As an improvement of IP multicast technology, Application Layer Multicast (ALM) has many advantages such as unlimited network architecture, rich resource and high data transfer rate. Considering node performance and end to end delay, a fast and efficient method was proposed to establish application layer multicast tree, it was named Ant Colony Algorithm based Tree-ring Application Layer Multicast Model (ACOTRM). The available studies only gave a topology cursory and had no complete and clear description of the concrete construction process. In view of this, a complete ALM hierarchical tree-ring concrete construction process was put forward including several key steps, such as clustering division, connection in cluster ring, generation of feasible solution and maintenance of the model in survival time. In addition, in order to optimize the ALM state tree, each node was set with a specific priority. The simulation results show that the proposed model provides lower average delay and higher average data delivery ratio, which increases the system stability and forwarding efficiency at the same time.

The traditional data stream clustering algorithms are mostly based on distance or density, so their clustering quality and processing efficiency are weak. To address the above problems, this paper proposed a data stream clustering algorithm based on dependent function. Firstly, the data points were modeled in the form of matter-element and dependent function was established to solve the problem. Secondly, the value of the dependent function was calculated. According to this value, the degree that data point belongs to a certain cluster was judged. Then, the proposed method was applied to online-offline framework of the data stream clustering. Finally, the proposed algorithm was tested by using the real data set KDD-CUP99 and randomly generated artificial data sets. The experimental results show that clustering purity of the proposed method is over 92%, and it can deal with about 6300 records per second. Compared with the traditional algorithm, the processing efficiency of the algorithm is greatly improved. In the aspects of dimension and the number of cluster, the algorithm shows stronger scalability, and it is suitable for processing large dynamic data set.