Mobile Edge Cooperative Caching Strategy Based on Spatio-temporal Graph Convolutional Model
Linming Lian, Ningjiang Chen, Pingjie Ou, Xuemei Yuan · 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD) · 2022
To meet the low delay requirements for data content access in Industrial Internet of Things, efficient content caching strategies need to be designed in mobile edge computing architectures. Most existing caching strategies reduce access delay by predicting content popularity and caching popular content earlier during off-peak traffic, but these strategies only focus on the temporal features of content popularity and ignore the spatial correlation of content popularity. Therefore, we propose a mobile edge cooperative caching strategy based on spatio-temporal graph convolutional model(STCC). In STCC, we integrate graph convolutional neural network and the gated recurrent unit to mine the spatio-temporal characteristics of content popularity and make effective prediction. Moreover, we divide the collaboration domain by hierarchical clustering and design a heuristic cache placement strategy to minimize the access delay. Simulation experiments show that the spatio-temporal graph convolution model proposed in STCC can predict content popularity better than existing time series model. Compared with existing caching strategies, STCC can significantly improve the cache hit ratio and reduce the average content access delay.