Abstract We consider caching networks in which the routing cost for serving a content request can be reduced by caching the requested content item in cache nodes closer to the users. We refer to the cost reduction enabled by caching as the caching gain, and the product of the caching gain of a content request and its request rate as caching gain rate. We aim to study fair content allocation strategies through a utility-driven framework, where each request achieves a utility of its caching gain rate, and consider a family of α -fair utility functions to capture different degrees of fairness. The resulting problem is an NP-hard problem with a non-decreasing submodular objective function. Submodularity allows us to devise a deterministic allocation strategy with an optimality guarantee factor arbitrarily close to 1 − 1 ∕ e . When 0 α ≤ 1 , we further propose a randomized strategy that attains an improved optimality guarantee, ( 1 − 1 ∕ e ) 1 − α , in expectation. Through extensive simulations over synthetic and real-world network topologies, we evaluate the performance of our proposed strategies and discuss the effect of fairness.

中文翻译：

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公平缓存网络

摘要 我们考虑缓存网络，其中通过将请求的内容项缓存在更靠近用户的缓存节点中，可以降低服务内容请求的路由成本。我们将通过缓存实现的成本降低称为缓存增益，将内容请求的缓存增益与其请求率的乘积称为缓存增益率。我们的目标是通过效用驱动的框架研究公平的内容分配策略，其中每个请求实现其缓存增益率的效用，并考虑一系列 α -fair 效用函数来捕获不同程度的公平性。由此产生的问题是一个具有非递减子模目标函数的 NP 难问题。子模块性允许我们设计一个确定性的分配策略，其最优性保证因子任意接近 1 − 1 ∕ e 。当 0 α ≤ 1 时，我们进一步提出了一种随机策略，可以在预期中获得改进的最优性保证 ( 1 − 1 ∕ e ) 1 − α 。通过对合成和现实世界网络拓扑的广泛模拟，我们评估了我们提出的策略的性能并讨论了公平性的影响。