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On the Exploration and Exploitation Trade-off in Cooperative Caching-enabled Networks

机译：在合作缓存的网络中的探索与剥削权衡

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摘要

Edge-caching is considered as a promising solution to address network congestion and to reduce delivery latency by bringing relevant contents close to users. In this context, the commonly used notion involves the storage of the most popular contents in the cache, while consequently increasing the cache hit ratio (CHR). In the majority of prior works, the content popularity is assumed to be perfectly known and often a priori. However, in reality, the content popularity has to be explored especially for uncertain contents, such as new entrants and items with fast time-varying popularity. In this paper, we develop a framework to analyze the joint exploration and exploitation trade-off by caching both popular and uncertain contents to enable more efficient content caching. Particularly, we formulate an optimization problem to maximize the trade-off between exploration and exploitation subject to the storage capacity, guaranteed CHR and back-haul energy budget constraints. Furthermore, we solve the formulated mixed-integer combinatorial problem by using branch-and-bound method and relaxing the binary to box constraints. The superiority in performance of the proposed method over the state-of-the-art solution is demonstrated in terms of the CHR and back-haul energy on a realistic Movie-lens dataset.
机译：边缘缓存被认为是解决网络拥塞的有希望的解决方案，并通过将相关内容靠近用户来减少交付延迟。在这种情况下，常用的概念涉及在缓存中存储最流行的内容，同时因此增加了高速缓存命中率（Chr）。在大多数事先作品中，假设内容流行度被认为是完全知名的并且通常是先验的。然而，实际上，必须为不确定内容探索内容人气，例如新进入者和具有快速时变得普及的项目。在本文中，我们开发了一个框架，通过缓存流行和不确定的内容来实现一个流行和不确定的内容来分析联合探索和开发权衡，以实现更有效的内容缓存。特别是，我们制定了优化问题，以最大限度地提高勘探和剥削之间的权衡，以储存容量，保证Chr和返回能源预算约束。此外，我们通过使用分支和绑定方法来解决配交的混合整数组合问题，并放松二进制文件到框限制。就现实的电影镜头数据集上的CHR和背荷能量而言，在最先进的解决方案上表现了所提出的方法的优越性。

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