Dynamic Resource Allocation for Packet Loss Differentiated Services in VPN Access Links
Bibliographic record
Abstract
With the increasing deployment of IP/MPLS VPN services their QoS control mechanisms on the core network have been extensively studied in the literature. Unfortunately, satisfying requirements of QoS in VPN access links is missing, where lots of small and medium businesses (SMB) or future home networks usually purchase a fixed and limited bandwidth connection to the external network. When more and more users and traffic in the group need to share the limited resource, the access link is becoming the critical factor to affect customers' QoS. This paper tries to provide a low-cost and practical solution to approach the QoS control issue. Specifically, it measures the on-line traffic and dynamically controls the bandwidth usage for each traffic class towards guaranteeing a quantitative packet loss parameter. First, the traffic is differentiated into several classes according to a packet loss probability parameter. Then the statistic QoS parameter is guaranteed by dynamically allocating the appropriate bandwidth for each single class. The numeric results are obtained from the live NCIT*net2 network and demonstrate it is practicable and effective in the real application
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".