Network edge cache adjusting method oriented to user mobility

Abstract

The invention discloses a network edge cache adjusting method oriented to user mobility. The method comprises the following steps: S1, grouping all network nodes to constructing architecture of multilayer network cache; S2, orderly computing the retention time at each node and the retention time at a user region by the user; S3, establishing a prediction model for the downloading capacity of the mobile user at each node according to the mobile condition of the user and the network status; and S4, adjusting an edge cache deployment way by combining the downloading capacity prediction model witha self-adaptive blocking feedback network cache adjusting method. Through the technical scheme provided by the invention, the space of a skeleton network load is further reduced by redesigning a deployment location of the cache space, a downloading condition feedback mechanism is added, the problem of the prediction limitation of the downloading capacity prediction model is corrected, the appropriate amount of and adequate amount of cache service can be provided according to a user demand, thereby satisfying the content access demand of the user with strong mobility, and the cache space canbe greatly saved.

Description

technical field [0001] The invention relates to the communication field, in particular to a user mobility-oriented network edge buffer adjustment method. Background technique [0002] With the rapid development of mobile Internet technology, the ever-increasing number of mobile networking terminal devices and explosive Internet content put forward higher requirements for mobile Internet. With the development of high-speed railways and the introduction of the concept of Internet of Vehicles, the mobility of users has been greatly improved, which poses new challenges to the traditional mobile Internet. [0003] In order to provide users with more stable connection, higher access speed, richer content and more secure communication. Edge caching has been proven to be an effective solution that can reduce the latency of user access to content and reduce the burden on the backbone network, and has received widespread attention as a countermeasure against network traffic growth. ...

AI Technical Summary

Problems solved by technology

On the other hand, when designing the cache deployment strategy, when the number of cache files increases significantly and the network burden increases, there will be a defect of network prediction limit, and the reasonable cache deployment effect of nodes will also be greatly reduced

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