Hunting the Pertinency of Bloom Filter in Computer Networking and Beyond: A Survey

Patgiri, Ripon; Nayak, Sabuzima; Borgohain, Samir Kumar

doi:10.1155/2019/2712417

Cited by 14 publications

(4 citation statements)

References 63 publications

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“…Many such structures were proposed in the context of network routing strategies, where Bloom filters are widely used to address packet filtering and forwarding, and the application scenario often requires the use of multiple sets [Geravand and Ahmadi 2013, Broder and Mitzenmacher 2003, Patgiri et al 2019. For instance, the longest prefix match solution proposed in [Dharmapurikar et al 2006] is one of the first strategies using the naive approach of one separate data structure for each set.…”

Section: Related Workmentioning

confidence: 99%

Bloom filter variants for multiple sets: a comparative assessment

Calderoni¹,

Maio²,

Palmieri³

2022

jucs

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In this paper we compare two probabilistic data structures for association queries derived from the well-known Bloom filter: the shifting Bloom filter (ShBF), and the spatial Bloom filter (SBF). With respect to the original data structure, both variants add the ability to store multiple subsets in the same filter, using different strategies. We analyse the performance of the two data structures with respect to false positive probability, and the inter-set error probability (the probability for an element in the set of being recognised as belonging to the wrong subset). As part of our analysis, we extended the functionality of the shifting Bloom filter, optimising the filter for any non-trivial number of subsets. We propose a new generalised ShBF definition with applications outside of our specific domain, and present new probability formulas. Results of the comparison show that the ShBF provides better space efficiency, but at a significantly higher computational cost than the SBF.

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Section: Related Workmentioning

confidence: 99%

Bloom filter variants for multiple sets: a comparative assessment

Calderoni¹,

Maio²,

Palmieri³

2022

jucs

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“…In P2P networks, the BF is beneficial in improving file sharing performance [26]. Chen et al [27] used a bloom filter in an overlay based on DHT global inverted indexes, and the BF reduced the unnecessary network load when performing multi-keyword searches in P2P networks.…”

Section: Bloom Filter-based Routing Approachmentioning

confidence: 99%

A Cuckoo Filter-Based Name Resolution and Routing Method in Information-Centric Networking

Lian

Li²,

Wang³

et al. 2022

Electronics

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Information-centric networking (ICN) is a new network architecture that routes content based on names to improve transmission performance. Therefore, the efficiency of name resolution and routing becomes a critical issue in ICN. The bloom filter-based routing scheme has gained significant attention for its ability to improve the memory efficiency of routing nodes in the network, but it cannot handle the movement or deletion of content and has a high false positive rate, which increases bandwidth consumption. In this paper, we propose a cuckoo filter-based name resolution and routing method where resolution requests are forwarded through a hierarchical network structure to the node closest to the content copy as much as possible to minimize latency. This method achieves reliable content removal and allows summaries of content to be exchanged between nodes for resolution error correction and information synchronization based on a modified cuckoo filter. The simulation results show that our method can effectively reduce the number of false positives, and it can reduce the additional overhead caused by processing false positives for a large-scale network by 50% compared with the bloom filter-based scheme.

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“…It has met a vast application domains, namely, Big Data [2], IoT [3], Computer Networking [4], Network Security and Privacy [5], [6], Biometrics [7], and Bioinformatics [8]. Counting Bloom Filter is useful in Computer Network, Network Security and Privacy [9]. Therefore, there are diverse Bloom Filters available to address the various problems of diverse domains [10]- [14].…”

Section: Introductionmentioning

confidence: 99%

countBF: A General-purpose High Accuracy and Space Efficient Counting Bloom Filter

Nayak¹,

Patgiri²

2021

Preprint

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Bloom Filter is a probabilistic data structure for the membership query, and it has been intensely experimented in various fields to reduce memory consumption and enhance a system's performance. Bloom Filter is classified into two key categories: counting Bloom Filter (CBF), and non-counting Bloom Filter. CBF has a higher false positive probability than standard Bloom Filter (SBF), i.e., CBF uses a higher memory footprint than SBF. But CBF can address the issue of the false negative probability. Notably, SBF is also false negative free, but it cannot support delete operations like CBF. To address these issues, we present a novel counting Bloom Filter based on SBF and 2D Bloom Filter, called countBF. countBF uses a modified murmur hash function to enhance its various requirements, which is experimentally evaluated. Our experimental results show that countBF uses 1.96× and 7.85× less memory than SBF and CBF respectively, while preserving lower false positive probability and execution time than both SBF and CBF. The overall accuracy of countBF is 99.999921, and it proves the superiority of countBF over SBF and CBF. Also, we compare with other state-of-the-art counting Bloom Filters.

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Hunting the Pertinency of Bloom Filter in Computer Networking and Beyond: A Survey

Cited by 14 publications

References 63 publications

Bloom filter variants for multiple sets: a comparative assessment

Bloom filter variants for multiple sets: a comparative assessment

A Cuckoo Filter-Based Name Resolution and Routing Method in Information-Centric Networking

countBF: A General-purpose High Accuracy and Space Efficient Counting Bloom Filter

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