<![CDATA[Distributed storage systems are a fundamental component of large-scale internet services. To meet the in-creasing needs of users regarding availability and latency, the design of data storage systems has developed into data replication techniques, one of which is geo-replication. Causal consistency is an attractive method for storing geo-replicated data because it is at the crucial point between ease of programming and resulting performance. This method also enables high availability and low latency. However, when implemented into cloud storage, there are limitations regarding throughput and costs. We surveyed several models using methods related to causal consistency in geo-replication cases designed by previous researchers. The mod-els used were derived from papers on causal consistency in geo-replication cases published within the last five years. In this study, we compared the performance of previously designed models based on their performance results. The results of this study are grouping models based on throughput and latency performance obtained.]]>
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