Smart Name Lookup for NDN Forwarding Plane via Neural Networks

TL;DR

This paper introduces Pyramid-NN, a neural network-based index for NDN name lookup that reduces memory use and false positives while maintaining high lookup speed, addressing limitations of existing indexes.

Contribution

It presents a novel neural network model, Pyramid-NN, for building an efficient name lookup index tailored for NDN forwarding, improving performance over traditional methods.

Findings

Reduces memory consumption to 58.258 MB for 2 million names

Achieves throughput of about 177 MSPS on SRAMs

Maintains high lookup performance with lower false positives

Abstract

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.