论文标题
自适应离线和基于在线相似性的缓存
Adaptive Offline and Online Similarity-Based Caching
论文作者
论文摘要
通过基于相似性的内容交付，可以通过在差异成本下交付相关内容来满足内容的要求。这封信介绍了跨网络的缓存和基于相似性的交付决策的联合优化，以最大程度地减少平均延迟和相似性成本的加权总和。首先引入了一种收敛的替代梯度下降算法，以提出脱机情况，并具有先验的请求率，然后扩展到在线设置。数值结果验证了该方法相对于标准的每条调查解决方案的优势。
With similarity-based content delivery, the request for a content can be satisfied by delivering a related content under a dissimilarity cost. This letter addresses the joint optimization of caching and similarity-based delivery decisions across a network so as to minimize the weighted sum of average delay and dissimilarity cost. A convergent alternate gradient descent ascent algorithm is first introduced for an offline scenario with prior knowledge of the request rates, and then extended to an online setting. Numerical results validate the advantages of the approach with respect to standard per-cache solutions.
