A Fog Computing Network Content Cache Replacement Method Based on Value Function

Abstract

The invention discloses a value function-based fog computing network content cache replacement method. In the method, when a user terminal sends a content request, it sequentially accesses fog nodes to obtain content, and updates information such as the number of visits in real time; if the fog node is traversed Still unable to obtain the content, then decide whether to cache the content from the cloud to the fog node according to its value function, which combines factors such as content popularity, content size and available cache storage space; and the present invention proposes a new content cache replacement method, If the size of the cached content is larger than the remaining capacity of the fog node, execute the content cache replacement method, select the most suitable fog node to delete part of the content and store the new content. The invention can be applied to the design and deployment of the fog computing network, and the proposed content cache replacement method based on the value function improves the probability of user requests being responded, and the storage space utilization rate of the fog nodes is high, and the cache performance advantage is obvious.

Description

technical field [0001] The invention relates to the field of fog computing communication, in particular to a value function-based fog computing network content cache replacement method. Background technique [0002] Today, the demand for data traffic is increasing day by day. Cloud computing technology has made a significant contribution to the development of current communication network technology, but a large number of user visits will lead to serious problems such as insufficient bandwidth and network delay. In order to solve this problem, fog computing came into being, which effectively improves the throughput of the network and reduces the delay of the network. In the fog computing network, fog nodes are very close to users and have functions such as storage, computing, and communication. Therefore, fog nodes can cache popular and interesting content in advance. This can effectively reduce the time for users to access the network and offload backhaul traffic. There...

AI Technical Summary

Problems solved by technology

For example, fog nodes should be considered with limited storage capacity, as heavy loads will result in high latency and low Quality of Service (QoS) for users

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