DiffServ-enabled adaptive traffic engineering over MPLS
Bibliographic record
Abstract
In the past few years, new types of Internet applications that require performance guarantees beyond the best effort service have emerged. These applications have spread to include voice, video teleconferencing, and a new set of pervasive computing devices that demand hard delay bound guarantees. The Differentiated services model when combined with traffic engineering can provide a suitable architecture for ensuring QoS guarantees to such applications, especially when multiple parallel paths to the same destination exist in a provider's network. Multiprotocol label switching (MPLS) is a new technology that facilitates significantly the process of traffic engineering in IP networks. With MPLS and differentiated services traffic engineering (DS-TE), it is possible to define explicit routes with different performance guarantees on each route to ensure QoS constraints are met. We introduce a probe packet approach to measure the delay on established label switched paths (LSPs) between ingress/egress edge routers pair. These measurements are then used in a delay estimation algorithm to provide the measure of the expected delay within the path. We demonstrate, through simulations, that our proposed model provides a better service to higher priority traffic classes.
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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 | 0.000 | 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 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".