CAC and routing for multi‐service networks with blocked wide‐band calls delayed, part I: exact link MDP framework
Bibliographic record
Abstract
Abstract In this paper, we study the call admission control (CAC) and routing issue in multi‐service networks. Two categories of calls are considered: a narrow‐band (NB) with blocked calls cleared and a wide‐band (WB) with blocked calls delayed. The objective function is formulated as reward maximisation with penalty for delay. The optimisation is subject to quality of service (QoS) constraints and, possibly, grade of service (GoS) constraints. A suboptimal solution is achieved by applying Markov decision process (MDP) theory together with a two‐level approximation. First, the network is decomposed into a set of links assumed to have independent Markov and reward processes respectively. Second, the dimensions of the link Markov and reward processes are reduced by aggregation of the call classes into call categories. The CAC and routing policy is computed by the policy iteration algorithm from MDP theory. The numerical results show that the proposed CAC and routing method, based on the exact link MDP framework, is able to find an efficient trade‐off between reward loss and average call set‐up delay, outperforming conventional methods such as the least loaded routing (LLR). Copyright © 2005 AEIT.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".