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Horae: causal consistency model based on hot data governance

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Abstract

Causal consistency has attracted considerable attention in distributed systems because it meets the high availability and high-performance requirements in the presence of network partitions. Existing causal consistency models seldom pay attention to hot data governance and run the data stabilization process periodically and thus fail to meet the user requirements of real-time data and high concurrency. In response to this problem, this study proposes a model based on thermal data governance, the Horae model, which simplifies causal sequence verification by sorting, accelerates data stability, and optimizes model update visibility and read response latency. Furthermore, the Horae model stores hot data, reduces the number of partition loads, increases operation parallelism, and ultimately improves throughput. Theoretical analysis and simulation experiments show that the proposed model outperforms existing models in terms of throughput, read response time, and update visibility.

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Acknowledgements

The authors of the paper express their gratitude to AJE for providing language assistance on this work.

Funding

This work was supported by the National Natural Science Foundation of China (61802106), the Natural Science Foundation of Hebei Province (F2016201244) and the Social Science Foundation of Hebei Province (HB18SH002).

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Authors and Affiliations

  1. College of Computer and Cyberspace Security, Hebei University, Baoding, 071002, China

    Junfeng Tian & Qianyu Yang

  2. Key Laboratory of High Trusted Information System of Hebei Province, Hebei University, Baoding, 071002, China

    Junfeng Tian & Qianyu Yang

Authors
  1. Junfeng Tian
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  2. Qianyu Yang
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Contributions

JT designed the study and performed the experiments; QY performed the experiments, analyzed the data, and wrote the manuscript.

Corresponding author

Correspondence to Qianyu Yang.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Tian, J., Yang, Q. Horae: causal consistency model based on hot data governance. J Supercomput 78, 4574–4599 (2022). https://doi.org/10.1007/s11227-021-04030-w

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  • DOI: https://doi.org/10.1007/s11227-021-04030-w

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