Abstract:

To avoid abnormal traffic affecting the safe operation of the cloud architecture data center network, it was necessary to filter the abnormal traffic of the cloud architecture data center network.The difficulty of filtering abnormal traffic varies under different signal-to-noise ratios and channel conditions.In order to ensure the filtering effect of abnormal traffic under different filtering conditions, a cloud architecture data center network abnormal traffic filtering algorithm based on improved grey clustering algorithm was proposed.A network traffic transmission model was built for cloud architecture data centers through time-frequency analysis, and network traffic sequences were collected.Weighted generalized distance was introduced to improve the grey clustering algorithm, and the improved grey clustering algorithm was used to calculate the optimal clustering results of network traffic sequence features, achieving traffic sequence feature extraction.The principal component eigenvalues of traffic sequence features were obtained through principal component analysis, two subspaces were constructed, and traffic features were mapped in a matrix manner to the two subspaces.Abnormal traffic was filtered based on the square prediction error of the mapping period vector and the threshold calculation results.The experimental results show that this algorithm can achieve feature extraction of data center network traffic sequences through clustering, effectively filtering abnormal traffic under different signal-to-noise ratios and channel conditions.When the signal-to-noise ratio of the network was 25 dB and the traffic was transmitted in a Gaussian channel, the filtering effect of abnormal traffic was more prominent.

Key words: improved grey clustering, cloud architecture, data center network, abnormal traffic, weighted generalized distance, principal component analysis