User-Aware and Flexible Proactive Caching Using LSTM and Ensemble Learning in IoT-MEC Networks

Abstract

To meet the stringent demands of emerging Internet-of-Things (IoT) applications, such as smart home, smart city, and virtual reality in 5G/6G IoT networks, edge content caching for mobile/multiaccess edge computing (MEC) has been identified as a promising approach to improve the quality of services in terms of latency and energy consumption. However, the limitations of cache capacity make it difficult to develop an effective common caching framework that satisfies diverse user preferences. In this article, we propose a new content caching strategy that maximizes the cache hit ratio through flexible prediction in dynamically changing network and user environments. It is based on a hierarchical deep learning architecture: long short-term memory (LSTM)-based local learning and ensemble-based meta-learning. First, as a local learning model, we employ an LSTM method with seasonal-trend decomposition using loess (STL)-based preprocessing. It identifies the attributes for demand prediction on the contents in various demographic user groups. Second, as a metalearning model, we employ a regression-based ensemble learning method, which uses an online convex optimization framework and exhibits sublinear “regret” performance. It orchestrates the obtained multiple demographic user preferences into a unified caching strategy in real time. Extensive experiments were conducted on the popular MovieLens data sets. It was shown that the proposed control provides up to a 30% higher cache hit ratio than conventional representative algorithms and a near-optimal cache hit ratio within approximately 9% of the optimal caching scheme with perfect prior knowledge of content popularity. The proposed learning and caching control can be implemented as a core function of the 5G/6G standard’s network data analytic function (NWDAF) module.