Data consistency is a critical topic in distributed systems. In existing consistency models, causal consistency has attracted a significant amount of attention because it can satisfy high‐performance requirements even in the presence of network partitions. At present, most of the causal consistency models face a tradeoff between throughput and update visibility. Simultaneously, they cannot take full advantage of partial geo‐replication. To resolve the problems, this paper proposes a causal consistency model that supports partial replication using the adjacency list, called Adjoin. In Adjoin, each data center (DC) stores only a subset of the full data, by reading adjacency relationships, and the relevant nodes quickly reach synchronization. We also introduce the Adjacency Stable Vector and Adjacency Dependency Set to capture causality, which reduces the system storage overhead. We evaluate Adjoin with different workloads on a cloud platform using multiple sites. The results show that Adjoin has good performance in terms of throughput and update visibility compared with previous causal consistency models.

中文翻译：

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Adjoin：分布式系统中基于邻接表的因果一致性模型

数据一致性是分布式系统中的一个关键主题。在现有的一致性模型中，因果一致性引起了大量关注，因为它即使在存在网络分区的情况下也能满足高性能要求。目前，大多数因果一致性模型都面临吞吐量和更新可见性之间的权衡。同时，他们无法充分利用部分地理复制。针对上述问题，本文提出了一种支持使用邻接表进行部分复制的因果一致性模型，称为Adjoin。在 Adjoin 中，每个数据中心（DC）只存储完整数据的一个子集，通过读取邻接关系，相关节点快速达到同步。我们还引入了 Adjacency Stable Vector 和 Adjacency Dependency Set 来捕捉因果关系，这减少了系统存储开销。我们在使用多个站点的云平台上评估具有不同工作负载的 Adjoin。结果表明，与之前的因果一致性模型相比，Adjoin 在吞吐量和更新可见性方面具有良好的性能。