The role of traffic forecasting in QoS routing - a case study of time-dependent routing
Bibliographic record
Abstract
QoS routing solutions can be classified into two categories, state-dependent and time-dependent, according to their awareness of the future traffic demand in the network. Compared with representative state-dependent routing algorithms, a time-dependent variation of WSP - TDWSP - is proposed in this paper to study the role of traffic forecasting in QoS routing, by customizing itself for a range of traffic demands. Our simulation results confirm the feasibility of traffic forecasting in the context of QoS routing, which empowers TDWSP to achieve better routing performance and to overcome QoS routing difficulties, even though completely accurate traffic prediction is not required. The case study involving TDWSP further reveals that even a static forecast can remain effective over a large area in the solvable traffic demand space, if the network topology and the peak traffic value are given. Thus, the role of traffic forecasting in QoS routing becomes more prominent.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".