The paper presents risk-neutral and risk-averse caching policies that can be deployed in a femtocell network with limited storage capacity to reduce the time delay of servicing content requests. The caching policies use a forecasting algorithm to estimate the cumulative distribution function of content requests based on the content features. Given the cumulative distribution function, a mixed-integer linear program is used to compute where to cache content in the femtocell network. The caching policies account for the uncertainty associated with estimating the content requests using the coherent Conditional Value-at-Risk (CVaR) measure. For a large number of content, a risk-neutral caching policy is constructed that accounts for both the content features and routing protocol that only requires the evaluation of a unimodular linear program. Using data from YouTube (comprising 25,000 videos) and the NS-3 simulator, the caching policies reduce the delay of retrieving content in femtocell networks compared with industry standard caching policies. Specifically, a 6\% reduction in delay is achieved by accounting for the uncertainty, and a 60\% reduction in delay is achieved if both the uncertainty and femtocell routing protocol are accounted for compared to the risk-neutral caching policy that neglects the routing protocol.

中文翻译：

---

使用密度预测的 Femtocell 网络中 YouTube 内容的风险规避缓存策略

本文提出了风险中性和规避风险的缓存策略，这些策略可以部署在存储容量有限的毫微微蜂窝网络中，以减少服务内容请求的时间延迟。缓存策略使用预测算法根据内容特征估计内容请求的累积分布函数。给定累积分布函数，混合整数线性程序用于计算在毫微微蜂窝网络中缓存内容的位置。缓存策略考虑了与使用一致的条件风险价值 (CVaR) 度量估计内容请求相关的不确定性。对于大量内容，构建风险中性缓存策略，考虑内容特征和路由协议，只需要评估单模线性程序。使用来自 YouTube（包含 25,000 个视频）和 NS-3 模拟器的数据，与行业标准缓存策略相比，缓存策略减少了在 femtocell 网络中检索内容的延迟。具体来说，通过考虑不确定性实现了 6% 的延迟减少，如果与忽略路由的风险中立缓存策略相比，如果同时考虑了不确定性和 femtocell 路由协议，则实现了 60% 的延迟减少协议。