Priced-Based Fair Bandwidth Allocation for Networked Multimedia
Bibliographic record
Abstract
The high demand of bandwidth from multimedia applications, specially video applications which consume the great majority of the Internet bandwidth, has caused a challenge for service providers and network operators. On the one hand, the allocation of bandwidth in a fair manner for multimedia users is necessary, so that the total utility of all users is maximized for higher quality of experience. On the other hand, optimizing the utilization of network resources such as maximizing throughput is also important for network operators to reduce cost and/or maximize profits. These two requirements could potentially be conflicting; hence, achieving both at the same time is challenging, and the reason why very few previous efforts have targeted this problem. Examples include Traffic Management Using Multipath Protocol (TRUMP) and Logarithmic-Based Multipath Protocol (LBMP), both of which achieve good results but are not without shortcomings. In this paper, we propose a Price-Based Fair Bandwidth Allocation (PBFA) method by implementing an optimized sending rate adaptation technique and combining it with an intuitive investment method to optimize the feedback prices to achieve efficient and fair bandwidth allocation. The results of our performance tests, using different simulations under different network topologies, show that PBFA achieves improvements of as much as 90% in fairness, 207% in throughput, and 91% in utility compared to TRUMP
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".