Multi-Head Attention Based Popularity Prediction Caching in Social Content-Centric Networking With Mobile Edge Computing

With the rapid growth of social network traffic, the design of an efficient caching strategy is crucial in the social content-centric network (SocialCCN). In order to design a more comprehensive popularity prediction caching strategy, in this letter, we proposed a novel architecture that integrates mobile edge computing (MEC) in SocialCCN (MeSoCCN) and proposed multi-head attention based popularity prediction caching strategy in MeSoCCN. Firstly, we proposed a multi-head attention based popularity prediction model (MAPP) that considers multi-dimensional features including history and future popularity, social relationships, and geographic location to predict content popularity. Then, we design a caching strategy based on the prediction results of MAPP. The simulation results show that the proposed MAPP model achieves lower predictive error and the proposed predictive caching strategy improves cache hit rate and reduces hop redundancy in the network.

Automated summary

This letter proposes a novel architecture integrating mobile edge computing in a social content-centric network and a multi-head attention based popularity prediction caching strategy. The simulation results show that the proposed model achieves lower predictive error and improves cache hit rate, reducing hop redundancy in the network.