摘要
网络流量预测可以对大规模网络进行合理的管理、规划和设计。但传统网络存在隐蔽信道攻击行为,在正常的传输中隐秘地传送一些信息。上述隐秘信息不具备明显的检测特征,导致传统方法进行网络流量预测时,不能根据流量特征检测到隐秘信息流量,导致检测值与真实值之间差距过大,网络流量预测误差大。提出一种采用马尔柯夫链算法的IPV6环境下的网络流量预测方法。首先对IPV6环境下的网络流量历史值进行分析,计算网络流量历史观察值加权后的总和,根据总和建立马尔柯夫链网络流量预测模型,对IPV6环境下的网络流量作出初步的预测,并将灰色理论引入到组建的马尔柯夫链模型中,得到IPV6环境下网络流量初步预测的残差值,并对其进行修正,将IPV6环境下的网络流量状态划分为不同的区间,以此为依据对IPV6环境下的网络流量进行精确的预测。仿真结果证明,改进算法可以有效预测IPV6环境下的网络流量的变化趋势,提升了IPV6环境下的网络性能管理质量。
The network traffic prediction can manage, plan and design the large-scale network. In this paper, we proposed an algorithm of network traffic prediction under IPV6 environment using the Markov chain algorithm. Firstly, the method analyzed the history value of network traffic under IPV6 environment and calculated the history observed value summation after weighting. Then it built a prediction model of network traffic prediction based on the Markov chain according to the summation to make preliminary prediction on the network traffic under IPV6 environment. We also introduced grey theory into the model to obtain the residual value of preliminary prediction on the network traffic under IPV6 environment and made amendment to it. Finally, we divided the network traffic state into different sections under IPV6 environment and conducted precision prediction on network traffic on that basis. The simulation results show that the modified algorithm can predict the variation trend of network traffic under IPV6 environment effectively. It improves the management quality of network performance under IPV6 environment.
出处
《计算机仿真》
CSCD
北大核心
2016年第9期313-316,共4页
Computer Simulation
基金
湖北省教育厅人文社会科学研究青年项目(15Q065)
