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网络流量能直接反映网络性能的好坏,网络流量的建模与预测对于大规模网络的规划设计、网络资源管理以及用户行为的调节等方面都具有积极意义。本课题首先介绍了网络流量的特征,在分析了小波理论的基础上提出了一种基于小波变换的网络流量预测模型。该模型采用小波分解把网络流量数据分解成小波系数和尺度系数,即高频系数和低频系数,将不同频率成分的系数单支重构为高频流量分量和低频流量分量。本课题,我们通过网络流量采集软件来采集网络流量,网络流量的单步预测这些实验的结果验证了本文提出的预测模型的有效性和优越性。
1.1 课题研究背景和研究意义
随着计算机网络的迅速发展,目前的网络规模极为庞大和复杂,因此发生各种问题的可能性也越来越大,使管理网络的难度也增大。传统的网络管理是在告警之后,解决潜在的问题,即为一种响应式的行为,这时候网络服务很可能己经受到影响。
如果在网络Qos管理和流量工程中,能够根据实际采集的网络流量观测值序列,建立该流量参数的正常行为,然后平稳化该序列,建立网络流量预测模型,对网络流量进行预测,并检测在将来超越阈值的可能性和发生时间。这样,在网络过载发生之前,可以预先采取防范措施,来保证网络的正常服务。所以研究较好的网络流量预测模型,并分析其性能,以及研究网络流量预测的实际应用,就显得尤为重要。
小波变换是二十世纪八十年代后期发展起来的应用数学分支。与经典的傅里叶分析相比较,小波分析有着许多显著的优点。小波分析是一种在时域和频域上同时