Provisioning of Computing Resources for Web Applications under Time-Varying Traffic
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
Provisioning computing resources for a web application poses a challenge due to the time-varying nature of the application workload. We consider an environment where a subscriber acquires computing resources from an infrastructure provider to deploy a transactional web application. The objective is to acquire sufficient resources to meet a performance target given by Pr[response time ≤ x] ≥ β. Markov-modulated Poisson process (MMPP) has been shown to effectively model time-varying traffic. It has also been shown that service times can be characterized by a hyper-Erlang (HE) distribution. HE is a special case of the phase-type (PH) distribution. We model the application by an MMPP/PH/1 queue and use analytic results of this model to determine the required capacity to meet a given performance target over an extended period of time. An implementation of the TPC-W benchmark is used to verify the effectiveness of the model. We also investigate the relationship between the required capacity and workload parameters.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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