Controlling View Divergence of Data Freshness in a Replicated Database System Using Statistical Update Delay Estimation

We propose a method of controlling the view divergence of data freshness when copies of sites in a replicated database are updated asynchronously. The view divergence of the replicated data freshness is the difference in the recentness of the updates reflected in the data acquired by clients. Our method accesses multiple sites and provides a client with data that reflects all the updates received by the sites. First, we define the probabilistic recentness of updates reflected in acquired data as read data freshness (RDF). The degree of RDF of data acquired by clients is the range of view divergence. Second, we propose a way to select sites in a replicated database by using the probability distribution of the update delays so that the data acquired by a client satisfies its required RDF. This way calculates the minimum number of sites in order to reduce the overhead of read transactions. Our method continues to adaptively and reliably provide data that meets the client’s requirements in an environment where the delay of update propagation varies and applications’ requirements change depending on the situation. Finally, we evaluate by simulation the view divergence we can control using our method. The simulation showed that our method can control the view divergence to about 1/4 that of a normal read transaction for 100 replicas. In addition, the increase in the overhead of a read transaction imposed by our method is not as much as the increase in the total number of replicas. key words: information dissemination, update delay distribution, nonparametric estimation, asynchronous update, lazy replication