论文标题
雾计算网络中的能力感知边缘缓存
Capacity-Aware Edge Caching in Fog Computing Networks
论文作者
论文摘要
本文研究了雾计算网络中的边缘缓存，其中通过考虑有限的雾化缓存能力和基站（BSS）的连接能力提出了一个能力感知的边缘缓存框架。通过允许在FOG节点和云数据中心之间进行合作，平均下载时间（ADT）最小化问题被提出为多级处理器排队过程。我们证明了配制问题的凸度，并提出了一种基于乘数的交替方向方法（基于ADMM）的算法，该算法可以达到最小ADT并比现有算法快得多。仿真结果表明，必须根据网络状态来平衡BSS的雾化缓存能力和连接能力的分配。虽然当BS连接能力足够时，通过利用所有可用的FOG CACHE能力来最大化边缘 - 速率击中（ECHR）是有帮助的，但在BS连接能力不足时，最好保持较低的ECHR并将更多的流量分配给云。
This paper 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 connectivity capacity of BSs needs to be balanced according to the network status. 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.
