Asymptotic Performance of Energy-Aware Multiserver Queueing Systems with Setup Times
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Bibliographic record
Abstract
Energy demands of modern datacentres are an immense concern. An intuitive solution is to turn servers off to incur less costs. However, the control problem of when to turn a specific server off, and when to then turn that server back on, is far from trivial. As such, many different authors have modeled this problem as an M/M/C queue where each server can be turned on, with an exponentially distributed setup time, or turned off instantaneously. We analyse this well-established model under the asymptotic regime where the number of servers approaches infinity while the load per server remains fixed and show that not only are many of the control policies in the literature equivalent under this regime, but they are also optimal under any cost function which is non-decreasing in the expected energy cost and response time.
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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 | 0.000 | 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 | 0.001 |
| 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