Optimal Capacity and Flow Assignment for Self-Healing ATM Networks Based on Path Restoration: Nonlinear Models
Bibliographic record
Abstract
This thesis addresses the design of self-healing Asynchronous Transfer Mode (ATM) networks which is a special aspect of a more general problem, referred to as capacity and flow assignment (CFA) problem in self-healing ATM networks. We have proposed two nonlinear mathematical models for global reconfiguration strategy and failure-oriented reconfiguration strategy in our thesis. Our restoration strategies aim to minimize the capacity installation cost and the routing cost when a single link failure occurs in the network. A special case of the augmented Lagrangian method so-called Separable Augmented Lagrangian Algorithm (SALA) is proposed for solving the proposed nonlinear mathematical models. Numerical results are presented comparing the two restoration strategies in terms of five performance metrics which are capacity installation cost, total required capacity, routing cost, total network cost and required CPU time for convergence of the algorithms. Our results show that the global reconfiguration strategy has always performed better than the failure-oriented reconfiguration strategy for all the network scenarios, topologies and bandwidth requirements.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".