Approximate method for polling systems with time-limited-based polling tables
Bibliographic record
Abstract
Time-limited based polling systems with polling tables are encountered frequently in operating systems schedulers such as the fair share scheduler. The analysis of such systems are usually carried out by simulation. We present a simple approximation for determining the mean waiting times in such polling systems. This approximation can be used by system designers to carry out a first cut design to reduce the set of alternative designs and then use simulation to assess a few good designs. The method transforms the M table polling system with N distinct stations, (N/spl les/M), to an equivalent M pseudostations cyclic polling system. It then uses the known K-limited polling results by approximating K from the time limit and the mean service time of each station. Polling systems are a class of multiqueueing systems attended to a single server. Such systems are encountered very frequently in communications and computer, traffic signal, and also manufacturing systems. For computer systems in a multiprogramming environment we would like to keep the CPU busy all the time (a 100% utilization would be optimal).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".