FCTrees: A Front-Coded Family of Compressed Tree-Based FIB Structures for NDN Routers

Karrakchou, Ouassim; Samaan, Nancy; Karmouch, Ahmed

doi:10.1109/tnsm.2020.2969172

Cited by 8 publications

(2 citation statements)

References 47 publications

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“…The lookup algorithm of NDN can be divided into two categories in [8]: trie-based [9][10][11][12][13][14], hash-based [15][16][17][18][19][20][21], bloom filter-based [22][23][24] and other kind of lookup algorithm [25][26][27][28]. Considering the demand of Longest Prefix Match (LPM) in different lookup methods, trie-based algorithms, such as NPT [9], NCE [10], MATA [11], BPT [12], can collaborate with the LPM method effectively owing to the feature of trie structure.…”

Section: Related Workmentioning

confidence: 99%

“…Trie-based method is a common category in NDN name lookup, and specifically, it can be separated as four sub-categories [8,28]: component-based [9], encoded component-based [10,14,29], character-based [11,13] and bit-based [12]. Their data structures are all based on trie structure, and the difference is only the granularity when inserting a name into a trie.…”

Section: Related Workmentioning

confidence: 99%

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An Entropy-Based Approach: Compressing Names for NDN Lookup

Niu

Yang

2021

IEEE Access

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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.

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