Discrete-time analysis of packet data discarding in high speed multimedia networks
Bibliographic record
Abstract
Packet discarding policies have been shown to significantly enhance the goodput of the system in high speed networks. In this paper, we develop a discrete time queueing model that combines two major discarding policies - the Partial Message Discard (PMD) and the Early Message Discard (EMD) policy. The PMD policy discards any subsequent packets that belong to a message that has lost a packet due to buffer overflow. The EMD policy protects against PMD by allowing the packets of a new message into the system only when the queue length is below a particular threshold. Packets can also be corrupted by the wireless medium which causes them to be discarded from the system. We represent packet data stream generation in high speed data networks by using the discrete Platoon Arrival Process (PAP), for capturing the correlation of intervals between packet arrivals. Using this model, we are able to obtain the probabilities of a successful message transmission and the Goodput of the system. The details of our model will be given for the case of a single source arrival. We briefly describe and present our results for the case of two arrival sources. The model has applications in streaming multimedia.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".