Bandwidth Allocation with Fairness in Multipath Networks
Bibliographic record
Abstract
Network resource management and traffic engineering are important subjects in today's Internet.In terms of traffic engineering, bandwidth allocation and splitting it in a fair manner among different users have become challenging.In addition, optimizing the utilization of network resources, increasing the user utility and throughput are also considerable.So, the user satisfaction with regard to the resource allocation and Quality of Service (QoS) are the most important factors that should be taken into the consideration.At the first step, Network Utility Maximization (NUM) problem has been considered as an initial stage to design any traffic engineering method.In this paper and by considering the mentioned issues, first of all we take into account the NUM problem and optimization decomposition methods by focusing on Traffic Management Using Multipath Protocol (TRUMP), and its weaknesses to tackle the fair resource allocation problem associated with it.We then propose a model to tackle the fair bandwidth allocation issue by implementing an optimized sending rate adaptation model using an intuitive investment method to optimize the link prices (delay and loss) to achieve an efficient fair bandwidth allocation model.The model is evaluated by using different simulations and different topologies under various network conditions.Our results show that the proposed model behaves fairer than TRUMP in certain path selections.As an average from the results and at a minimum point our model achieves 26% improvement in fairness in contrast to TRUMP.In addition, for large networks we can enjoy approximately 90% improvement in fairness measure.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".