Bipartite graph sampling method and device for test bed topology

Abstract

This disclosure is oriented to the bipartite graph topology widely existing in computer networks, and proposes a bipartite graph sampling method and device for testing bed topology, adopting the bipartite graph sampling method, and reducing the test by a large proportion under the premise of maintaining the stability of topology attributes. The number of simulation resource nodes required for bed construction solves the difficult problem of test bed construction under the condition of limited simulation resources, thereby achieving economic benefits such as greatly reducing test bed construction costs and increasing the complexity of test task running time. The present invention can maintain nodes Under the premise that important topological properties such as degree distribution, clustering coefficient and path length are stable, that is, they remain approximately unchanged, the scale of the bipartite graph topology of the computer network test bed can be reduced in a large proportion, and the scale reduction rate can reach more than 96%.

Description

technical field [0001] The invention relates to the field of network topology, in particular to a bipartite graph sampling method and device for a test bed topology. Background technique [0002] The development, testing, and evaluation of computer network technologies such as routing protocols and resource positioning require the construction of corresponding simulation test beds. Among them, the scale reduction of the test bed topology is an important guarantee to reduce the cost of test bed construction and improve the efficiency of test running time. In the diverse topological connections of computer networks, the bipartite graph topology is a widely existing connection mode. When it is necessary to test and evaluate the rationality of the topology connection relationship between subnets and subnets in a multi-subnet structure, a bipartite graph topology between subnets needs to be configured on the simulated computer network test bed. Graph sampling is a common method...

AI Technical Summary

Problems solved by technology

However, existing graph sampling techniques (for example, forest fire, random walk, breadth first search, and snowball) have a wide range of applications and can be applied to diverse graph structures such as social networks and transportation networks. The perception of the unique attributes of the graph topology cannot achieve a large scale reduction of more than 70% of the bipartite graph topology under the condition of maintaining the stability of the attributes

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