A robust method for fitting the (/spl sigma//spl I.oarr/, /spl rho//spl I.oarr/) model to a traffic source
Bibliographic record
Abstract
A communication network, which offers a deterministic QoS guarantee to VBR traffic sources, must use a traffic regulation scheme to reserve network resources for each source. The key component of a traffic regulation scheme is the traffic characterization model used to characterize the traffic of each source. The (/spl sigma//spl I.oarr/, /spl rho//spl I.oarr/)model is so far the most popular traffic model used in communication networks. In order to achieve high network utilization, parameters of the traffic model should be selected carefully, such that the model specifies the actual traffic as accurately as possible. We present a novel method for selecting the parameters of a (/spl sigma//spl I.oarr/, /spl rho//spl I.oarr/)model for a VBR traffic source. Our method strives for accuracy, implementation simplicity and execution speed as the design goals. Our approach consists of two parts: 1) constructing the empirical envelope of the source from the traffic, and 2) finding the model parameters from the empirical envelope. We present novel solutions for these two problems. Our method for constructing the empirical envelope is faster and more accurate than the presently existing methods and can be employed in real-time applications. Our method for finding the (/spl sigma//spl I.oarr/, /spl rho//spl I.oarr/)model parameters from the empirical envelope is based on the 'divide and conquer' and sequential programming optimization techniques, and finds a near optimum result. The performance and accuracy of our methods were experimentally compared to other available methods. The results showed that the overall performance, specifically the speed and the accuracy of our methods, are significantly better than the current methods found in the literature.
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.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".