Admission Control and Bandwidth Allocation for Class A Traffic in RPR Networks
Bibliographic record
Abstract
In this thesis, we study the admission control and bandwidth allocation methods for classA traffic in RPR networks. First, we investigate the performance of classA traffic under the current RPR protocol. The simulation results show that RPR networks can support low-delay classA traffic even if the networks are congested with classB and classC traffic. The low-delay performance, however, is subject to the condition that the load of classA traffic must be properly controlled. Consequently, an admission control mechanism must be used for classA traffic. In this thesis, several admission control algorithms are studied. They are the Simple Sum algorithm, the Measured Sum algorithm, and the Equivalent Bandwidth algorithm. The simulation results show that the Equivalent Bandwidth algorithm is the most suitable to use as the admission control mechanism for classA traffic. The admission control mechanism makes admission decision based on the available bandwidth allocated to the classA traffic. The existing RPR standard assumes the bandwidth allocated for classA traffic at each node is fixed. The fixed bandwidth allocation introduces inflexibility and inefficient use of bandwidth for classA traffic. In this thesis, three bandwidth allocation algorithms are proposed to dynamically allocate bandwidth for classA traffic. These algorithms have different levels of complexity and can be applied to different traffic environments. Simulation results show that the proposed algorithms improve the bandwidth efficiency of the RPR networks. The proposed algorithms are also readily integrated with the existing Internet Quality of Services (QoS) paradigms such as Diffserv and RSVP services.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 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".