A call admission control for service differentiation and fairness management in WDM grooming networks
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Bibliographic record
Abstract
We investigate a call admission control (CAC) mechanism to provide fairness control and service differentiation in a WDM network with grooming capabilities. A WDM grooming network can handle different classes of traffic streams which differ by their bandwidth requirements. We assume that for each class, call interarrival and holding times are exponentially distributed. Using a Markov decision process approach, an optimal CAC policy is derived to provide fairness in the network. The policy iteration algorithm is used to numerically compute the optimal policy. Furthermore, we propose a heuristic decomposition algorithm with lower computational complexity and very good performance. Simulation results compare the performance of our proposed policy, with that of complete sharing and complete partitioning policies. Comparisons show that our proposed policy provides the best performance in most cases. Although this approach is motivated by WDM networks, it may be deployed to determine the optimal resource allocation in many problems in wireless and wired telecommunications systems.
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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 | 0.000 | 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 | 0.000 |
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 it