This article studies edge caching in fog computing networks, where a capacity-aware edge caching framework is proposed by considering both the limited fog cache capacity and the connectivity capacity of base stations (BSs). By allowing cooperation between fog nodes and cloud data center, the average-download-time (ADT) minimization problem is formulated as a multi-class processor queuing process. We prove the convexity of the formulated problem and propose an Alternating Direction Method of Multipliers (ADMM)-based algorithm that can achieve the minimum ADT and converge much faster than existing algorithms. Simulation results demonstrate that the allocation of fog cache capacity and BS connectivity capacity needs to be balanced to take the full advantage of edge caching. While the maximization of the edge-cache-hit-ratio (ECHR) by utilizing all available fog cache capacity is helpful when the BS connectivity capacity is sufficient, it is preferable to keep a lower ECHR and allocate more traffic to the cloud when the BS connectivity capacity is deficient.

中文翻译：

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雾计算网络中的容量感知边缘缓存

本文研究雾计算网络中的边缘缓存，通过考虑有限的雾缓存容量和基站（BSs）的连接能力，提出了一种容量感知边缘缓存框架。通过允许雾节点和云数据中心之间的合作，平均下载时间 (ADT) 最小化问题被表述为多类处理器排队过程。我们证明了公式化问题的凸性，并提出了一种基于乘法器交替方向法 (ADMM) 的算法，该算法可以实现最小 ADT，并且比现有算法收敛速度更快。仿真结果表明，需要平衡雾缓存容量和 BS 连接容量的分配，以充分利用边缘缓存。