PGCE: A distributed storage causal consistency model based on partial geo-replication and cloud-edge collaboration architecture

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...

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Bibliographic Details
Published in:Computer networks (Amsterdam, Netherlands : 1999) Netherlands : 1999), 2022-07, Vol.212, p.109065, Article 109065
Main Authors: Junfeng, Tian, Wenqing, Bai, Haoyi, Jia
Format: Article
Language:English
Subjects:
Causal consistency
Cloud computing
Cloud-edge collaboration distributed network architecture
Collaboration
Computer architecture
Computer networks
Consistency
Distributed storage
Metadata
Network latency
Nodes
Partial geo-replication
Replication
Storage systems
Synchronism
Timestamp stability mechanism
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Summary: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.
ISSN:1389-1286
1872-7069
DOI:10.1016/j.comnet.2022.109065