P-Store: An Elastic Database System with Predictive Provisioning

Abstract

OLTP database systems are a critical part of the operation of many enterprises. Such systems are often configured statically with sufficient capacity for peak load. For many OLTP applications, however, the maximum load is an order of magnitude larger than the minimum, and load varies in a repeating daily pattern. It is thus prudent to allocate computing resources dynamically to match demand. One can allocate resources reactively after a load increase is detected, but this places additional burden on the already-overloaded system to reconfigure. A predictive allocation, in advance of load increases, is clearly preferable. We present P-Store, the first elastic OLTP DBMS to use prediction, and apply it to the workload of B2W Digital (B2W), a large online retailer. Our study shows that P-Store outperforms a reactive system on B2W's workload by causing 72% fewer latency violations, and achieves performance comparable to static allocation for peak demand while using 50% fewer servers.