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.

中文翻译：

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Edge QoE：通过深度强化学习进行智能大数据缓存

在移动边缘网络（MEN）中，大数据缓存服务有望为移动用户提供比普通方案更好的体验质量（QoE）。但是，越来越多的传感器和设备类型正在产生大量的大数据。提取有价值的内容进行缓存已成为满足QoE的关键问题。因此，迫切需要提出一些合理的策略来改善QoE，这是以内容为中心的缓存的主要挑战。本文介绍了一种新颖的大数据体系结构，该体系结构由用于内容提取和缓存决策，提高服务质量并确保QoE的数据管理单元组成。然后提出了一种提高QoE的缓存策略，包括三个部分：（1）缓存位置决策，即部署缓存节点以使其更接近用户的方法。（2）缓存容量评估，旨在寻找合适的内容以匹配缓存节点的容量；（3）缓存优先级选择，这导致根据内容的优先级来缓存内容以满足用户需求。通过这种架构和策略，我们特别使用了基于深度强化学习的缓存算法，以实现智能缓存的更低成本。实验结果表明，我们的方案比现有算法具有更高的QoE。我们特别使用基于深度强化学习的缓存算法来降低智能缓存的成本。实验结果表明，我们的方案比现有算法具有更高的QoE。我们特别使用基于深度强化学习的缓存算法来降低智能缓存的成本。实验结果表明，我们的方案比现有算法具有更高的QoE。