Adaptive Self-Tuning Memory in DB2
Adam J. Storm, Christian Garcia-Arellano, et al.
VLDB 2006
Optimizing configuration parameters for achieving service level objectives is time-consuming and skills-intensive. This paper proposes a generic approach to automating this task. By generic, we mean that the approach is relatively independent of the target system for which the optimization is done. Our approach uses online adjustment of configuration parameters to discover the system's performance characteristics. Doing so creates two challenges: (1) handling interdependencies between configuration parameters and (2) minimizing the deleterious effects on production workload while the optimization is underway. Our approach addresses (1) by including in the architecture a rule-based component that handles interdependencies between configuration parameters. For (2), we use a feedback mechanism for online optimization that searches the parameter space in a way that generally avoids poor performance at intermediate steps. Our studies of a DB2 Universal Database Server under an e-commerce workload indicate that our approach is effective in practice. © 2004, IEEE. All rights reserved.
Adam J. Storm, Christian Garcia-Arellano, et al.
VLDB 2006
Yixin Diao, Joseph L. Hellerstein, et al.
MASCOTS 2006
K. Kloeckner, Constantin Adam, et al.
IBM J. Res. Dev
Yixin Diao, Aliza Heching
NOMS 2012