Improving TCP performance in wired-wireless networks by using a novel adaptive bandwidth estimation mechanism
Bibliographic record
Abstract
The paper presents a novel dynamic bandwidth estimation mechanism for improving TCP (Transmission Control Protocol) performance in wired-cum-wireless networks. The key idea is to measure continuously the bandwidth used by a TCP flow by monitoring the rate of returning acknowledgements (ACKs) and the round-trip time (RTT) values. The distinguishing feature of this mechanism (compared to other mechanisms such as that in TCP Westwood) is that it exploits the burstiness pattern of ACK arrivals and estimates the available bandwidth more accurately. In the proposed mechanism, the bandwidth sample is calculated by distributing a burst of ACKs over an off period based on the degree of congestion and burstiness in the network. The estimation technique is robust against burstiness of ACK arrival and type of loss (e.g., wireless loss, congestion loss). A new variant of TCP New-Reno based on this adaptive bandwidth estimation technique is referred to as TCP Prairie. Simulation results obtained using ns-2 reveal that TCP Prairie provides significant throughput performance improvement over TCP New-Reno and TCP Westwood under congestion and/or wireless loss scenarios. Also, compared to TCP Westwood, TCP Prairie is observed to be more friendly towards TCP New-Reno.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".