Performance and Scalability Evaluation of Oracle VM Server Software Virtualization in a 64 Bit Linux Environment
Bibliographic record
Abstract
The growing adoption of virtualization in the enterprise environment has resulted in a couple of huge benefits, however, this has not been without its attendant problems and anomalies, such as performance tuning and erratic performance metrics, unresponsive virtualized systems, crashed virtualized servers, misconfigured virtual hosting platforms, amongst others. The focus of this research was the analysis of the performance of the Oracle VM server virtualization platform against that of the bare-metal server environment. The scalability and its support for high volume transactions were also analyzed using 30 and 50 active users for the performance evaluation. Swing bench and LM bench, two open suite benchmark tools were utilized in measuring performance. Scalability was also measured using Swing bench. Evidential results gathered from Swing bench revealed 4% and 8% overhead for 30 and 50 active users respectively in the performance evaluation of Oracle database in a single Oracle VM. Correspondingly, performance metrics of 75% and 87% were obtained with 30 and 50 active users in a dual Oracle VM server, indicating performance scalability improvement with two virtual machines. Our results also revealed significant percentages in latency and bandwidth achievement by Oracle VM server which cannot be overlooked, despite some variances in results obtained from LM bench measurement.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".