Set Reconciliation via Counting Bloom Filters

2013 ◽  
Vol 25 (10) ◽  
pp. 2367-2380 ◽  
Author(s):  
Deke Guo ◽  
Mo Li
Keyword(s):  
Bloom Filters ◽  
Set Reconciliation ◽  
Counting Bloom Filters

Accelerating packet classification with counting bloom filters for virtual OpenFlow switching

2018 ◽  
Vol 15 (10) ◽  
pp. 117-128 ◽  
Author(s):  
Jinyuan Zhao ◽  
Zhigang Hu ◽  
Bing Xiong ◽  
Keqin Li
Keyword(s):  
Packet Classification ◽  
Bloom Filters ◽  
Counting Bloom Filters

Implementing error detection in fast counting Bloom filters

2014 ◽  
Vol 50 (22) ◽  
pp. 1602-1604 ◽  
Author(s):  
P. Reviriego ◽  
J.A. Maestro
Keyword(s):  
Error Detection ◽  
Bloom Filters ◽  
Counting Bloom Filters

scholarly journals Comparing set reconciliation methods based on bloom filters and their variants

2016 ◽  
Vol 21 (2) ◽  
pp. 157-167
Author(s):  
Zhiyao Hu ◽  
Xiaoqiang Teng ◽  
Deke Guo ◽  
Bangbang Ren ◽  
Pin Lv ◽  
...  
Keyword(s):  
Bloom Filters ◽  
Set Reconciliation

scholarly journals Space- and computationally-efficient set reconciliation via parity bitmap sketch (PBS)

2020 ◽  
Vol 14 (4) ◽  
pp. 458-470
Author(s):  
Long Gong ◽  
Ziheng Liu ◽  
Liang Liu ◽  
Jun Xu ◽  
Mitsunori Ogihara ◽  
...  
Keyword(s):  
Computational Complexity ◽  
Performance Metrics ◽  
Optimal Parameter ◽  
Parameter Tuning ◽  
Bloom Filters ◽  
Communication Overhead ◽  
Algorithmic Problem ◽  
Low Computational Complexity ◽  
Theoretical Minimum ◽  
Set Reconciliation

Set reconciliation is a fundamental algorithmic problem that arises in many networking, system, and database applications. In this problem, two large sets A and B of objects (bitcoins, files, records, etc.) are stored respectively at two different network-connected hosts, which we name Alice and Bob respectively. Alice and Bob communicate with each other to learn A Δ B , the difference between A and B , and as a result the reconciled set A ∪ B. Current set reconciliation schemes are based on either invertible Bloom filters (IBF) or error-correction codes (ECC). The former has a low computational complexity of O(d) , where d is the cardinality of A Δ B , but has a high communication overhead that is several times larger than the theoretical minimum. The latter has a low communication overhead close to the theoretical minimum, but has a much higher computational complexity of O(d 2 ). In this work, we propose Parity Bitmap Sketch (PBS), an ECC-based set reconciliation scheme that gets the better of both worlds: PBS has both a low computational complexity of O(d) just like IBF-based solutions and a low communication overhead of roughly twice the theoretical minimum. A separate contribution of this work is a novel rigorous analytical framework that can be used for the precise calculation of various performance metrics and for the near-optimal parameter tuning of PBS.

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