Proactive caching method based on content popularity and movement rules of users

Abstract

The invention discloses a proactive caching method based on content popularity and movement rules of users. By introducing social relationship mining, users in a small cellular network are subjected to community division, and according to a Zipf law, content popularity distribution of each community is analyzed; by a frequent pattern mining algorithm, the movement rules of the users are mined, andaccording to a rule confidence degree, probability distribution that the user accesses each base station in a next time period is acquired; and according to content request distribution of the usersand probability distribution of accessing to each base station, under limited cache capacity of each base station, a cache hit ratio is maximized by optimizing a caching strategy. By adopting the proactive caching method disclosed by the invention, a return load can be reduced, and a user delay is reduced; and meanwhile, the method has the decomposition characteristic and is low in algorithm complexity.

Description

technical field [0001] The invention belongs to the technical field of communication, and in particular relates to an active caching method based on user content popularity and mobile rules. Background technique [0002] The rapid development of communication technology has brought a profound impact on people's way of life while promoting the development of society. In particular, the popularity of smartphones has not only enriched the experience of mobile users, but also brought a large number of new wireless services, resulting in explosive growth of mobile traffic. In order to meet the unprecedentedly large traffic demand, a promising approach is the deployment of small cell networks. Small base stations can offload the data traffic on the macro wireless network to micro cells. When combined with macro cells and Wi-Fi offloading, etc. When using other wireless access network technologies, small cell base stations can bring better mobile experience and wireless coverage t...

AI Technical Summary

Problems solved by technology

[0003] However, due to the large-scale deployment of small base stations, the cost of infrastructure construction has increased significantly, and operators cannot deploy large-capacity backhaul links connected to the core network for small base stations.

In the traditional network, the content requested by the user must be obtained from the server through the backhaul link. However, when the network is busy or a large number of requests flood in, it will bring a huge traffic load to the network. When the demand for road bandwidth, storage space, and processor processing capacity exceeds the inherent capacity of the network, it will cause a great burden on the network, and the backhaul link of the small base station will be congested, which will greatly increase the delay for users to obtain content. Once the network is congested, there will be data loss, increased delay, and reduced throughput. In severe cases, it will lead to "congestion collapse", the network performance will drop sharply, the user service quality will deteriorate, and the user experience quality will become unbearable.

Therefore, in this passive service mode, the small base station network cannot take advantage of its increased network capacity

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