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首页> 外文期刊>IEEE Transactions on Cognitive Communications and Networking >Adaptive Caching in the YouTube Content Distribution Network: A Revealed Preference Game-Theoretic Learning Approach
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Adaptive Caching in the YouTube Content Distribution Network: A Revealed Preference Game-Theoretic Learning Approach

机译：YouTube内容发布网络中的自适应缓存：一种偏好偏好的博弈论学习方法

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摘要

Cognitive systems are dynamical systems that adapt their behavior to achieve sophisticated outcomes in nonstationary environments. This paper considers two cognitive aspects regarding the problem of distributed caching with limited capacity in a content distribution network that serves YouTube users with multiple edge servers: first, the theory of revealed preference from microeconomics is used to estimate human utility maximization behavior. In particular, a nonparametric learning algorithm is provided to estimate the request probability of YouTube videos from past user behavior. Second, using these estimated request probabilities, the adaptive caching problem is formulated as a noncooperative repeated game in which servers autonomously decide, which videos to cache. The utility function tradesoff the placement cost for caching videos locally with the latency cost associated with delivering the video to the users from a neighboring server. The game is nonstationary as the preferences of users in each region evolve over time. We then propose an adaptive popularity-based video caching algorithm that has two timescales: The slow timescale corresponds to learning user preferences, whereas the fast timescale is a regret-matching algorithm that provides individual servers with caching prescriptions. It is shown that, if all servers follow simple regret minimization for caching, their global behavior is sophisticated-the network achieves a correlated equilibrium, which means that servers can coordinate their caching strategies in a distributed fashion as if there exists a centralized coordinating device that they all trust to follow. In the numerical examples, we use real data from YouTube to illustrate the results.
机译：认知系统是动态系统，可以使其行为适应非平稳环境中的复杂结果。本文考虑了两个方面的认知问题，这些问题涉及在为具有多个边缘服务器的YouTube用户提供服务的内容分发网络中容量有限的分布式缓存的问题：首先，从微观经济学中揭示偏好的理论用于估计人类效用最大化行为。特别是，提供了一种非参数学习算法，用于根据过去的用户行为来估算YouTube视频的请求概率。其次，使用这些估计的请求概率，将自适应缓存问题表述为非合作重复游戏，其中服务器自主决定要缓存哪些视频。实用程序功能权衡了本地缓存视频的放置成本和与从相邻服务器向用户交付视频相关的延迟成本。该游戏是不稳定的，因为每个地区的用户偏好会随着时间而发展。然后，我们提出了一种基于自适应流行度的视频缓存算法，该算法具有两个时标：慢速时标对应于学习用户的偏好，而快速时标是一种遗憾匹配算法，可为各个服务器提供缓存处方。结果表明，如果所有服务器都遵循简单的后悔最小化缓存策略，则它们的全局行为很复杂-网络达到了相关的平衡，这意味着服务器可以以分布式方式协调其缓存策略，就像存在一个集中的协调设备一样。他们都相信会跟随。在数字示例中，我们使用来自YouTube的真实数据来说明结果。

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