Caching can be leveraged to significantly improve network performance and mitigate congestion. However, characterizing the optimal tradeoff between routing cost and cache deployment cost remains an open problem. In this paper, for a network with arbitrary topology and congestion-dependent nonlinear cost functions, we aim to jointly determine the cache deployment, content placement, and hop-by-hop routing strategies, so that the sum of routing cost and cache deployment cost is minimized. We tackle this NP-hard problem starting with a fixed-routing setting, and then to a general dynamic-routing setting. For the fixed-routing setting, a Gradient-combining Frank-Wolfe algorithm with $(\frac{1}{2},1)$-approximation is presented. For the general dynamic-routing setting, we obtain a set of KKT necessary optimal conditions, and devise a distributed and adaptive online algorithm based on the conditions. We demonstrate via extensive simulation that our algorithms significantly outperform a number of baseline techniques.

翻译：缓存技术可显著提升网络性能并缓解拥塞，但路由成本与缓存部署成本之间的最优权衡表征仍是一个开放性问题。本文针对具有任意拓扑结构且包含拥塞依赖非线性成本函数的网络，旨在联合优化缓存部署、内容放置及逐跳路由策略，最小化路由成本与缓存部署成本之和。我们首先在固定路由场景下处理该NP-hard问题，随后扩展至通用动态路由场景。对于固定路由场景，提出一种具有(1/2,1)-近似比的梯度组合Frank-Wolfe算法；对于通用动态路由场景，推导出一组KKT必要条件最优性条件，并据此设计分布式自适应在线算法。通过大量仿真验证，所提算法在性能上显著优于多种基准方法。