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In this paper, we address the problem of transparently scaling out transactional (OLTP) workloads on relational databases, to support database-as-a-service in cloud computing environment. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. Capturing and modeling the transactional workload over a period of time, and then exploiting that information for data placement and replication has been shown to provide significant benefits in performance, both in terms of transaction latencies and overall throughput. However, such workload-aware data placement approaches can incur very high overheads, and further, may perform worse than naive approaches if the workload changes.
Quamar et al. (Mon,) studied this question.