Abstract:
The cluster algorithm may make classification on a few attributes of objects. Based on the above feature, this paper studies the Gaussian mixture model (GMM) of network traffic and its log-normal distribution on flow scale. The EM algorithm is used to cluster traffics with interactive features. It is shown that EM algorithm is more appropriate on traffic clustering than K-means algorithm. The clustering analysis on both the balanced and unbalanced traffics shows that GMM is effective on different kinds of traffics. The lognormal distribution and the transitivity of power law from application layer to IP layer are studied. After the lognormal distribution in application layer produced by user behaviors and application features is transferred to IP layer via the control protocols in transport layer, the traffic presents fractal and selfsimilar on the packet scale.