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  1. Proceedings of the 2014 International Workshop on Data Intensive Scalable Computing Systems (DISCS '14)
  2. Par-BF: a parallel partitioned Bloom filter for dynamic data sets
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Par-BF: a parallel partitioned Bloom filter for dynamic data sets
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Par-BF: a parallel partitioned Bloom filter for dynamic data sets

Content Provider ACM Digital Library
Author Liu, Yi Ge, Xiongzi Du, David H. C. Huang, Xiaoxia
Abstract Compared with a hash table, a Bloom Filter (BF) is more space-efficient for supporting fast matching though resulting in a controllable and acceptable false positive probability. The space size of the basic BF is predetermined based on the expected number of elements to be stored. However, we cannot predict the scale of a BF space for dynamic sets. The two existing solutions for supporting dynamic sets, Scalable BF (SBF) and Dynamic BF (DBF), still face some challenges on system performance and memory overhead. This paper presents a new BF for dynamic data sets, called Partitioned BF (Par-BF). Compared with DBF and SBF, the size and the range of the false positive probability can be calculated by a group of formulas to leverage a sweet spot between high-performance and low-overhead. Moreover, Par-BF supports parallel fast matching which can improve the overall throughput. From our trace-driven experimental results, the IOPS of Par-BF outperforms that of DBF and SBF from 6X to 10X, and from 2X to 4X, respectively. Meanwhile, through our proposed garbage collection policy, the memory overhead of Par-BF is less than half of the memory usage of SBF.
Starting Page 1
Ending Page 8
Page Count 8
File Format PDF
ISBN 9781479970384
DOI 10.1109/DISCS.2014.5
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2014-11-16
Access Restriction Subscribed
Subject Keyword Bloom filter Parallel Dynamic sets Par-bf
Content Type Text
Resource Type Article
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