Dynamically Allocated Bloom Filter-Based PIT Architectures

doi:10.1109/ACCESS.2022.3158368

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Dynamically Allocated Bloom Filter-Based PIT Architectures

2022-01-01

Saeyoung Jang

Hayoung Byun *

Hyesook Lim

DOI：10.1109/ACCESS.2022.3158368

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Abstract

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As a key component in implementing Named Data Networking (NDN), Pending Interest Table (PIT) requires an efficient exact-matching algorithm for a scalable and fast PIT lookup. A Bloom filter (BF) is a memory-efficient data structure for performing exact matching operations. In this paper, three different BF-based PIT architectures are proposed: PIT using functional Bloom filters (FBF-PIT), PIT using counting Bloom filters with return values (rCBF-PIT), and a refined rCBF-PIT with signatures (R-rCBF-PIT). The proposed BF-based PITs incrementally allocate a new BF for storing multiple incoming faces of Interest packets with the same content name. For a Data packet lookup, the proposed PIT architectures simultaneously access every BF structure to find matching faces and delete the faces (i.e., matching Interest packet information). The functional Bloom filter (FBF) used in an FBF-PIT is a key-value data structure that stores values only without keys. However, because the number of non-reusable conflict cells in the FBF increases as the number of stored packets increases in the FBF-PIT, the indeterminable rate increases. To decrease the indeterminable rate, we propose the rCBF-PIT, which uses counting Bloom filters with return values (rCBFs), allowing reusable conflict cells. False positives for Interest packets lead to incorrect deletions that can cause false negatives for incoming Data packets. Because most of the false positives occur in the first BF structure, we finally propose the R-rCBF-PIT, in which the first rCBF is replaced with an rCBF with a signature field. The proposed PITs also provide an aging mechanism using a valid bit and a hit bit for entry expiration. Simulation results show that rCBF-PIT and R-rCBF-PIT both reduce the indeterminable rate by more than 81% compared with FBF-PIT. The results also show that R-rCBF-PIT resolves false negatives caused by incorrect deletions by including the signature fields in the first rCBF.

Keywords:

Faces

Information filters

Matched filters

Programming

Indexes

Hash functions

Filtering algorithms

Bloom filter

dynamic data structure

named data networking

pending Interest table