Self-adaptive multi-constraint path searching method based on feedback

Abstract

The invention provides a self-adaptive multi-constraint path searching method based on feedback. The self-adaptive multi-constraint path searching method based on the feedback comprises the following steps: when searching a first path, which meets a first additive constraint and does not meet a second additive constrain, from a first node to a second node based on a single-source shortest path algorithm, searching a second path, which meets the second additive constraint, from the second node to the first node; and when determining the second path does not meet the first additive constraint, determining a measuring function for searching a third path from the first node to the second node based on the first additive constraint of the first path, the second additive constraint of the second path, the preset first additive constraint and the preset second additive constraint to search the third path based on the single-source shortest path algorithm so as to determine whether the third path meets the first additive constraint and the second additive constraint at the same time. Therefore, the searching success rate can be effectively improved.

Description

technical field [0001] The invention relates to a router path search method, in particular to a feedback-based self-adaptive multi-constraint path search method. Background technique [0002] With the increasing network scale, QoS path algorithm has always been a research hotspot in the network field. With the rapid development of network technology and the rise of various network services, people have higher and higher requirements for network service quality, but IP networks cannot provide service quality assurance. In order to meet the needs of users, multiple QoS constrained routing is proposed. Multi-QoS constrained routing is a routing mechanism based on data flow QoS requests and network available resources. Multi-QoS constrained routing can not only meet the transmission requirements of services with strict requirements on multiple QoS parameters, but also improve network transmission efficiency and make full use of network resources. Network operators can also use...

AI Technical Summary

Problems solved by technology

The disadvantage is that the success rate is relatively low. Although the path found at the end has the least cost, the searched path may not be able to satisfy the two additivity QoS constraints at the same time.

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