Fast and deterministic hash table lookup using discriminative bloom filters

2013 ◽  
Vol 36 (2) ◽  
pp. 657-666 ◽  
Author(s):  
Kun Huang ◽  
Gaogang Xie ◽  
Rui Li ◽  
Shuai Xiong
Keyword(s):  
Hash Table ◽  
Bloom Filters ◽  
Table Lookup

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..

Efficient FTL-Aware Data Categorization and Identification Scheme for Flash Memory

2015 ◽  
Vol 24 (08) ◽  
pp. 1550113 ◽  
Author(s):  
Sololia Gudeta Ayele ◽  
Rize Jin ◽  
Se Jin Kwon ◽  
Muhammad Attique ◽  
Tae-Sung Chung
Keyword(s):  
Flash Memory ◽  
False Positive ◽  
Hash Table ◽  
Bloom Filters ◽  
False Identification ◽  
Address Space ◽  
Memory Consumption ◽  
Block Number ◽  
Definition Of ◽  
Memory Utilization

Hot data identification in flash memory is of great interest because it significantly affects the garbage collection and wear-leveling performance. Presently, certain hot and cold data classification schemes based on Bloom filters (BFs) have been proposed. Although BFs are efficient in most cases, there is a significant trade-off between false positive rates, which are the result of hash value collisions and memory utilization. In this paper, we suggest a better data categorization mechanism that is based on a hashing technique called Fingerprinting by Random Polynomials with the aim of reducing false positive rates and achieving lower memory consumption compared to the BF-based schemes. We also introduce a new methodology for classifying write requests by linking the definition of hot and cold write requests to the flash memory software layer, the flash translation layer (FTL) characteristics. Our approach improves space utilization by representing each logical block number (lbn) by one counter in the hash table, and achieves an extremely low error rate by choosing the degree of the hash function based on the address space of the flash memory. In addition, we achieved lower false identification rates. We demonstrate the performance using mathematical analysis and trace-driven simulation.

Fast hash table lookup using extended bloom filter

2005 ◽  
Vol 35 (4) ◽  
pp. 181-192 ◽  
Author(s):  
Haoyu Song ◽  
Sarang Dharmapurikar ◽  
Jonathan Turner ◽  
John Lockwood
Keyword(s):  
Hash Table ◽  
Bloom Filter ◽  
Table Lookup

scholarly journals Improving on hash-based probabilistic sequence classification using multiple spaced seeds and multi-index Bloom filters

2018 ◽  
Author(s):  
Justin Chu ◽  
Hamid Mohamadi ◽  
Emre Erhan ◽  
Jeffery Tse ◽  
Readman Chiu ◽  
...  
Keyword(s):  
Hash Table ◽  
Bloom Filter ◽  
Bloom Filters ◽  
Sequencing Data ◽  
Multi Index ◽  
Sequencing Errors ◽  
Spaced Seeds ◽  
The Face ◽  
Order Of Magnitude ◽  
Higher Sensitivity

ABSTRACTAlignment-free classification of sequences against collections of sequences has enabled high-throughput processing of sequencing data in many bioinformatics analysis pipelines. Originally hash-table based, much work has been done to improve and reduce the memory requirement of indexing of k-mer sequences with probabilistic indexing strategies. These efforts have led to lower memory highly efficient indexes, but often lack sensitivity in the face of sequencing errors or polymorphism because they are k-mer based. To address this, we designed a new memory efficient data structure that can tolerate mismatches using multiple spaced seeds, called a multi-index Bloom Filter. Implemented as part of BioBloom Tools, we demonstrate our algorithm in two applications, read binning for targeted assembly and taxonomic read assignment. Our tool shows a higher sensitivity and specificity for read-binning than BWA MEM at an order of magnitude less time. For taxonomic classification, we show higher sensitivity than CLARK-S at an order of magnitude less time while using half the memory.

Two-stage IPv6 route search algorithm based on perfect-Hash table and multibit-trie

2013 ◽  
Vol 33 (5) ◽  
pp. 1194-1196
Author(s):  
Fei DU ◽  
Zhiguo DONG ◽  
Lin MIAO ◽  
Yupeng TUO
Keyword(s):  
Search Algorithm ◽  
Hash Table ◽  
Two Stage ◽  
Route Search
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