Database Virtualization: A New Frontier for Database Tuning and Physical Design
Bibliographic record
Abstract
Resource virtualization is currently being employed at all levels of the IT infrastructure to improve provisioning and manageability, with the goal of reducing total cost of ownership. This means that database systems will increasingly be run in virtualized environments, inside virtual machines. This has many benefits, but it also introduces new tuning and physical design problems that are of interest to the database research community. In this paper, we discuss how virtualization can benefit database systems, and we present the tuning problems it introduces, which relate to setting the new "tuning knobs" that control resource allocation to virtual machines in the virtualized environment. We present a formulation of the visualization design problem, which focuses on setting resource allocation levels for different database workloads statically at deployment and configuration time. An important component of the solution to this problem is modeling the cost of a workload for a given resource allocation. We present an approach to this cost modeling that relies on using the query optimizer in a special virtualization-aware "what-if" mode. We also discuss the next steps in solving this problem, and present some long-term research directions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".