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.

scholarly journals Analysis of Counting Bloom Filters Used for Count Thresholding

Electronics ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 779
Author(s):  
Kibeom Kim ◽  
Yongjo Jeong ◽  
Youngjoo Lee ◽  
Sunggu Lee
Keyword(s):  
Large Data ◽  
Bloom Filter ◽  
Bloom Filters ◽  
Data Sets ◽  
Approximate Counting ◽  
Data Set ◽  
Typical Parameter ◽  
Parameter Values ◽  
Counting Bloom Filter ◽  
Counting Bloom Filters

A bloom filter is an extremely useful tool applicable to various fields of electronics and computers; it enables highly efficient search of extremely large data sets with no false negatives but a possibly small number of false positives. A counting bloom filter is a variant of a bloom filter that is typically used to permit deletions as well as additions of elements to a target data set. However, it is also sometimes useful to use a counting bloom filter as an approximate counting mechanism that can be used, for example, to determine when a specific web page has been referenced more than a specific number of times or when a memory address is a “hot” address. This paper derives, for the first time, highly accurate approximate false positive probabilities and optimal numbers of hash functions for counting bloom filters used in count thresholding applications. The analysis is confirmed by comparisons to existing theoretical results, which show an error, with respect to exact analysis, of less than 0.48% for typical parameter values.

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

Parloom: A New Low-Power Set-Associative Instruction Cache Architecture Utilizing Enhanced Counting Bloom Filter and Partial Tags

2019 ◽  
Vol 28 (12) ◽  
pp. 1950203
Author(s):  
Sajjad Rostami-Sani ◽  
Mojtaba Valinataj ◽  
Saeideh Alinezhad Chamazcoti
Keyword(s):  
Energy Consumption ◽  
System Performance ◽  
Bloom Filter ◽  
Instruction Cache ◽  
Cache Architecture ◽  
Power Set ◽  
Novel Method ◽  
Instruction Caches ◽  
Cache System ◽  
Counting Bloom Filter

The cache system dissipates a significant amount of energy compared to the other memory components. This will be intensified if a cache is designed with a set-associative structure to improve the system performance because the parallel accesses to the entries of a set for tag comparisons lead to even more energy consumption. In this paper, a novel method is proposed as a combination of a counting Bloom filter and partial tags to mitigate the energy consumption of set-associative caches. This new hybrid method noticeably decreases the cache energy consumption especially in highly-associative instruction caches. In fact, it uses an enhanced counting Bloom filter to predict cache misses with a high accuracy as well as partial tags to decrease the overall cache size. This way, unnecessary tag comparisons can be prevented and therefore, the cache energy consumption is considerably reduced. Based on the simulation results, the proposed method provides the energy reduction from 22% to 31% for 4-way–32-way set-associative L1 caches bigger than 16[Formula: see text]kB running the MiBench programs. The improvements are attained with a negligible system performance degradation compared to the traditional cache system.

FMPC: A Fast Multi-Dimensional Packet Classification Algorithm

2014 ◽  
Vol 644-650 ◽  
pp. 3365-3370
Author(s):  
Zhen Hong Guo ◽  
Lin Li ◽  
Qing Wang ◽  
Meng Lin ◽  
Rui Pan
Keyword(s):  
Rapid Development ◽  
Hash Table ◽  
Bloom Filter ◽  
Packet Classification ◽  
Binary Search ◽  
Bloom Filters ◽  
Algorithm Analysis ◽  
Hash Tables ◽  
Worst Case ◽  
Embedded Sram

With the rapid development of the Internet, the number of firewall rules is increasing. The enormous quantity of rules challenges the performance of the packet classification that has already become a bottleneck in firewalls. This dissertation proposes a rapid and multi-dimensional algorithm for packet classification based on BSOL(Binary Search On Leaves), which is named FMPC(FastMulti-dimensional Packet Classification). Different from BSOL, FMPC cuts all dimensions at the same time to decompose rule spaces and stores leaf spaces into hash tables; FMPC constructs a Bloom Filter for every hash table and stores them into embedded SRAM. When classifying a packet, FMPC performs parallel queries on Bloom Filters and determines how to visit hash tables according to the results. Algorithm analysis and the result of simulations show: the average number of hash-table lookups of FMPC is 1 when classifying a packet, which is much smaller than that of BSOL; inthe worst case, the number of hash-table lookups of FMPCisO(logwmax+1⁡), which is also smaller than that of BSOL in multi-dimensional environment, where wmax is the length, in bits, of the dimension whose length is the longest..

The Variable-Increment Counting Bloom Filter

2014 ◽  
Vol 22 (4) ◽  
pp. 1092-1105 ◽  
Author(s):  
Ori Rottenstreich ◽  
Yossi Kanizo ◽  
Isaac Keslassy
Keyword(s):  
Bloom Filter ◽  
Counting Bloom Filter

Improving counting Bloom filter performance with fingerprints

2016 ◽  
Vol 116 (4) ◽  
pp. 304-309 ◽  
Author(s):  
Salvatore Pontarelli ◽  
Pedro Reviriego ◽  
Juan Antonio Maestro
Keyword(s):  
Bloom Filter ◽  
Filter Performance ◽  
Counting Bloom Filter

scholarly journals A Novel Hardware Security Architecture: PD-CRP(PUF Database & Challenge-Response Pair) Bloom Filter on Memristor Based PUF

2020 ◽  
Author(s):  
Jungwon Lee ◽  
Seoyeon Choi ◽  
Dayoung Kim ◽  
Yunyoung Choi ◽  
Wookyung Sun
Keyword(s):  
Data Transmission ◽  
Hardware Security ◽  
Bloom Filter ◽  
Transmission Error ◽  
Bloom Filters ◽  
Search Performance ◽  
Security Technology ◽  
Security Environment ◽  
Filter Size ◽  
Simulation Results

Because the development of the internet of things (IoT) requires technology that transfers information between objects without human intervention, the core of IoT security will be secure authentication between devices or between devices and servers. Software-based authentication may be a security vulnerability in IoT, but hardware-based security technology can provide a strong security environment. A physical unclonable functions (PUFs) are a hardware security element suitable for lightweight applications. PUFs can generate challenge-response pairs(CRPs) that cannot be controlled or predicted by utilizing inherent physical variations that occur in the manufacturing process. In particular, pulse width memristive PUF (PWM-PUF) improves security performance by applying different write pulse widths and bank structures. Bloom filter (BF) is probabilistic data structures that answer membership queries using small memories. Bloom filter can improve search performance and reduce memory usage and are used in areas such as networking, security, big data, and IoT. In this paper, we propose a structure that applies Bloom filters based on the PWM-PUF to reduce PUF data transmission errors. The proposed structure uses two different Bloom filter types that store different information and that are located in front of and behind the PWM-PUF, improving security by removing challenges from attacker access. Simulation results show that the proposed structure decreases the data transmission error rate and reuse rate as the Bloom filter size increases, the simulation results also show that the proposed structure improves PWM-PUF security with a very small Bloom filter memory.

Multi-Granularities Counting Bloom Filter

2006 ◽  
pp. 542-551
Author(s):  
Zhou Mingzhong ◽  
Gong Jian ◽  
Ding Wei ◽  
Cheng Guang
Keyword(s):  
Bloom Filter ◽  
Counting Bloom Filter
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