Computer Networks has published an article written by Tian Junfeng, Bai Wenqing, and Jia Haoyi, Hebei University School of Cyber Security and Computer, Baoding 071000, China, and Hebei Key Laboratory of High Confidence Information Systems (Hebei University), Baoding 071000, China.
Abstract: “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.“