For users requesting popular contents from content providers, edge caching can alleviate backhaul pressure and enhance the quality of experience of users. Recently there is also a growing concern about content freshness that is quantified by age of information (AoI). Therefore, AoI-aware online caching algorithms are required, which is challenging because the content popularity is usually unknown in advance and may vary over time. In this paper, we propose an online digital twin (DT) empowered content resale mechanism in AoI-aware edge caching networks. We aim to design an optimal two-timescale caching strategy to maximize the utility of an edge network service provider (ENSP). The formulated optimization problem is non-convex and NP-hard. To tackle this intractable problem, we propose a DT-assisted Online Caching Algorithm (DT-OCA). In specific, we first decompose our formulated problem into a series of subproblems, each handling a cache period. For each cache period, we use a DT-based prediction method to effectively capture future content popularity, and develop online caching strategy. Competitive ratio analysis and extensive experimental results demonstrate that our algorithm has promising performance, and outperforms other benchmark algorithms. Insightful observations are also found and discussed.

翻译：针对用户从内容提供商请求热门内容的需求，边缘缓存技术能够减轻回程链路压力并提升用户体验质量。近年来，内容新鲜度问题日益受到关注，其量化指标为信息年龄（AoI）。因此，需要设计AoI感知的在线缓存算法，而此类算法的挑战在于内容流行度通常无法预先获知且可能随时间动态变化。本文提出一种在线数字孪生（DT）赋能的内容转售机制，应用于AoI感知的边缘缓存网络。我们旨在设计一种最优双时间尺度缓存策略，以最大化边缘网络服务提供商（ENSP）的效用。所构建的优化问题具有非凸性和NP难特性。为求解这一复杂问题，我们提出DT辅助在线缓存算法（DT-OCA）。具体而言，首先将原问题分解为一系列子问题，每个子问题对应一个缓存周期。针对每个缓存周期，采用基于DT的预测方法有效捕获未来内容流行度，并制定在线缓存策略。竞争比分析与大量实验结果表明，本算法具有优异性能，且优于其他基准算法。此外，本文还发现并讨论了若干具有启发性的现象。