Name lookup is a key technology for the forwarding plane of content router in Named Data Networking (NDN). To realize the efficient name lookup, what counts is deploying a highperformance index in content routers. So far, the proposed indexes have shown good performance, most of which are optimized for or evaluated with URLs collected from the current Internet, as the large-scale NDN names are not available yet. Unfortunately, the performance of these indexes is always impacted in terms of lookup speed, memory consumption and false positive probability, as the distributions of URLs retrieved in memory may differ from those of real NDN names independently generated by content-centric applications online. Focusing on this gap, a smart mapping model named Pyramid-NN via neural networks is proposed to build an index called LNI for NDN forwarding plane. Through learning the distributions of the names retrieved in the static memory, LNI can not only reduce the memory consumption and the probability of false positive, but also ensure the performance of real NDN name lookup. Experimental results show that LNI-based FIB can reduce the memory consumption to 58.258 MB for 2 million names. Moreover, as it can be deployed on SRAMs, the throughput is about 177 MSPS, which well meets the current network requirement for fast packet processing.
翻译:命名数据网络(NDN) 中内容路由器转发路由器( NDN) 的关键技术 。 要实现高效的地名查找, 计算数字是在内容路由器中部署高性能指数。 到目前为止, 拟议的索引显示良好的性能, 大部分是当前互联网收集的 URL 优化或评估的 URL, 因为大型 NDN 名称尚未提供 。 不幸的是, 这些索引的性能在外观速度、 记忆消耗和假正概率方面总是受到影响, 因为在记忆中检索的 URM 分布可能不同于由内容中心应用程序在线独立生成的真实的 NDN 名称 。 实验结果显示, 以 LNI 为基础的 FIB 能够将内容消耗减少到58. 258 MM, 提议一个名为 Pyramid- NNN 的智能网络智能绘图模型来构建一个名为 LNI 的 NNDN 转发机 URLN 的索引。 通过学习静态记忆中检索到的名称的分布, LNI 不仅能够减少记忆消耗量和假正概率, 而且确保真实的NDN 名称的外观的运行。 。 实验结果显示基于 LIBIB 的FIB 可以将目前的M IM 。