比较,两者偏差度小,验证了模型能准确预测网络状态。For evaluating the two values of the average queue length and the queue waiting time, the core indicators of congestion control algorithms, more accurately in the network with priority scheduling service, a computational model, which includes the data arriving process, the data leaving process and the priority scheduling service, was designed by using the three statistical methods： the Pareto distribution, the Poisson random process and the weighted average method. And the computational function of curve shape parameter was deduced by using the matrix method. By comparing the simulation results produced from a test bed with the results of the computational model, it is found that the deviation is small, which proves that the new model can predict the status of network correctly.

关 键 词：平均队列长度 队列等待时间 帕雷托分布 曲线性参数 泊松分布 计算模型 矩阵 

分 类 号：TN915.07[电子电信—通信与信息系统] TP393[电子电信—信息与通信工程]