Built-in caching in Named Data Networking (NDN) promises to provide efficient content delivery, where the dedicated on-path caching scheme is deployed to serve users’ requests on the forwarding path. In this work, in order to utilize limited caching resources to achieve optimal performance, the caching placement decision is made by jointly considering the content popularity, underlying network topology, forwarding strategy, and on-path caching service mechanism in NDN. More specifically, we propose a service-aware caching model framework. In the model, we define the Caching Service Matrix (CSM), which describes the position where each user’s request is served for each piece of content. In order to make CSM comply with the caching placement, underlying topology, forwarding strategy, and on-path caching service mechanism, we propose two algorithms to calculate CSM under the preceding constraints. With CSM, we get a closed form expression of caching placement utility, and hence we formulate optimal caching placement into optimization problems. Moreover, considering the interdependency among the elements of the decision variable, we adopt the differential grouping co-evolutionary (DG2-E) algorithm to decompose and solve the problems. Simulation results show the proposed scheme outperforms conventional solutions in terms of inter-domain traffic saving and speed of response under both tree and arbitrary topologies.

命名数据网络（NDN）中的内置缓存有望提供有效的内容交付，其中部署了专用的基于路径的缓存方案来满足转发路径上用户的请求。在这项工作中，为了利用有限的缓存资源来实现最佳性能，通过共同考虑NDN中的内容受欢迎程度，底层网络拓扑，转发策略和路径上缓存服务机制来做出缓存放置决策。更具体地说，我们提出了一种服务感知缓存模型框架。在模型中，我们定义了缓存服务矩阵（CSM），该矩阵描述了每个用户请求为每个内容提供服务的位置。为了使CSM符合缓存位置，底层拓扑，转发策略和路径缓存服务机制，我们提出了两种在上述约束条件下计算CSM的算法。使用CSM，我们获得了缓存放置实用程序的封闭形式表达式，因此我们将最佳缓存放置公式化为优化问题。此外，考虑到决策变量元素之间的相互依赖性，我们采用差分分组协同进化算法（DG2-E）分解并解决了这些问题。仿真结果表明，该方案在树型和任意拓扑下域间流量节省和响应速度方面均优于传统解决方案。考虑到决策变量元素之间的相互依赖性，我们采用差分分组协同进化算法（DG2-E）进行分解和求解。仿真结果表明，该方案在树型和任意拓扑下域间流量节省和响应速度方面均优于传统解决方案。考虑到决策变量元素之间的相互依赖性，我们采用差分分组协同进化算法（DG2-E）进行分解和求解。仿真结果表明，该方案在树型和任意拓扑下域间流量节省和响应速度方面均优于传统解决方案。