Bee colony optimization based network traffic scheduling method under multiple QoS (quality of service) constraints

Abstract

The invention provides a bee colony optimization based network traffic scheduling method under multiple QoS (quality of service) constraints. According to the method, a multi-objective optimization problem is solved with a multi-objective artificial bee colony optimization algorithm, a fitness function is improved in combination of the algorithm with a Pareto sorting mechanism and a crowding distance, solution selection is performed with a Boltzmann strategy, a found Pareto solution is recorded with an external file, and neighborhood search of the colony is guided according to global information, so that the found Pareto optimal solutions are uniformly distributed at the real Pareto optimal front end. The degree of importance of each objective is analyzed according to actual conditions, an optimal traffic scheduling scheme is determined, so that after traffic scheduling, the network traffic is scheduled as required, the service level for users is improved, the utilization rate of network resources is increased, the load balancing purpose is achieved, and the traffic scheduling effect is optimal. With the application of the method, the high-utilization and low-consumption traffic scheduling of the network traffic under the multiple QoS constraints can be realized.

Description

technical field [0001] The present invention is a network traffic scheduling scheme designed based on a multi-objective Artificial Bee Colony Algorithm (Multi-objective Artificial Bee Colony Algorithm), which is applicable to multi-QoS (Quality of Service, quality of service) constraints, and realizes network traffic under multi-QoS constraints. Traffic load balancing. The technology belongs to the field of computer network. Background technique [0002] With the rapid development of the Internet in the world and the rapid popularization of various Internet applications, the number of network users is increasing day by day, and the needs of users for various network information resources and the information generated are increasing. The increase in traffic, the rapid growth of visits and data traffic, resulting in a substantial increase in network traffic. The traffic in the network is unevenly distributed. Some links in the network are congested due to overload, while oth...

AI Technical Summary

Problems solved by technology

For modern networks with wide coverage, limited line resources, high-speed bandwidth costs, and the contradiction between rapidly increasing business traffic and limited bandwidth resources, traffic on the network is prone to congestion, resulting in increased business delays and traffic jitter. User network requirements cannot be met

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