The Research Of Constraint QoS Routing Technology Based On The Next Generation Network

With the development of network technology, the constantly emerging new business put forward different requirements to meet the user's QoS (Quality of Service). In recent years, the industry paid widely attention to the technologies of network integration in the NGN(Next Generation Network). Among them, the forecasting of the available resources, the management of the networks and scheduling of network resources are the difficult pointsBased on the next generation virtual network structure, full considering the different performance characteristics of the network, such as available bandwidth, the condition of the network traffic, the nodes location, the nodes resource constraints and the system reliability requirements etc, this paper cited the respective advantages of the modern optimization algorithm of GA (Genetic Algorithm) and PSO (Particle Swarm Optimization) algorithm to put forward the new algorithm—GA-PSO, and applied the new algorithm to the multi-constraint QoS routing selection, which can comprehensively consider the network delay, delay jitter, bandwidth, packet loss rate and other index constraints, make full use of the dynamic network information and adaptive respond to the network state changes. The new optimization routing scheme gets the comprehensive minimum cost under the multi-constraints and achieves high utilization of the whole network resources.The mainly work of this paper includes the following:(1) Putting forward the multi-constraint QoS routing model accord to the next generation hierarchical network. The network has constraints of the bandwidth, delay, packet loss rate, delay jitter and cost etc, the question is how to effectively find out the optimal path from the source node to destination node while satisfy the constraints condition.(2) Defining the fitness function F(x), which can comprehensively evaluate the path. The fitness value calculated by the fitness function is a new routing index, which is used to instead the traditional evaluation metrics like hop count or delay. Fitness function is composed of the target function and the penalty function. The design of the penalty function, which is transformed from the index constraints, is one of the paper's innovative points. By transforming the multi-constraints to be part of the fitness functions, the constrained QoS routing optimal problem turn to a optimization problem without restriction.(3) Proposing the GA-PSO optimization algorithm,which is inspired by the modem heuristic optimization algorithm of genetic algorithm and particle swarm algorithm. Firstly, the GA-PSO algorithm is based on the PSO, introducing the thought of natural selection and variation in GA,which can enhance the diversity of particle swarm and improve the global search ability; Secondly, discretising the GA-PSO algorithm process, so that the algorithm can used in the routing problem; Finally, discussing the neighborhood selection method of the particle swarm. Including the global neighbor definition method and the local neighbor definition method.Then we get the improved particle swarm optimization algorithm that combines genetic algorithm thoughts can be applied in the routing problem.(4) Using the Matlab software to analysis the simulation results of the proposed GA-PSO applied in the QoS routing. The simulation is mainly discussed in three apart:the feasibility, effectiveness and reliability. The output is the chart about the GA-PSO algorithm's specific iterative process and search probability.Through the simulation, the proposed GA-PSO optimization algorithm showed that can be successfully applied in the constraint QoS routing problem, can better prevent converging in the local optimal solution after introducing the GA algorithm thoughts and can greatly improve the search probability under the definition of local neighbors.