Finite Buffer Queue Analysis and Scheduling for Heavy-Tailed Traffic in Packet-SwitchingWireless Networks
Bibliographic record
Abstract
Recent research based on traffic measurements shows that Internet traffic flows have a fractal nature (i.e., self-similarity property), which causes an underestimation of network engineering parameters when using the conventional Poisson model. Preliminary field measurements demonstrate that packet data traffic in wireless communications also exhibits self-similarity. In this paper, we investigate the queuing behavior of self-similar traffic flows for data applications in packet-switching wireless networks. The traffic is generated by an on-off source with heavy-tailed on periods. We extend a previous relationship among the asymptotic distribution of loss probability, finite buffer size, traffic specifications, and transmission rate for a wireline system to a wireless system, taking into account wireless propagation channel characteristics. We also investigate the multiplexing of heavy-tailed traffic flows with a finite buffer for the downlink transmission of a cellular network. Computer simulation results demonstrate that assumptions made in the theoretical analysis are reasonable and the derived relationship is accurate.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".