A Multiserver Approximation for Cloud Scaling Analysis
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
Queueing models of web service systems run at increasingly large scales, with large customer populations and with multiservers introduced by scaling up the services. "Scalable" multiserver approximations, in the sense that they that are insensitive to customer population size, are essential for solution in a reasonable time. A thorough analysis of the potential errors, which is needed before the approximations can be used with confidence, is the goal of this work. Three scalable approximations are evaluated: an equivalent single server SS, an approximation RF introduced by Rolia, and one based on a binomial distribution for queue state AB. AB and SS are suggested by previous work but have not been evaluated before. For AB and SS, multiple classes are merged into one to calculate the waiting. The analysis employs a novel traffic intensity measure for closed multiserver workloads. The vast majority of errors are less than 1%, with the worst cases being up to about 30%. The largest errors occur near the knee of the throughput/response time curves. Of the approximations, AB is consistently the most accurate and SS the least accurate.
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.
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 | 0.000 | 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