Information centric networking caching method based on content popularity prediction
An information-centric network and popularity technology, applied in data exchange networks, transmission systems, instruments, etc., can solve the problems of shortening node life, ignoring user distinction, lack of data content object integration and processing, etc.
Patent Information
- Authority / Receiving Office
- CN · China
- Current Assignee / Owner
- Publication Date
- 2017-02-22
Smart Images

Figure 1 
Figure 2 
Figure 3
Abstract
Description
technical field
[0001] The present application relates to the technical field of communication network data processing, in particular to an information center network caching method based on content popularity prediction. Background technique
[0002] With the development of communication networks, the future network will use content distribution and sharing as the main application. At the same time, the future network will have thousands of terminals connected to the network. The existing Internet centered on IP addresses cannot solve the complex and Insufficient bandwidth is limited, and Information Centric Networking (ICN) solves the problem that each access to content in the end-to-end communication mode must be indirectly mapped to the device where the content is located by focusing on the data content itself rather than the location of the data content. problem, thereby effectively reducing network traffic overhead.
[0003] One of the key technologies of ICN is in-ne...
Examples
Embodiment 1
[0072] Embodiment 1 of the present invention provides an information center network caching method based on content popularity prediction, the method includes the following steps:
[0073] Step S1, node n records all Interest packets passing through the node, and calculates the similarity between the content requested by all Interest packets passing through the node according to the name information of the content requested by each Interest packet;
[0074] Wherein, calculating the similarity between contents in step S1 includes:
[0075] A total of S topics are defined according to the shared content in the network, m 1 , m 2 ,...,m S is the topic probability distribution of content m, r 1 , r 2 ,...,r S is the topic probability distribution of content r, using the relative entropy distance to calculate the similarity between content m and content r, then the relative entropy distance between content m and content r is expressed as D KL (m, r), where,
[0076]
[00...