A task-oriented priority queue for telephone switch design .II. with modified FCFS and forking
Bibliographic record
Abstract
When developing a telephone switch, it is useful to know how long it will take to process the various tasks associated with call processing. The model developed in this paper gives expected sojourn times for those tasks. It is a priority queueing model with a modified first-come first-served (FCFS) service discipline, which mimics the treatment of tasks in actual system software. The model is an M/G/1 queueing model with preemption (preemptive resume). It consists of multiple queues, one for each distinct priority, where each task has been preassigned a constant priority. Within each priority queue, the tasks are further grouped by type. An arriving task will join the back of the group of tasks of its type, regardless of where this group is positioned in the queue. Upon completion of a task, multiple subsequent tasks can be given ready-for-service status, and enter the priority queues. This is referred to its forking. Call processing involves many ordered sets of tasks (jobs), some of which will contain forks. The model produces results that compare favorably with those obtained by simulation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".