ID Bloom Filter: Achieving Faster Multi-Set Membership Query in Network Applications

被引:0
|
作者
Liu, Peng [1 ]
Wang, Hao [1 ]
Gao, Siang [1 ]
Yang, Tong [1 ,2 ]
Zou, Lei [1 ]
Uden, Lorna [3 ]
Li, Xiaoming [1 ]
机构
[1] Peking Univ, Beijing, Peoples R China
[2] NUDT, Collaborat Innovat Ctr High Performance Comp, Changsha, Peoples R China
[3] Staffordshire Univ, Sch Comp, Stoke On Trent, Staffs, England
关键词
FRAMEWORK;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The problem of multi-set membership query plays a significant role in many network applications, including routers and firewalls. Answering multi-set membership query means telling whether an element belongs to the multi-set, and if yes, which particular set it belongs to. Most traditional solutions for multi-set membership query are based on Bloom filters. However, these solutions cannot achieve high accuracy and high speed at the same time when the memory is tight. To address this issue, this paper presents the ID Bloom Filter (IBF) and ID Bloom Filter with ones' Complement (IBFC). The key technique in IBF is mapping each element to k positions in a filter and directly recording its set ID at these positions. It has a small memory usage as well as a high processing speed. To achieve higher accuracy, we propose IBFC that records the set ID and its ones' complement together. The experimental results show that our IBF and IBFC are faster than the state-of-the-art while achieving a high accuracy.
引用
收藏
页数:6
相关论文
共 38 条
  • [1] Difference Bloom Filter: a Probabilistic Structure for Multi-set Membership Query
    Yang, Dongsheng
    Tian, Deyu
    Gong, Junzhi
    Gao, Siang
    Yang, Tong
    Li, Xiaoming
    [J]. 2017 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2017,
  • [2] BhBF: A Bloom Filter Using Bh Sequences for Multi-set Membership Query
    Pei, Shuyu
    Xie, Kun
    Wang, Xin
    Xie, Gaogang
    Li, Kenli
    Li, Wei
    Li, Yanbiao
    Wen, Jigang
    [J]. ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, 2022, 16 (05)
  • [3] Bloom Filter With Noisy Coding Framework for Multi-Set Membership Testing
    Dai, Haipeng
    Yu, Jun
    Li, Meng
    Wang, Wei
    Liu, Alex X.
    Ma, Jinghao
    Qi, Lianyong
    Chen, Guihai
    [J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2023, 35 (07) : 6710 - 6724
  • [4] When Bloom Filters Are No Longer Compact: Multi-Set Membership Lookup for Network Applications
    Qiao, Yan
    Chen, Shigang
    Mo, Zhen
    Yoon, Myungkeun
    [J]. IEEE-ACM TRANSACTIONS ON NETWORKING, 2016, 24 (06) : 3326 - 3339
  • [5] Noisy Bloom Filters for Multi-Set Membership Testing
    Dai, Haipeng
    Zhong, Yuankun
    Liu, Alex X.
    Wang, Wei
    Li, Meng
    [J]. SIGMETRICS/PERFORMANCE 2016: PROCEEDINGS OF THE SIGMETRICS/PERFORMANCE JOINT INTERNATIONAL CONFERENCE ON MEASUREMENT AND MODELING OF COMPUTER SCIENCE, 2016, : 139 - 151
  • [6] Approaches for Improving Bloom Filter-Based Set Membership Query
    Lee, HyunYong
    Lee, Byung-Tak
    [J]. JOURNAL OF INFORMATION PROCESSING SYSTEMS, 2019, 15 (03): : 550 - 569
  • [7] Locality-Sensitive Bloom Filter for Approximate Membership Query
    Hua, Yu
    Xiao, Bin
    Veeravalli, Bharadwaj
    Feng, Dan
    [J]. IEEE TRANSACTIONS ON COMPUTERS, 2012, 61 (06) : 817 - 830
  • [8] Two-tier Bloom filter to achieve faster membership testing
    Jimeno, M.
    Christensen, K. J.
    Roginsky, A.
    [J]. ELECTRONICS LETTERS, 2008, 44 (07) : 503 - 504
  • [9] Building Fast and Compact Sketches for Approximately Multi-Set Multi-Membership Querying
    Li, Rundong
    Wang, Pinghui
    Zhu, Jiongli
    Zhao, Junzhou
    Di, Jia
    Yang, Xiaofei
    Ye, Kai
    [J]. SIGMOD '21: PROCEEDINGS OF THE 2021 INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA, 2021, : 1077 - 1089
  • [10] Multiclass Membership Determination Integrating an ID-Bound Method with Bloom Filter
    Chen, Lu-, I
    Ma, Heng
    [J]. INFORMATION TECHNOLOGY AND CONTROL, 2020, 49 (02): : 197 - 205