The current causal consistency model has trouble facing the high synchronization overheads and response delays found in cloud storage systems. This paper proposes a causal consistency model for distributed storage based on partial geographical replication and Cloud-Edge collaboration structure (PGCE). The model is based on the distributed network architecture of Cloud-Edge collaboration, and the cloud dataset is divided into multiple subsets by a hash function to store these subsets in the edge nodes that are close to the user network to realize partial geo-replication. At the same time, the timestamp stabilization mechanism and metadata processing service are set up to implement data consistency between nodes on the premise of ensuring causality, reducing the overhead of metadata processing and data synchronization. The client interacts directly with the edge nodes, which reduces the response delay of interacts with the cloud DC. An evaluation of the PGCE compared with existing models shows that it has better performance in response latency and throughput.

At present, most of the causal consistency models that rely on cloud storage have problems such as high operation delays and large metadata overhead. To solve these problems, this paper proposes a causal consistency model for edge storage based on hash rings, CCESHP. The proposed model uses two hashes to map the keys and servers on the hash ring for grouping and stores a subset of the complete data set in a replica node located at the edge of the network, thereby realizing a partial geographic replication strategy in the edge storage environment. Operation latency will be reduced since the edge replica is closer to the client. At the same time, it also generates and maintains a combined timestamp to capture causality according to the update type, which can keep the amount of managed metadata in a relatively stable and low state, reduce the overhead of system management metadata, and improve system throughput. The experimental evaluation results under different workloads show that the model has better performance in throughput and operation delay when compared with the existing causal consistency model.

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.