Predicting Replicated Database Scalability from Standalone Database Profiling

This paper develops analytical models to predict the throughput and the response time of a replicated database using measurements of the workload on a standalone database. These models allow workload scalability to be estimated before the replicated system is deployed, making the technique useful for capacity planning and dynamic service provisioning. The models capture the scalability limits stemming from update propagation and aborts for both multi-master and single-master replicated databases that support snapshot isolation. We validate the models by comparing their throughput and response time predictions against experimental measurements on two prototype replicated database systems running the TPC-W and RUBiS workloads. We show that the model predictions match the experimental results for both the multi-master and single-master designs and for the various workload mixes of TPC-W and RUBiS.