On resource pooling in SITA-like parallel server systems
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Bibliographic record
Abstract
The routing policy Size Interval Task Assignment (SITA) isolates small arrivals from large arrivals, while choosing intervals to balance the workload of each server. It works well for highly variable arrivals, but the isolation can cause server idle-ness. To improve this, we suggest a scheme to add pooling to these SITA-like systems, which can result in better performance. We propose a routing policy, SITA-JSQ, which chooses a proportion of the dedicated arrivals, originally allocated by the SITA policy with equal loads, as flexible arrivals allocated by a JSQ policy between adjacent servers. Under heavy traffic and Complete Resource Pooling conditions, the asymptotic Brownian Motion limit for the unfinished processing times processes is obtained. Using these limits, we show that SITA-JSQ gives asymptotically better performance with respect to unfinished processing times than SITA. Through simulation, we also demonstrate significant reductions in mean waiting times. Finally, we compare our approach to cycle stealing from idle servers.
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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 | 0.001 |
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