Buffer management and rate guarantees for TCP over satellite‐ATM networks
Bibliographic record
Abstract
Abstract Future broadband satellite networks will support a variety of service types. Many such systems are being design with ATM or ATM‐like technology. A majority of Internet applications use TCP for data transfer. As a result, these systems must efficiently transport TCP traffic and provide service guarantees to such traffic. Several mechanisms have been presented in recent literature to improve TCP performance. Most of these can be categorized as either TCP enhancements or network‐based buffer management techniques. Providing minimum rate guarantees to TCP traffic has also been suggested as a way to improve its performance in the presence of higher priority traffic sharing the link. However, the relative performance of the TCP enhancements versus the buffer management schemes has not been analyzed for long latency networks. In this paper, we address three issues. First, we present a performance analysis of TCP over satellite‐ATM links using a best effort service—the ATM unspecified bit rate (UBR) service. This analysis shows that the relative impacts of buffer management, TCP policies and rate guarantees on TCP performance, depend heavily on the latency of the network. Second, we show through simulations that the buffer size required in the network for high TCP performance is proportional to the delay‐bandwidth product of the network. Third, we propose a buffer management scheme called differential fair buffer allocation (DFBA) and show how it is used to implement a service that provides minimum rate guarantees to TCP traffic. An example of such a service is the ATM guaranteed frame rate (GFR) service, which is being standardized by the ATM Forum and the ITU. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".