【摘要】 提出了一种基于半马尔柯夫过程的流量预测方法。通过半马尔柯夫过程描述网络流量特性,将网络流量划分为四种状态:忙、空闲、上升和下降。通过各状态下的网络流量特性及各状态间的相互转换关系,推导了对忙状态下网络流速率上界的预测方法。对广域网和局域网的实际流量数据的分析和检验表明,95%的数据均服从半马尔柯夫过程相应状态下的随机分布;90%的流量预测以0.8或0.9的概率低于我们所预计的流量上界,且主干网流量预测的流量上界与实际流量之间的相对误差低于15%。更多还原

【Abstract】 a traffic prediction method based on semi-Markov process was presented. The semi-Markov process was used to describe network traffic characteristics and divide network traffic into four states: busy, idle, rising and falling. The prediction algorithm about traffic upper bound in busy state was concluded according to traffic stochastic distribution in each state and mutual transition relationships between states. Some practical trace data from WAN and LAN were employed to verify the traffic prediction method. About 95% trace data really follow the corresponding stochastic distribution in each state and 90% of predictions are correct with the probability of 0.8 or 0.9, and the relative error is lower than 15% for backbone networks.更多还原