Sizing Multiple Buffer Pools for
Bibliographic record
Abstract
The buffer area is a key resource in database management systems (DBMSs) and the performance of a DBMS is greatly influenced by the effective use of the buffer area. Current DBMSs such as DB2 Universal Database (DB2/UDB), logically divide the buffer area into a number of independent buffer pools. Each database object (table or index) is assigned to a specific buffer pool. The task of configuring the buffer pools, which defines the mapping of database objects to buffer pools and the setting the size for each of the buffer pools, is crucial for achieving optimal performance. In this thesis, we focus on the buffer pool sizing problem. The goal of our research is to support the DBMS automatically determining an optimal buffer pool sizes for a given workload. This problem has been shown to be a complex constrained optimization problem. Currently this task is performed manually by the database administrators (DBAs). We present a cost model based on data access time and use a greedy algorithm to solve the optimization problem. The approach is implemented and verified against the TPC-C benchmark database using DB2/UDB. Experimental results show the cost model is accurate, and that the greedy algorithm is fast and sufficient in finding an optimal buffer pool sizes. i Acknowledgements I would like to express my sincere gratitude to my supervisor, Dr. Pat Martin, for his excellent guidance, precious advice and endless support during my graduate study and research at Queen's University. Without his help, this thesis would never have got finished. I would also like to thank Wendy Powley, our wonderful database expert, for the help in setting up the experimental environments and the suggestions about writing this thesis. My thanks also go to my friendly labmates, whose help and advice is very helpful to this thesis. Special thanks are given to the School of Computing at Queen’s University for providing me the opportunity to pursue graduate studies and their support. I also thank IBM Canada Ltd. NSERC, and CITO for the financial support. Finally, I would like to thank my parents, and my beautiful wife, Jie Lu, for their love, support, and encouragement in these years. ii
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".