Provisioning of Computing Resources for Web Applications under Time-Varying Traffic
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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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".