Throughput and delay performance of transport user in congestion controlled hybrid ATM/TDMA networks
Bibliographic record
Abstract
The end-to-end throughput and delay performance characteristics are analyzed for a virtual circuit (VC) transport user in a hybrid asynchronous transfer mode/time division multiple access (ATM/TDMA) network. An automatic repeat request (ARQ) transport user is assumed with an underlying ATM cell-level global congestion control in an ATM multiplexer node. The analysis is based on the interaction of packet level control with the queue management at the ATM cell level. The transport user is assumed over M-node VC to analyze the throughput and delay using Norton equivalent queueing model. The transport layer service characteristic of the model is obtained from the end-to-end protocol efficiency of Go-Back-N (GBN) and selective repeat (SR) ARQ schemes. The ATM layer is assumed with a leaky bucket (LB), virtual leaky bucket (VLB), modified LB (mLB), or modified VLB (mVLB) congestion control scheme. A global congestion control scheme prioritizes transit traffic over local traffic, and ensures quality of service (QOS) to several classes of service. Based on the global congestion status, the transport users modulate their end-to-end flow control parameters, i.e., packet size in case of video and voice users, and window size in case of data users. The probability of cell-loss at the ATM layer is reflected at the transport layer to derive the effective throughput and delay characteristics. The mVLB scheme consistently provided better end-to-end throughput and delay performance for both GBN and SR transport users.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".