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Selective-Splitting and Cache-Maintenance Algorithms for Associative-Client Caches

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Abstract

We propose a number of selective-splitting and cache-maintenance algorithms to reduce the computational complexity of associative-client caches and network load. Our selective-splitting algorithms selectively split query-intersected semantic regions based on the relative region access-latency or relative region size in a semantic data caching and replacement model. Our cache-maintenance algorithms are set up for studying a variety of design issues in synchronizing associative-client caches. We analyzed the performance of our proposed algorithms in a network environment. Results from our study show that the selective-splitting algorithms reduce the number of splitting operations by 80% in most cases, and the avoidance-based maintenance algorithms outperform the detection-based maintenance algorithms not only in reducing the network traffic but also in rendering consistent performance under various experimental variances.

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Authors and Affiliations

  1. Tenfold Corp., Draper, UT, 84020

    Jiaxin J. Gao

  2. Comp. Sci. Dept., Brigham Young Univ., Provo, UT, 84602

    Dallan Quass & Yiu-Kai Ng

Authors
  1. Jiaxin J. Gao
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  2. Dallan Quass
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  3. Yiu-Kai Ng
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Gao, J.J., Quass, D. & Ng, YK. Selective-Splitting and Cache-Maintenance Algorithms for Associative-Client Caches. Distributed and Parallel Databases 16, 5–43 (2004). https://doi.org/10.1023/B:DAPD.0000026269.11225.f5

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  • DOI: https://doi.org/10.1023/B:DAPD.0000026269.11225.f5

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