Packet Loss Measurement and Control for VPN based Services
Why this work is in the frame
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Bibliographic record
Abstract
Provisioning QoS enabled VPN services over packet switched networks is increasingly important for service providers. Prior works usually adopted the proactive service admission approach, but little attention has been given to the control of QoSetersparameters after the service has been instantiated. This paper proposes a packet loss measurement and rate-based feedback control system that maintains preset packet loss targets for instantiated VPN services in the provider's backbone network. Specifically, the system utilizes the measurement and estimation of packet loss probability as the feedback signal, and then applies a pole placement technology to design the controller for throttling ingress customers' traffic. Through a number of experiments, the transient and steady state performance of the controller is evaluated. The numeric results show that, under appropriately selected control gains, it is possible to maintain the network operation within a prescribed loss range.
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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 | 0.001 | 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 | 0.001 |
| 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 it