Performance Analysis of Oracle Database in Virtual Environments
Bibliographic record
Abstract
The prevalence of server consolidation to capitalize on performance gains has seen various vendors presenting their virtual solution as the best option. This therefore highlights the importance of an independent and unbiased academic research in facilitating informed decisions. In this paper we examine the correctness of the marketing claim currently available at the Oracle's website that the Oracle VM server virtualization is four times more scalable than the VMware' virtualization solution. Therefore, we present results of tests and quantitative analysis of the effects of workload and configuration options on the Oracle database performance. The scalability tests were conducted in a non virtualized environment and compared with results of the tests in two virtualized server environments on the Red Hat Enterprise Linux 5.6 platform: (1) VMware ESXi 4.1 and (2) Oracle VM 2.2. The results of our tests confirm that the Oracle VM offers more scalability for Oracle 11g database applications than the VMware ESXi on a number of performance benchmarks. However, the experimental results for Oracle database applications do not show a clearly superior scalability of the Oracle VM server virtualization compared to the VMware ESXi virtualization solution. It is expected that the presented results will contribute to the awareness on the importance of an independent research for server virtualization informed decisions.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".