Multitenancy benefits in application servers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it