Automatic Database Troubleshooting of Azure SQL Databases

Exponentially growing number of workloads has been transferred from on-premises to the cloud environment during last decade. It incurs a constantly increasing load on the monitoring and troubleshooting capabilities of the platforms that host those applications nowadays. Data mining techniques and use of telemetry data have recently become an unavoidable means for tracking the behavior of cloud services. The research effort elaborated in this paper is focused on exploiting real-world data to build an automatic database troubleshooting system that exploits the combination of the more comprehensive statistical data science models and an expert system to perform the root cause analysis. An extensive evaluation study was conducted during the eight-month period with a plethora of Azure SQL production workloads for a varying number of databases. The obtained results confirmed the viability and cost-effectiveness of such an approach at the scale of the cloud.