isBF: Scalable in-packet bloom filter based multicast

Nikolaevskiy, Ilya; Lukyanenko, Andrey; Polishchuk, Tatiana; Polishchuk, Valentin; Gurtov, Andrei

doi:10.1016/j.comcom.2015.05.002

Cited by 20 publications

(12 citation statements)

References 10 publications

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“…Note that, due to the unavoidable hash conflicts (generating same hash value for diverse input elements), the membership query based on BF may incur false positive errors, i.e., wrongly indicating that a non-member element is a member of S. In Fig. 2, the query of x 5 returns positive because the bits at position 6, 8, and 11 are all 1, though x 5 S. Although BF incurs false positives, for many applications the space savings and constant locating time outweigh this drawback when the probability of false positive is small.…”

Section: A Framework Of Bloom Filtermentioning

confidence: 99%

“…Then the node forwards the packet to the neighbours which pass the membership test. Most-recently, endeavours have been made to improve the BF-enabled multicast routing in terms of scalability [5] [3] [43], loop mitigation [4], and flow leakage [3] [43]. Due to the limited bits in the header and the large number of nodes in the multicast tree, the in-packet BF incurs scalability to some extent.…”

Section: B Packet Routing and Forwardingmentioning

confidence: 99%

“…A possible solution is to split the multicast tree into multiple parts and represent them separately. Nikolavskiy et al propose to represent the multicast tree as multiple trees with their splitting algorithms [5]. By contrast, as stated in [43], the multicast tree can also be divided hierarchically so that nodes in each stage are represented by a BF.…”

Section: B Packet Routing and Forwardingmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

Luo

Guo

Richard

et al. 2019

IEEE Commun. Surv. Tutorials

174

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Bloom filter (BF) has been widely used to support membership query, i.e., to judge whether a given element x is a member of a given set S or not. Recent years have seen a flourish design explosion of BF due to its characteristic of space-efficiency and the functionality of constant-time membership query. The existing reviews or surveys mainly focus on the applications of BF, but fall short in covering the current trends, thereby lacking intrinsic understanding of their design philosophy. To this end, this survey provides an overview of BF and its variants, with an emphasis on the optimization techniques. Basically, we survey the existing variants from two dimensions, i.e., performance and generalization. To improve the performance, dozens of variants devote themselves to reducing the false positives and implementation costs. Besides, tens of variants generalize the BF framework in more scenarios by diversifying the input sets and enriching the output functionalities. To summarize the existing efforts, we conduct an in-depth study of the existing literature on BF optimization, covering more than 60 variants. We unearth the design philosophy of these variants and elaborate how the employed optimization techniques improve BF. Furthermore, comprehensive analysis and qualitative comparison are conducted from the perspectives of BF components. Lastly, we highlight the future trends of designing BFs. This is, to the best of our knowledge, the first survey that accomplishes such goals.

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Section: A Framework Of Bloom Filtermentioning

confidence: 99%

Section: B Packet Routing and Forwardingmentioning

confidence: 99%

Section: B Packet Routing and Forwardingmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

Luo

Guo

Richard

et al. 2019

IEEE Commun. Surv. Tutorials

174

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show abstract

“…На начальном этапе эксплуатации после развертывания можно оценить различные характеристики интенсивности информационных, служебных пакетов, времена отклика на запросы, частоты нераспо-знанных и пропущенных сообщений. В связи с этим один из возможных подходов выявления аномально-го поведения -использование данных, отражающих состояния системы, которые могут быть применены в статистическом анализе [9,10].…”

Section: постановка задачиunclassified

Analysis of statistical data from network infrastructure monitoring to detect abnormal behavior of system local segments

Бажаев¹,

Lebedev²,

Кривцова³

2017

Naučno-teh. vestn. inf. tehnol. meh. opt.

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“…Основываясь на особенностях функционирования беспроводных сенсорных сетей и применяя ре-комендуемые настройки, направленные на оптимизацию работы удаленного узла беспроводной сети, на-рушитель может реализовать атаку типа «широковещательный шторм» [9][10][11].…”

Section: постановка задачи исследованияunclassified