Network content spreading method based on network node distributed Pagerank

Abstract

The invention provides a network content spreading method based on network node distributed Pagerank. The method comprises the steps that 1, by taking each node in a network server as a center node, asubgraph of the connecting relation between the center node and network adjacent nodes is constructed; 2, Pagerank values between any two nodes in the subgraphs are calculated; 3, according to an interaction operation between two nodes in the same subgraph, all the Pagerank values in the subgraph are calculated and updated; and 4, according to a set node spreading rule, two nodes corresponding tothe Pagerank value confirming to the rule are selected from each updated subgraph to spread network content. According to the network content spreading method, by setting one node which represents all other global information in each node neighbor connection relation graph to continuously iterate to gradually approach global information, the problem that the difference between a local model and the actual condition is too great is solved, and the content spreading performance is improved.

Description

technical field [0001] The invention relates to the technical field of computer networks, in particular to a network content diffusion method based on distributed Pagerank of network nodes. Background technique [0002] Currently, video and content services have become one of the main ways of Internet entertainment and occupy most of the network traffic. In order to ensure service quality, service providers usually use content distribution network, cloud service and other technologies to process large-scale user requests nearby and in a decentralized manner, thereby reducing the pressure on the center and improving processing efficiency. However, there are still some problems in such a structure, for example: the location of the data center is still far away from the user, and it is difficult to truly reflect "nearby"; the user resources in the network are huge, such as PCs, mobile phones, set-top boxes and other devices. These resources are still in an idle state, huge res...

AI Technical Summary

Problems solved by technology

However, there are still some problems in such a structure, for example: the location of the data center is still far away from the user, and it is difficult to truly reflect "nearby"; the user resources in the network are huge, such as PCs, mobile phones, set-top boxes and other devices. These resources are still in an idle state, the huge resources have not been rationally utilized, and the total amount of resources deployed is limited, and service bottlenecks will continue to appear

Existing methods pay more attention to local information, but there is no suitable method for approximating global information

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