Aggregation of long-range dependent traffic streams using multifractal wavelet models
Bibliographic record
Abstract
The operation of Diffserv QoS service provisioning requires the aggregation of IP or MPLS traffic streams into a limited number of DiffServ classes and queue them accordingly. To be able to analytically estimate the QoS of each Diffserv class, the characteristic of the class aggregate traffic must be determined. This paper presents an analytical technique to characterize aggregate traffic. It is widely known that IP traffic stream is highly bursty and correlated, and can be modeled as a long range dependent traffic stream. By using multifractal wavelet models (MWM) to represent each long range dependent traffic stream, the proposed technique calculates MWM model parameters for the aggregate traffic and uses them to estimate the IDC of the aggregate traffic. The calculated MWM parameters are also used to estimate QoS received by the traffic aggregate. Both analysis and simulation are used to examine the performance of the proposed technique for various traffic conditions and scenarios. The MWM parameters of a number of real traffic traces are estimated and used to determine the characteristics of the aggregate traffic. The analytically derived parameters are compared to those directly measured from the simulated aggregate traffic. The results show that the calculation technique is accurate and can be effectively used in Diffserv network.
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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 | 0.001 |
| 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".