Multitenancy benefits in application servers
Bibliographic record
Abstract
Multitenancy enables sharing of resources between different users, also known as tenants and is a backbone feature of cloud computing. The tenants execute their code as if resources were held individually by them. The sharing is transparent; the tenants are isolated from each other and one tenant is not allowed to affect the performance of the rest by overusing a resource. We propose a theoretical model to describe and predict memory footprint reductions by different levels of multitenancy in application servers, including our multitenancy level, which enables even further sharing, acting as an Application-Server-as-a-Service (ASaaS). We confirm our model by implementing a small custom application server in Java and measuring its footprint for different multitenancy levels. We find that our ASaaS approach requires up to 65% less memory without any major response time overheads. Finally, we perform an analysis of potential memory sharing on an enterprise software stack between different levels of multitenancy, including our proposed ASaaS level, and the results support our findings.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".