A Bayesian Poisson–Gaussian Process Model for Popularity Learning in Edge-Caching Networks

Abstract

Edge-caching is recognized as an efficient technique for future cellular networks to improve network capacity and user-perceived quality of experience. To enhance the performance of caching systems, designing an accurate content request prediction algorithm plays an important role. In this paper, we develop a flexible model, a Poisson regressor based on a Gaussian process, for the content request distribution. The first important advantage of the proposed model is that it encourages the already existing or seen contents with similar features to be correlated in the feature space and therefore it acts as a regularizer for the estimation. Second, it allows to predict the popularities of newly-added or unseen contents whose statistical data is not available in advance. In order to learn the model parameters, which yield the Poisson arrival rates or alternatively the content popularities, we invoke the Bayesian approach which is robust against over-fitting. However, the resulting posterior distribution is analytically intractable to compute. To tackle this, we apply a Markov Chain Monte Carlo (MCMC) method to approximate this distribution which is also asymptotically exact. Nevertheless, the MCMC is computationally demanding especially when the number of contents is large. Thus, we employ the Variational Bayes (VB) method as an alternative low complexity solution. More specifically, the VB method addresses the approximation of the posterior distribution through an optimization problem. Subsequently, we present a fast block-coordinate descent algorithm to solve this optimization problem. Finally, extensive simulation results both on synthetic and real-world datasets are provided to show the accuracy of our prediction algorithm and the cache hit ratio (CHR) gain compared to existing methods from the literature.

Unsupervised Recurrent Federated Learning for Edge Popularity Prediction in Privacy-Preserving Mobile Edge Computing Networks

“…To this end, many different algorithms have been proposed in the literature to predict the dynamic popularity over the recent years. Some inspiring examples include the time-series prediction method [16], [20], the social-driven prediction method [21], [22], and the statistics-based prediction method [23], [24]. Jiang et al [20] proposed an online content popularity prediction algorithm by exploiting the content features and user preferences, where the user preferences were learned offline from the historically requested information.…”

“…For instance, Trzcinśki et al [23] used support vector regression based on Gaussian radial basis functions to predict the online video popularity. Similarly, a Bayesian hierarchical probabilistic model was designed [24] to regress the content popularity in an EC network. While the existing statistical approaches show potentials in achieving accurate and stable prediction, they are still far from practical use.…”

“…While the existing statistical approaches show potentials in achieving accurate and stable prediction, they are still far from practical use. For instance, the selection of probabilistic models in statisticsbased methods [23], [24] has critical impacts on the prediction performance, but the selection criteria are unclear and impossible to be a priori given in the real world. Most importantly, they inevitably raise privacy issues due to the fact that statistics-based methods require access to the historical request log data of users for popularity prediction.…”

Online Spatiotemporal Popularity Learning via Variational Bayes for Cooperative Caching

“…A binary logistic classification method was introduced in [17] to classify user interests based on content features. In [18], content features were used to improve the prediction accuracy within the Bayesian framework. The authors of [19] introduced a Bayesian factor analysis model to model the correlations among contents.…”

On the Exploration and Exploitation Trade-off in Cooperative Caching-enabled Networks

“…Further, repeated downloads of a few popular contents have been considered as a primary factor for the growth of data traffic, which causes more power consumption that in turn increases carbon emissions. Therefore to address this challenge brought by data traffic over mobile networks and to reduce the backhaul load, content caching at the edge node has been considered as a promising solution [2]- [7]. The main principle of caching is that the storage of most popular contents at the edge node effectively reduces the backhaul load while avoiding duplicate content transmissions from the original content servers [8].…”