We show experiments for on-line convergence to a global partitioning solution for sharing the database buffer pool, storage cache, and disk bandwidth in dif- ferent application configurations. We compare our ap- proach to two baseline approaches, which optimize ei- ther the memory partitioning, or the disk partitioning, as well as combinations of these approaches without global coordination. We show that for most application con- figurations, our computed model effectively prunes most of the search space, even without any additional tuning through experimental sampling. Our dynamic resource algorithmperforms similar to an experimental exhaustive search algorithm, but provides a solution within minutes, versus days of running time. At the same time, our global resource partitioning solution.
