CCECGP: causal consistency model of edge–cloud collaborative based on grouping protocol

At present, most causal consistency models based on cloud storage can no longer meet the needs of delay-sensitive applications. Moreover, the overhead of data synchronization between replicas is too high. This paper proposes a causal consistency model of edge–cloud collaborative based on grouping protocol. The model is based on the edge–cloud collaboration architecture, partitions cloud data centers and groups edge nodes by distributed hash tables, and stores a subset of the complete data set in nodes located at the edge of the network, thereby realizing partial geo-replication in edge–cloud collaboration environment. At the same time, we design a group synchronization algorithm called Imp_Paxos, so that the update only needs to be synchronized to the main group, which reduces the visibility delay of the update and decreases the data synchronization overhead. Besides, a sort timestamp is proposed in this paper, which generates different timestamps according to the type of update to track causality, keeping the amount of metadata managed in a relatively stable and low state. Therefore, the proposed model reduces the overhead of metadata for system management and improves throughput quantity of system. Experiments show that our model performs well in terms of throughput, operation latency, and update visibility latency compared with existing causal consistency models.