Resource discovery method in wireless network virtualization environment

Abstract

The invention relates to the technical field of mobile communication, and particularly relates to a resource discovery method in a wireless network virtualization environment. The method includes thefollowing steps: virtualizing underlying physical network resources to obtain virtual network resources, and storing the virtual network resources in a resource repository for resource discovery; andclustering the virtual network resources by using a classification attribute hierarchical clustering method based on information entropy weighting to generate clustered virtual network resources, wherein virtual network providers can find the virtual network resources required by users from the clustered virtual network resources. According to the scheme of the invention, the qualitative analysisof virtual network resource characteristics is transformed into quantitative analysis, the resource discovery delay can be effectively reduced, and the efficiency of virtual network configuration canbe improved.

Description

technical field [0001] The invention relates to the technical field of mobile communication, in particular to a resource discovery method in a wireless network virtualization environment. Background technique [0002] After decades of development, wireless communication technology and network interconnection technology have become increasingly mature, and a large number of diversified mobile services have emerged, making great contributions to people's work and life. The wireless network has gradually become an important access form of the Internet in the future. However, the existing network architecture model is fixed in design and only consists of the role of an Internet service provider. It is facing challenges in terms of scalability, mobility, security, A series of problems in terms of service quality have become prominent, and it is difficult to meet the current high-quality, high-service and emerging applications that are emerging one after another. The academic cir...

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Problems solved by technology

There are also certain methods in hierarchical clustering methods (such as references: Guha, S, Rastogi, R, Kyuseok Shim. ROCK: a robust clustering algorithm for categorical attributes [J]. Information Systems, 2000, 25 (5): 345 -366.) It can cluster and classify data, but there are still significant shortcomings, the amount of calculation is relatively large, and the time complexity is high. Each clustering operation must calculate the distance between all objects in each cluster, and consume a lot of time. above operations that compare their sizes

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