Causal consistency model for distributed data store based on shared graph and partial replication strategy

Abstract

In order to solve the problem of metadata propagation overhead,operation delay and remote update visibility latency in the current causal consistency model,a causal consistency model for distributed data stores based on the shared graph and partial replication strategy was proposed.This model was based on the topology of the shared graph,and each data center stored an arbitrary subset of the data.At the same time,the global stabilization strategy combining shared stable vector and hybrid logical clocks was proposed to provide data consistency guarantees on the premise of ensuring causality.The theoretical analysis and experimental results show that the proposed model can effectively balance the remote update visibility and the metadata overhead compared with the existing models while reducing the operation delay.

Keywords

• data consistency;causal consistency;shared graph;partial replication strategy;global stabilization strategy