Abstract
NDN (Named Data Networking) is one of the most popular future network architecture, a “clean slate” design for replacing the traditional TCP/IP network. However, the lookup algorithm of FIB entry in NDN is the bottleneck of the current NDN. Owing to the unique identifier of content name, whose length is variable, the size of FIB entries is proliferating, and the effectiveness of lookup algorithms is low. This paper proposed an entropy-oriented name processing mechanism, compressing the content names effectively by bringing in an encoding scheme. This mechanism can be split into two parts: name compression and lookup. The first part compressed the content names and converted them into a kind of code with a smaller size by considering the information redundancies of content names; the second part built a compact structure to minimize the memory footprint of FIB entries with keeping the high lookup performance. This mechanism outperformed many traditional name lookup algorithms, had better flexibility and cost less memory footprint.
Highlights
Name Data Networking (NDN) [1] is one of the most valuable future network technologies, which is a clean-slate architecture for replacing the traditional one based on TCP/IP, concentrating on the content itself rather than the location
Current lookup algorithms are designed for conventional IP forwarding, and the precedent hardware optimizations are only applied to the fixed-length IP addresses whose lengths are fixed
We develop the Paradigm Huffman Trie (PHT) to compress content names and CompactTrie to adapt the characteristics of PHT and accelerate the lookup operation
Summary
Name Data Networking (NDN) [1] is one of the most valuable future network technologies, which is a clean-slate architecture for replacing the traditional one based on TCP/IP, concentrating on the content itself rather than the location. It is impossible to customize quantities of optimized algorithms to fit those various features completely Such a problem has been ignored: the designers concentrate on the significance of content rather than a fundamental use of a network— data transmission. We combat the challenges mentioned above and design a super-effective NDN name encoding algorithm and a relative lookup algorithm In this system, we develop the Paradigm Huffman Trie (PHT) to compress content names and CompactTrie to adapt the characteristics of PHT and accelerate the lookup operation.
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