RecCac: Recommendation-Empowered Cooperative Edge Caching for Internet of Things

Suning Han, Xiuhua Li, Sun Chuan, Xiaofei Wang, Victor C. M. Leung
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引用次数: 0

Abstract

Edge caching is an emerging technology for supporting massive content access in mobile edge networks to address rapidly growing Internet of Things (IoT) services and con⁃ tent applications. However, the edge server is limited with the computation/storage capacity, which causes a low cache hit. Cooperative edge caching jointing neighbor edge servers is re⁃ garded as a promising technique to improve cache hit and reduce congestion of the net⁃ works. Further, recommender systems can provide personalized content services to meet us⁃ er’s requirements in the entertainment-oriented mobile networks. Therefore, we investigate the issue of joint cooperative edge caching and recommender systems to achieve additional cache gains by the soft caching framework. To measure the cache profits, the optimization problem is formulated as a 0–1 Integer Linear Programming (ILP), which is NP-hard. Spe⁃ cifically, the method of processing content requests is defined as server actions, we deter⁃ mine the server actions to maximize the quality of experience (QoE). We propose a cachefriendly heuristic algorithm to solve it. Simulation results demonstrate that the proposed framework has superior performance in improving the QoE.
RecCac:物联网的推荐协作边缘缓存
边缘缓存是一种新兴技术,用于支持移动边缘网络中的大规模内容访问,以解决快速增长的物联网(IoT)服务和内容应用。然而,边缘服务器受到计算/存储容量的限制,这导致缓存命中率较低。连接相邻边缘服务器的协作边缘缓存被认为是一种很有前途的提高缓存命中率和减少网络拥塞的技术。此外,推荐系统可以提供个性化的内容服务,以满足我们在面向娱乐的移动网络中的需求。因此,我们研究了联合协作边缘缓存和推荐系统的问题,以通过软缓存框架实现额外的缓存增益。为了衡量缓存利润,优化问题被公式化为0–1整数线性规划(ILP),这是NP困难的。具体而言,处理内容请求的方法被定义为服务器行为,我们确定服务器行为以最大限度地提高体验质量(QoE)。我们提出了一种缓存友好的启发式算法来解决这个问题。仿真结果表明,该框架在提高QoE方面具有优越的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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