Quantifying the properties of SRPT scheduling
Why this work is in the frame
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Bibliographic record
Abstract
This paper uses a probe-based sampling approach to study the behavioural properties of Web server scheduling strategies, such as processor sharing (PS) and shortest remaining processing time (SRPT). The approach is general purpose, in that it can be used to estimate the mean and variance of the job response time, for arbitrary arrival processes, service time distributions, and scheduling policies. In the paper, we apply the approach to trace-driven simulation of Web server scheduling to compare and contrast the PS and SRPT scheduling policies. We identify two types of unfairness, called endogenous and exogenous unfairness. We quantify each, focusing on the mean and variance of slowdown, conditioned on job size, for a range of system loads. Finally, we confirm recent theoretical results regarding the asymptotic convergence of scheduling policies with respect to slowdown, and illustrate typical performance results for a practical range of job sizes from an empirical Web server workload.
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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 | 0.000 |
| 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