TCP with Adaptive Delay and Loss Response for Heterogeneous Networks
Bibliographic record
Abstract
Long propagation delays and high bit error rates in heterogeneous networks with geostationary earth orbit (GEO) satellite links have negative impact on the performance of Transmission Control Protocol (TCP). In this paper, we propose modifications to TCP by introducing adaptive delay and loss response (TCP-ADaLR) to mitigate the adverse effects of satellite link characteristics. The proposed modifications incorporate delayed acknowledgment (ACK) recommended for Internet hosts. TCP-ADaLR introduces adaptive window increase and loss recovery mechanisms to address TCP performance degradation in satellite networks. We evaluate and compare the performance of TCP-ADaLR, TCP SACK, and TCP NewReno, with delayed ACK enabled and disabled. In the absence of losses, TCP-ADaLR exhibits the shortest user-perceived latency for HTTP and FTP applications. In the presence of only congestion losses, TCP-ADaLR shows comparable performance to TCP SACK and TCP NewReno. In the presence of only error losses, TCP-ADaLR exhibits improvements up to 61 % and 76 % in throughput and utilization, respectively. In the presence of both congestion and error losses, TCP-ADaLR exhibits goodput and throughput improvements up to 43%. TCP-ADaLR exhibits the best performance in the absence of losses and in the presence of losses due to both congestion and errors. It also friendly to TCP NewReno, exhibits better fairness, and maintains TCP end-to-end semantics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".