Optimal resource allocation and fairness control in all-optical WDM networks
Bibliographic record
Abstract
This paper investigates the problem of optimal wavelength allocation and fairness control in all-optical wavelength-division-multiplexing networks. A fundamental network topology, consisting of a two-hop path network, is studied for three classes of traffic. Each class corresponds to a source-destination pair. For each class, call interarrival and holding times are exponentially distributed. The objective is to determine a wavelength allocation policy in order to maximize the weighted sum of users of all classes (i.e., class-based utilization). This method is able to provide differentiated services and fairness management in the network. The problem can be formulated as a Markov decision process (MDP) to compute the optimal allocation policy. The policy iteration algorithm is employed to numerically compute the optimal allocation policy. It has been analytically and numerically shown that the optimal policy has the form of a monotonic nondecreasing switching curve for each class. Since the implementation of an MDP-based allocation scheme is practically infeasible for realistic networks, we develop approximations and derive a heuristic algorithm for ring networks. Simulation results compare the performance of the optimal policy and the heuristic algorithm, with those of complete sharing and complete partitioning policies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".