Content caching at base stations is an effective solution to cope with the unprecedented data traffic growth by prefetching contents near to end-users. To proactively servicing users, it is of high importance to extract predictive information from data requests. In this paper, we propose an accurate content request prediction algorithm for improving the performance of edge caching systems. In particular, we develop a Bayesian dynamical model through which a complex nonlinear latent temporal trend structure in the content requests can be accurately tracked and predicted. The dynamical model also leverages tensor train decomposition to capture content-location interactions to further enhance the accuracy of predictions. To infer the model’s parameters, we derive an approximation of the posterior distribution based on variational Bayes (VB) method with an embedded Kalman smoother algorithm. Based on the predictions of the proposed model, we design a cost-efficient proactive cooperative caching policy which adaptively utilizes network resources and optimizes the content delivery. The advantage of the proposed caching scheme is demonstrated via numerical results using two real-world datasets, which show that the developed Bayesian dynamical model substantially outperforms reference methods that ignore the temporal trends and content-location interactions.

中文翻译：

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通过张量序列分解进行趋势感知的主动缓存：贝叶斯观点


基站内容缓存是通过预取靠近最终用户的内容来应对前所未有的数据流量增长的有效解决方案。为了主动服务用户，从数据请求中提取预测信息非常重要。在本文中，我们提出了一种准确的内容请求预测算法，用于提高边缘缓存系统的性能。特别是，我们开发了一个贝叶斯动态模型，通过该模型可以准确跟踪和预测内容请求中复杂的非线性潜在时间趋势结构。动态模型还利用张量序列分解来捕获内容-位置交互，以进一步提高预测的准确性。为了推断模型的参数，我们基于带有嵌入式卡尔曼平滑算法的变分贝叶斯 (VB) 方法得出后验分布的近似值。基于所提出模型的预测，我们设计了一种具有成本效益的主动协作缓存策略，该策略自适应地利用网络资源并优化内容交付。使用两个真实世界数据集的数值结果证明了所提出的缓存方案的优点，这表明开发的贝叶斯动态模型大大优于忽略时间趋势和内容位置交互的参考方法。