Scalable Name Lookup for NDN Using Hierarchical Hashing and Patricia Trie

Named data networking (NDN) is a content-centric network for the future of the internet. An NDN a packet is delivered based on a content name instead of a destination IP address. The name lookup for packet forwarding is challenging because the content name is variable, and its space is unbounded. This paper proposes a novel name lookup scheme that employs a hashing technique combined with Patricia tries. In this scheme, hash tables are dynamically maintained according to the hierarchical structure of the name, so the name can effectively accommodate variable and unbounded content names. Unlike chaining, a Patricia trie compares a key string only once at a leaf node, so it provides a fast name lookup. The proposed lookup scheme is implemented and evaluated by using an Intel Core i7-2600 CPU and 4GB of memory. Experimental results show that our scheme gives good scalability and a high throughput of name lookup with a controlled memory consumption.

Asymmetric Rényi Problem

In 1960 Rényi, in his Michigan State University lectures, asked for the number of random queries necessary to recover a hidden bijective labelling ofndistinct objects. In each query one selects a random subset of labels and asks, which objects have these labels? We consider here an asymmetric version of the problem in which in every query an object is chosen with probabilityp> 1/2 and we ignore ‘inconclusive’ queries. We study the number of queries needed to recover the labelling in its entirety (Hn), before at least one element is recovered (Fn), and to recover a randomly chosen element (Dn). This problem exhibits several remarkable behaviours:Dnconverges in probability but not almost surely;HnandFnexhibit phase transitions with respect topin the second term. We prove that forp> 1/2 with high probability we need$$H_n=\log_{1/p} n +{\tfrac{1}{2}} \log_{p/(1-p)}\log n +o(\log \log n)$$queries to recover the entire bijection. This should be compared to its symmetric (p= 1/2) counterpart established by Pittel and Rubin, who proved that in this case one requires$$ H_n=\log_{2} n +\sqrt{2 \log_{2} n} +o(\sqrt{\log n})$$queries. As a bonus, our analysis implies novel results for random PATRICIA tries, as the problem is probabilistically equivalent to that of the height, fillup level, and typical depth of a PATRICIA trie built fromnindependent binary sequences generated by a biased(p) memoryless source.