Low-complexity collaborative caching strategy based on spatio-temporal graph convolutional model

Abstract

To meet the low delay requirements for data content access in industrial IoT, efficient content caching strategies need to be designed in the Mobile Edge Computing (MEC). Most existing caching policies reduce access delay by predicting content popularity and caching popular content earlier during off-peak traffic, but these strategies only focus on the temporal order of content popularity without fully considering the graph topology of MEC servers and ignore the spatial correlation of content popularity. This paper proposes a low-complexity collaborative caching strategy based on spatio-temporal graph convolutional model (STCC). We integrate graph convolutional neural network and gated recurrent unit to construct spatio-temporal graph convolutional model, which mines the spatio-temporal correlation features of content popularity and make effective predictions on the MEC server graph topology constructed by proximity and semantic relations. Furthermore, we use hierarchical clustering to classify MEC servers into collaborative domains and design a low-complexity heuristic collaborative caching content placement algorithm to minimize the average access delay. Compared with the existing MEC server caching strategy, simulation experiments show that STCC has a better prediction effect of content popularity, and achieves better performance in the two indicators of the cache hit ratio and average access delay.