Moneyball

Microsoft Azure SQL Database is among the leading relational database service providers in the cloud. Serverless compute automatically scales resources based on workload demand. When a database becomes idle its resources are reclaimed. When activity returns, resources are resumed. Customers pay only for resources they used. However, scaling is currently merely reactive, not proactive, according to customers' workloads. Therefore, resources may not be immediately available when a customer comes back online after a prolonged idle period. In this work, we focus on reducing this delay in resource availability by predicting the pause/resume patterns and proactively resuming resources for each database. Furthermore, we avoid taking away resources for short idle periods to relieve the back-end from ineffective pause/resume workflows. Results of this study are currently being used worldwide to find the middle ground between quality of service and cost of operation.