Tuning Database Configuration Parameters with iTuned

Database systems have a large number of configuration parameters that control memory distribution, I/O optimization, costing of query plans, parallelism, many aspects of logging, recovery, and other behavior. Regular users and even expert database administrators struggle to tune these parameters for good performance. The wave of research on improving database manageability has largely overlooked this problem which turns out to be hard to solve. We describe iTuned, a tool that automates the task of identifying good settings for database configuration parameters. iTuned has three novel features: (i) a technique called Adaptive Sampling that proactively brings in appropriate data through planned experiments to find high-impact parameters and high-performance parameter settings, (ii) an executor that supports online experiments in production database environments through a cycle-stealing paradigm that places near-zero overhead on the production workload; and (iii) portability across different database systems. We show the effectiveness of iTuned through an extensive evaluation based on different types of workloads, database systems, and usage scenarios.