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A causal consistency model based on grouping strategy

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Abstract

Data consistency has always been a significant topic in distributed systems. In the existing consistency models, causal consistency attracts more attention because it can meet high-performance requirements even when there are network partitions in the system. The synchronization method between replicas is one of the key indicators affecting the performance of causal consistency, especially when there are a large number of nodes in the system. In the case of deploying a large number of nodes in the system, this paper optimizes the synchronization mode between data centers and proposes a causal consistency model based on the grouping strategy (Gart). Gart manages all nodes in groups to reduce the management cost during data synchronization, and adopt a leader mechanism to improve the management efficiency of the system. At the same time, a client migration mechanism be introduced to ensure that throughput can be improved without sacrificing the remote update visibility. The simulation results demonstrate that compared with the existing causal consistency model, Gart can achieve better throughput when handling a large number of nodes, and with the same communication delay, it can achieve higher update visibility.

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Funding

Funding was provided by the National Natural Science Foundation of China (Grant No. 6180060654).

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Authors and Affiliations

  1. Hebei University, Baoding, China

    Junfeng Tian & Yanan Pang

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  1. Junfeng Tian
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  2. Yanan Pang
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Correspondence to Yanan Pang.

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Tian, J., Pang, Y. A causal consistency model based on grouping strategy. J Supercomput 78, 17736–17757 (2022). https://doi.org/10.1007/s11227-022-04441-3

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  • DOI: https://doi.org/10.1007/s11227-022-04441-3

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