灰色系统理论和马尔柯夫链相结合的网络流量预测方法

网络流量预测是网络测量领域一项重要的内容．通过流量测量与预测，可以了解到网络之间的流量情况及其趋势，从而更好地进行网络改建和负载均衡的设计．针对各时刻网络流量时间序列的非线性和多时间尺度特性，提出了将灰色系统理论和马尔可夫链相结合的流量预测方法——灰色马尔可夫模型．该方法综合了GM预测所需原始数据少、方法简单，而马尔可夫链又具有非线性拟合能力的特点，并应用此模型对实际网络流量进行预测，结果表明了该方法是有效可行的．

Novel network traffic forecasting algorithm based on grey model and Markov chain

Network Traffic prediction plays an important part in network measurement. By measuring and predicting, traffic within and between networks and its tendency can be grasped. Thus, the network and its balanced load can be better reconstructed. Based on the multi-time scale and the nonlinear character of the real-timely traffic time series, the grey markov model combining grey system theory and Markov chain is put forward. This method synthe- sizes the advantages of GM forecasting method, which is simple and needs less original data, and of Markov method which possesses the characteristics of nonlinear fitting. The method is used to build model of real network traffic, and the results are much better, and the practical application results show the proposed approach is feasible and effective.