With the development of Internet of Things (IoT) and communication technology, the number of next-generation IoT devices has increased explosively, and the delay requirement for content requests is becoming progressively higher. Fortunately, the edge-caching scheme can satisfy users' demands for low latency of content. However, the existing caching schemes are not smart enough. To address these challenges, we propose a human-like hybrid caching architecture based on the software defined edge cloud, which simultaneously considers the content popularity and the fine-grained user characteristics. Then, an optimization problem with a caching hit ratio as an optimization objective is formulated. To solve this problem, using reinforcement learning, we design a human-like hybrid caching algorithm. Extensive experiments show that compared with popular caching schemes, human-like hybrid caching schemes can improve the cache hit ratio by 20%.
@article{arxiv.1910.13693,
title = {Human-Like Hybrid Caching in Software-defined Edge Cloud},
author = {Yixue Hao and Miao Li and Di Wu and Min Chen and Mohammad Mehedi Hassan and Giancarlo Fortino},
journal= {arXiv preprint arXiv:1910.13693},
year = {2019}
}