In the edge computing (EC) environment, edge servers are deployed at base stations to offer highly accessible computing and storage resources to nearby app users. From the app vendor's perspective, caching data on edge servers can ensure low latency in app users’ retrieval of app data. However, an edge server normally owns limited resources due to its limited size. In this article, we investigate the collaborative caching problem in the EC environment with the aim to minimize the system cost including data caching cost, data migration cost, and quality-of-service (QoS) penalty. We model this collaborative edge data caching problem (CEDC) as a constrained optimization problem and prove that it is $\mathcal {NP}$NP-complete. We propose an online algorithm, called CEDC-O, to solve this CEDC problem during all time slots. CEDC-O is developed based on Lyapunov optimization, works online without requiring future information, and achieves provable close-to-optimal performance. CEDC-O is evaluated on a real-world data set, and the results demonstrate that it significantly outperforms four representative approaches.

中文翻译：

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边缘计算中的在线协作数据缓存

在边缘计算（EC）环境中，边缘服务器部署在基站，为附近的应用程序用户提供高度可访问的计算和存储资源。从应用供应商的角度来看，在边缘服务器上缓存数据可以确保应用用户检索应用数据的低延迟。但是，边缘服务器由于其大小有限，通常拥有的资源有限。在本文中，我们调查了协同缓存问题在 EC 环境中，旨在最小化系统成本，包括数据缓存成本、数据迁移成本和服务质量 (QoS) 惩罚。我们对此建模协同边缘数据缓存问题 (CEDC) 作为约束优化问题并证明它是 $\mathcal {NP}$NP-完全的。我们提出了一种称为 CEDC-O 的在线算法，以在所有时间段内解决此 CEDC 问题。CEDC-O 是基于 Lyapunov 优化开发的，无需未来信息即可在线工作，并实现可证明的接近最佳的性能。CEDC-O 在真实世界的数据集上进行评估，结果表明它明显优于四种代表性方法。