Iterative improvement of database configurations.
Published: 2006
Language: eng
Number of pages: 87
ISBN-13: 9780494210673
Description: Autonomic systems are motivated by the increased scale, complexity and cost of managing the current information infrastructure. Database configuration advisors are a very successful class of autonomic tools (they suggest desirable indexes, materialized views, and so on). Giving advice on configurations is a challenging task, yet the rewards on system responsiveness and performance can be huge.In this thesis we describe an iterative framework to provide on-going recommendations. We present algorithms that re-assess recommendations every step of the way until the DBMS reaches a satisfactory level of performance. Our proposal should be contrasted with the current approach of applying all of the recommender suggestions in one big batch. The proposed iterative framework allows the user to control the process of implementing new physical structures while observing the improvement in performance.
Notes: Source: Masters Abstracts International, Volume: 45-03, page: 1541.

Thesis (M.Sc.)--University of Toronto, 2006.

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