The Routing Algorithm Research On PTN Network Management

With IT technology and IP networks, data services and multimedia services represented by the internet continues to develop, also the transfer environment of the business has changed a lot. The needs of users are no longer limited to the traditional fixed rate of transmission of the voice business, but now the emerging data services, multimedia services, and some traditional business transferred to the IP bearer network such as VoIP voice services. Different business has different transmission rate, so SDH system of fixed transmission rate faces many problems and challenges, and has a lot of shortcomings. Then PTN networks based on Pseudo Wire Emulation (PWE3) technology has come into being, its unified platform to carry a variety of business has solved the above problems.Base station equipment room is often unattended, so it is necessary that PTN network management system achieves the operation and management of the network equipment. In the PTN network, when a lot of the number of network elements, network environment is very complex, previous manually route to create business will no longer has an advantage. So the automatic route study not only has theoretical significance but also practical application value.This paper firstly carries out a study on the establishment of the network topology environment. In order to more closely reflect the network environment, it uses American Dr. Salama Waxman topology generator for existing network topology characteristics. But when it has many network elements, the distribution of network elements and edges is uneven. This paper uses K-means clustering algorithm to solve network element distribution and adds a network learning parameters to solve edge distribution.Then this paper has a research on classical route calculation algorithm and mainly analyzes Dijkstra algorithm, Floyd algorithm, A*algorithm, ant colony algorithm and genetic algorithm in the route calculation application. Routing path has its own characteristics in PTN network management system that it has multiple constraints. When the constraint is greater than or equal to two, it has been proven as a NP_C problem and cannot be resolved with the traditional algorithm. Currently commonly used algorithm is ant colony algorithm or a hybrid algorithm of ant colony algorithm combined with other algorithms, such as ant colony algorithm with genetic algorithm. But this algorithm has defaults of blind search, no directionality and local optimum. At the same time, this two combined intelligent heuristic algorithm produces a lot of uncertainly. This paper presents an improved ant colony algorithm in response to these shortcomings. Finally it verifies the stability and superiority of the algorithm by experimental simulation.