Goals and benchmarks for autonomic configuration recommenders

Abstract

We are witnessing an explosive increase in the complexity of the information systems we rely upon. Autonomic systems address this challenge by continuously configuring and tuning themselves. Recently, a number of autonomic features have been incorporated into commercial RDBMS; tools for recommending database configurations (i.e., indexes, materialized views, partitions) for a given workload are prominent examples of this promising trend. In this paper, we introduce a flexible characterization of the performance goals of configuration recommenders and develop an experimental evaluation approach to benchmark the effectiveness of these autonomic tools. We focus on exploratory queries and present extensive experimental results using both real and synthetic data that demonstrate the validity of the approach introduced. Our results identify a specific index configuration based on single-column indexes as a very useful baseline for comparisons in the exploratory setting. Furthermore, the experimental results demonstrate the unfulfilled potential for achieving improvements of several orders of magnitude. Copyright 2005 ACM.