Probabilistic Analysis of Resequencing Queue Length in Multipath Packet Data Networks
Bibliographic record
Abstract
In multipath packet data networks, packets may reach the receiver out-of-sequence, i.e., packets arrive at the receiver in a sequence different from their egressing order at the transmitter. In practice, however, many applications require an in-sequence packet delivery, meaning that packets need to be delivered to an application on the receiver in their original order at the transmitter. The in-sequence packet delivery is usually implemented through the approach of packet resequencing. In this paper, a multipath data network with packet resequencing is modeled and the asymptotic properties of the steady-state probability distribution of the resequencing queue length are studied. The assumptions used are that the packets sent from the transmitter according to a Poisson process, and the transmission period of a packet along a route follows an exponential distribution. An asymptotic distribution function of the resequencing queue length is derived for a large queue length in the steady state of the network. Numerical and simulation examples are presented to validate the derived result. Through comparisons of large deviation and asymptotic values of the resequencing queue length distribution, we show that the asymptotic result provides a better approximation to the distribution function of the resequencing queue length than the large deviation result reported in the literature.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".