Kavli Affiliate: Zhuo Li
| First 5 Authors: Zhuo Li, Jindian Liu, Liu Yan, Beichuan Zhang, Peng Luo
| Summary:
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.
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