Approximate MVA for client-server systems with nonpreemptive priority
Bibliographic record
Abstract
A new approximate algorithm for the Stochastic Rendezvous Network (SRVN) model with nonpreemptive priority scheduling is introduced in this paper. SRVN is a performance model for client-server systems with synchronous communication which is different from Queueing Network models in two ways: it allows for nested services, and offers two phases of service (the first executed while the client is blocked and the second in parallel with the client). Earlier SRVN solutions have used a kind of approximate MVA based on heuristic assumptions to determine the queues properties at the instants of service request arrivals. More recently a new strategy called "Task-Directed Aggregation" (TDA) was introduced for the derivation of the arrival-instant probabilities equations for FIFO servers. The present paper applies TDA to nonpreemptive priority scheduling, thus demonstrating the value of this new strategy for models with no product-form solution. Experimental results show that the accuracy of the algorithm is good if the server is not saturated, and if a reasonable fraction of the load is available for the low-priority clients. The accuracy of the algorithm is consistent with results known for QN priority approximations.>
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".