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Popularity Cuckoo Filter: Always Keeping Popular Items in Mind

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Algorithms and Architectures for Parallel Processing (ICA3PP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14491))

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Abstract

A Bloom Filter is a basic and randomized means of storing information that can accurately determine membership status queries with no false negatives and a small probability of false positives. As its improvement, a Cuckoo Filter is a kind of new data structure which can support adding, removing items dynamically and achieving higher performance than a Bloom Filter. But current Cuckoo filters usually handle items assuming they have the same possibility to be queried, and treat them without difference, which is unable to satisfy the demand for querying that most popular items in dataset, such as in web caching.

We propose a new data structure called the popularity cuckoo filter that can make false positive smaller and prioritize storing members with higher popularity. Popularity cuckoo filters use different numbers of hash functions for items with different popularities, so they have better space efficiencies. Our experimental results show that the popularity cuckoo filter can distinguish items with large or small popularities well and suit datasets with irregular query patterns and non-uniform membership likelihood.

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Acknowledgement

This work is supported by National Natural Science Foundation of China under Grant No. 62002378, as well as partially funded by the Research Funding of NUDT under Grant ZK20-3.

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Authors and Affiliations

  1. School of Systems, National University of Defense Technology, Changsha, China

    Xuetan Cheng, Lailong Luo & Deke Guo

  2. School of Computer, National University of Defense Technology, Changsha, China

    Xiangrui Yang

  3. School of Computer Science and Engineering, Central South University, Changsha, China

    Wei Zou

Authors
  1. Xuetan Cheng
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  2. Lailong Luo
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  3. Wei Zou
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  4. Xiangrui Yang
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  5. Deke Guo
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Corresponding author

Correspondence to Lailong Luo .

Editor information

Editors and Affiliations

  1. Royal Melbourne Institute of Technology, Melbourne, VIC, Australia

    Zahir Tari

  2. Tianjin University, Tianjin, China

    Keqiu Li

  3. University of Arizona, Tucson, AZ, USA

    Hongyi Wu

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Cheng, X., Luo, L., Zou, W., Yang, X., Guo, D. (2024). Popularity Cuckoo Filter: Always Keeping Popular Items in Mind. In: Tari, Z., Li, K., Wu, H. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2023. Lecture Notes in Computer Science, vol 14491. Springer, Singapore. https://doi.org/10.1007/978-981-97-0808-6_25

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  • DOI: https://doi.org/10.1007/978-981-97-0808-6_25

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  • Print ISBN: 978-981-97-0807-9

  • Online ISBN: 978-981-97-0808-6

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