Adaptive self-tuning memory in DB2

  • Authors:

  • Adam J. Storm;Christian Garcia-Arellano;Sam S. Lightstone;Yixin Diao;M. Surendra

  • Affiliations:

  • IBM Canada;IBM Canada;IBM Canada;IBM TJ Watson Research Center;IBM TJ Watson Research Center

  • Venue:
  • VLDB '06 Proceedings of the 32nd international conference on Very large data bases
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

DB2 for Linux, UNIX, and Windows Version 9.1 introduces the Self-Tuning Memory Manager (STMM), which provides adaptive self tuning of both database memory heaps and cumulative database memory allocation. This technology provides state-of-the-art memory tuning combining control theory, runtime simulation modeling, cost-benefit analysis, and operating system resource analysis. In particular, the nove use of cost-benefit analysis and control theory techniques makes STMM a breakthrough technology in database memory management. The cost-benefit analysis allows STMM to tune memory between radically different memory consumers such as compiled statement cache, sort, and buffer pools. These methods allow for the fast convergence of memory settings while also providing stability in the presence of system noise. The tuning mode has been found in numerous experiments to tune memory allocation as well as expert human administrators, including OLTP, DSS, and mixed environments. We believe this is the first known use of cost-benefit analysis and control theory in database memory tuning across heterogeneous memory consumers.