Content Popularity Estimation in Edge-Caching Networks from Bayesian Inference Perspective

Abstract

The efficiency of cache-placement algorithms in edge-caching networks depends on the accuracy of the content request’s statistical model and the estimation method based on the postulated model. This paper studies these two important issues. First, we introduce a new model for content requests in stationary environments. The common approach to model the requests is through the Poisson stochastic process. However, the Poisson stochastic process is not a very flexible model since it cannot capture the correlations between contents. To resolve this limitation, we instead introduce the Poisson Factor Analysis (PFA) model for this purpose. In PFA, the correlations are modeled through additional random variables embedded in a low dimensional latent space. The correlations provide rich information about the underlying statistical properties of content requests which can be used for advanced cache-placement algorithms. Secondly, to learn the model, we use Bayesian Learning, an efficient framework which does not overfit. This is crucial in edge-caching systems since only partial view of the entire request set is available at the local cache and the learning method should be able to estimate the content popularities without overfitting. In the simulation results, we compare the performance of our approach with the existing popularity estimation method.