Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The goal of traffic engineering is to achieve a target Quality of Service (QoS) while maximizing network utilization. While determining the QoS for end-to-end paths in a network under self-similar traffic models is difficult, end-to-end network performance analysis is still essential in providing QoS to networks such as Virtual Private Networks (VPN) and Peer-to-Peer (P2P) networks. The Fast Importance Sampling based Traffic Engineering (FISTE) approach proposed in this article is a prediction-based approach that maps the ingress traffic levels of a network to the QoS of end-to-end path(s) in the network. Because FISTE is a hybrid of simulation analysis and closed-form analysis, it can treat a complex network as a black box. When we combined Simulated Annealing (SA) with FISTE, the resulting approach can provide a traffic engineering solution so that multiple end-to-end QoS requirements are satisfied while the network resource utilization is maximized. FISTE originated from the concept of Importance Sampling (IS), and our approach differs from the previous Importance Sampling based approaches since this is the first time that IS is applied to multi-queue systems under Fractional Gaussian Noise (FGN) input and traffic engineering.
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.
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 | 0.000 | 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