Traffic Trend Estimation for Profit Oriented Capacity Adaptation in Service Overlay Networks
Bibliographic record
Abstract
Service Overlay Networks (SON) can offer end to end Quality of Service by leasing bandwidth from Internet Autonomous Systems. To maximize profit, the SON can continually adapt its leased bandwidth to traffic demand dynamics based on online traffic trend estimation. In this paper, we propose novel approaches for online traffic trend estimation that fits the SON capacity adaptation. In the first approach, the smoothing parameter of the exponential smoothing (ES) model is adapted to traffic trend. Here, the trend is estimated using measured connection arrival rate autocorrelation or cumulative distribution functions. The second approach applies Kalman filter whose model is built from historical traffic data. In this case, availability of the estimation error distribution allows for better control of the network Grade of Service. Numerical study shows that the proposed autocorrelation based ES approach gives the best combined estimation response-stability performance when compared to known ES methods. The proposed Kalman filter based approach improves further the capacity adaptation performance by limiting the increase of connection blocking when traffic level is increasing.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".