Method and device for optimizing multi-constraint quality of service (QoS) routing selection

Abstract

The invention discloses a method and device for optimizing multi-constraint quality of service (QoS) routing selection. The method comprises the following steps: acquiring the topological structure and link parameters of an existing network in accordance with information of a prediction model; creating a corresponding multi-constraint QoS routing model in accordance with the determined topological structure and link parameters, and constructing penalty functions to transform multi-constraint conditions, as well as constructing fitness functions for evaluating paths; using a depth-first search method to acquire initial feasible paths and initializing particle swarms; calculating the fitness value of each particle, and finding out the optimal fitness value of the particle adjacent to each particle; using the generation algorithm and the genetic algorithm-particle swarm optimization (GA-PSO) to carry out iterative solution at the beginning of the initial feasible paths, and carrying out natural selection and variation operations; and finding out paths which meet conditions and are provided with the optimal fitness values, realizing optimal routing selection under the multi-constraint condition, and executing in accordance with the found routings.

Description

technical field [0001] The invention relates to the technical field of network communication routing, in particular to a method for realizing network multi-constraint QoS routing selection. Background technique [0002] At present, with the rapid development of network communication technology, the various needs of network users put forward many new requirements for the guarantee of QoS (Quality of Service) of the network, and the diversity of user services requires the network to have more reliable QoS support . QoS routing is a method to provide QoS guarantee for service flow at the network layer after integrating the information of each layer of the network. Usually, when selecting QoS routing, it is necessary to consider indicators such as bandwidth, delay, packet loss rate, delay jitter, and cost. The routing problem that considers the constraints of the above QoS indicators at the same time is called the multi-constraint QoS routing problem, and it is a problem of fin...

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Problems solved by technology

However, due to the inconsistency of the measurement scales of the various constraints in the routing problem, and the calculation of the optimal path under the condition of obtaining sufficient network state information is huge, it is often difficult to solve the optimization problem.

[0003] Most of the constraints considered in the prior art are only delay constraints and bandwidth constraints, or the optimization of a single constraint condition is sought under other constraints, so that the optimization of the overall network performance has not reached The best under all synthetic constraints

GA (Generation Algorithm Genetic Algorithm), ACO (Ant Colony Optimistic Ant Algorithm), PSO (Particles Swarm Optimistic Particle Swarm Algorithm) and other optimization algorithms have been widely used in optimization problems, and some scholars have introduced them into network routing. It has achieved good results in the problem, but it has not been well applied to the multi-constrained QoS routing problem.

The PSO algorithm has the advantage of fast convergence speed in solving continuous optimization problems, but it also has the disadvantage of being easily trapped in local optimum. The GA algorithm can select individuals with better performance by applying the ideas of Darwin's natural selection and crossover and mutation. Mutation can maintain the diversity of the population, but it does not guarantee rapid convergence within the effective time

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