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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".