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

scholarly journals A Cache Architecture for Counting Bloom Filters: Theory and Application

2011 ◽  
Vol 2011 ◽  
pp. 1-10
Author(s):  
Mahmood Ahmadi ◽  
Stephan Wong
Keyword(s):  
Mathematical Analysis ◽  
Bloom Filter ◽  
Packet Classification ◽  
Bloom Filters ◽  
Memory Hierarchies ◽  
Cache Architecture ◽  
Multilevel Memory ◽  
Memory Accesses ◽  
Counting Bloom Filter ◽  
Counting Bloom Filters

Within packet processing systems, lengthy memory accesses greatly reduce performance. To overcome this limitation, network processors utilize many different techniques, for example, utilizing multilevel memory hierarchies, special hardware architectures, and hardware threading. In this paper, we introduce a multilevel memory architecture for counting Bloom filters. Based on the probabilities of incrementing of the counters in the counting Bloom filter, a multi-level cache architecture called the cached counting Bloom filter (CCBF) is presented, where each cache level stores the items with the same counters. To test the CCBF architecture, we implement a software packet classifier that utilizes basic tuple space search using a 3-level CCBF. The results of mathematical analysis and implementation of the CCBF for packet classification show that the proposed cache architecture decreases the number of memory accesses when compared to a standard Bloom filter. Based on the mathematical analysis of CCBF, the number of accesses is decreased by at least 53%. The implementation results of the software packet classifier are at most 7.8% (3.5% in average) less than corresponding mathematical analysis results. This difference is due to some parameters in the packet classification application such as number of tuples, distribution of rules through the tuples, and utilized hashing functions.

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

Algebra Operations on Counting Bloom Filters

2012 ◽  
Vol 35 (12) ◽  
pp. 2598
Author(s):  
Xiao-Mei TIAN ◽  
Da-Fang ZHANG ◽  
Kun XIE ◽  
Chang-Qiong SHI ◽  
Xiao-Bo YANG
Keyword(s):  
Bloom Filters ◽  
Counting Bloom Filters

scholarly journals Accurate Counting Bloom Filters for Large-Scale Data Processing

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Wei Li ◽  
Kun Huang ◽  
Dafang Zhang ◽  
Zheng Qin
Keyword(s):  
Data Processing ◽  
False Positive ◽  
Large Scale ◽  
Bloom Filters ◽  
Positive Probability ◽  
Large Scale Data ◽  
Large Scale Data Processing ◽  
False Positive Probability ◽  
Scale Data ◽  
Counting Bloom Filters

Bloom filters are space-efficient randomized data structures for fast membership queries, allowing false positives. Counting Bloom Filters (CBFs) perform the same operations on dynamic sets that can be updated via insertions and deletions. CBFs have been extensively used in MapReduce to accelerate large-scale data processing on large clusters by reducing the volume of datasets. The false positive probability of CBF should be made as low as possible for filtering out more redundant datasets. In this paper, we propose a multilevel optimization approach to building an Accurate Counting Bloom Filter (ACBF) for reducing the false positive probability. ACBF is constructed by partitioning the counter vector into multiple levels. We propose an optimized ACBF by maximizing the first level size, in order to minimize the false positive probability while maintaining the same functionality as CBF. Simulation results show that the optimized ACBF reduces the false positive probability by up to 98.4% at the same memory consumption compared to CBF. We also implement ACBFs in MapReduce to speed up the reduce-side join. Experiments on realistic datasets show that ACBF reduces the false positive probability by 72.3% as well as the map outputs by 33.9% and improves the join execution times by 20% compared to CBF.

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