Adjoin: A causal consistency model based on the adjacency list in a distributed system

摘要

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.