Workload Detection and Continuous Automatic Bayesian Optimization in Database Management Systems

Abstract: The goal of this thesis has been to investigate the possibility of multi-workload optimization in Database Management Systems and workload detection. A system was successfully constructed to allow for multi-workload testing and data aggregation. The performance gain when optimizing using this project did not seem to match the optimizing performance obtained during testing within the single-workload framework. To test workload detection, data was collected for the benchmarks TPC-C, CH-benchmark and Wikipedia for two different types of metrics. The first was hardware-based metrics which was tested using the change detection technique CUSUM. It was found that hardware-metrics excelled in separating data for the chosen workloads in non-optimizing circumstances, and in optimizing situations it was found to be too unreliable. The second type consisted of the query types that were executed by the Database Management System. When tested with the DBSCAN clustering method all data points were clustered correctly.