Edge QoE: Intelligent Big Data Caching via Deep Reinforcement Learning
Xiaoming He, Kun Wang, Haodong Lu, Wenyao Xu, Song Guo
Abstract
In mobile edge networks (MENs), big data caching services are expected to provide mobile users with better quality of experience (QoE) than normal scenarios. However, the increasing types of sensors and devices are producing an explosion of big data. Extracting valuable contents for caching is becoming a vital issue for the satisfaction of QoE. Therefore, it is urgent to propose some rational strategies to improve QoE, which is the major challenge for content-centric caching. This article introduces a novel big data architecture consisting of data management units for content extraction and caching decision, improving quality of service and ensuring QoE. Then a caching strategy is proposed to improve QoE, including three parts: (1) the caching location decision, which means the method of deploying caching nodes to make them closer to users; (2) caching capacity assessment, which aims to seek suitable contents to match the capacity of caching nodes; and (3) caching priority choice, which leads to contents being cached according to their priority to meet user demands. With this architecture and strategy, we particularly use a caching algorithm based on deep reinforcement learning to achieve lower cost for intelligent caching. Experimental results indicate that our schemes achieve higher QoE than existing algorithms.