Tail probabilities for the multiplexing of fractional α-stable broadband traffic
Bibliographic record
Abstract
We investigate the tail probabilities of a multiplexer driven by /spl alpha/-stable self-similar traffic. We consider a parsimonious 4-parameter traffic model, which best captures the long range dependence and heavy-tails of aggregate packet traffic in broadband networks. Input traffic with these characteristics, induces buffer dynamics which are qualitatively different from those which arise in traditional traffic management. Using the effective bandwidth theory, we extend the recent results on /spl alpha/-stable self similar-driven queues with infinite buffer to the finite buffer case that model routers/switches more accurately. Queuing simulations with real broadband traffic emphasise the improvements in engineering considerations (e.g., connection admission control, buffer management, statistical multiplexing gains), with respect to the existing results so far. Our experiments involve a large set of real broadband network traffic which consists of Ethernet LAN, Internet WAN and MPEG-1 compressed video traces.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".