To achieve scalability and security, large networks are often structured hierarchically as a collection of domains. In hierarchical networks, the topology and QoS parameters of a domain have to be first aggregated before being propagated to other domains. However, topology aggregation may distort useful information. Although spanning tree aggregation can perfectly encode attribute information of symmetric networks, it can not be applied to asymmetric networks directly. In this paper, we propose a spanning tree based attribute aggregation method for asymmetric networks. The time complexity of the proposed method and the space complexity of its resulted aggregated topology are the same with that of the spanning tree aggregation method in symmetric networks. This method can guarantee that the attributes of more than half of the links in the networks are unaltered after aggregation. Simulation results show that the proposed method achieves the best tradeoff between information accuracy and space complexity among the existing asymmetric attribute aggregation methods.

A novel approach for fast traffic classification for the high speed networks is proposed, which bases on the protocol behavior statistical features. The packet size and a new parameter named "Estimated Protocol Processing Time" are collected from the real data flows. Then a set of joint probability distributions is obtained to describe the protocol behaviors and classify the traffic. Comparing the parameters of an unknown flow with the pre-obtained joint distributions, we can judge which application protocol the unknown flow belongs to. Distinct from other methods based on traditional inter-arrival time, we use the "Estimated Protocol Processing Time" to reduce the location dependence and time dependence and obtain better results than traditional traffic classification method. Since there is no need for character string searching and parallel feature for hardware implementation with pipeline-mode data processing, the proposed approach can be easily deployed in the hardware for real-time classification in the high speed networks.