On Achieving Fair and Throughput-Optimal Scheduling for TCP Flows in Wireless Networks
Bibliographic record
Abstract
Throughput-optimal scheduling has been heavily investigated given its ability to fully utilize network resources and maintain network stability. Most of the existing throughput-optimal algorithms, including the classic queue-length based MaxWeight algorithm and flow-delay-based MaxWeight algorithm, however, may bring a severe unfairness problem when scheduling transmission control protocol (TCP) controlled flows. As TCP is the dominant transport layer protocol in the Internet and it controls the majority of Internet traffic, we study how to design the scheduling algorithm that can ensure both throughput optimality and be compatible to TCP flows. In this paper, we analyze the reason behind the incompatibility between the existing scheduling algorithms and TCP, and then investigate the properties of the head-of-line access delay-based scheduling algorithm (HOLD) we proposed. We prove that the proposed HOLD can fairly schedule TCP flows in wireless networks with time-varying channel conditions and achieve throughput optimality with flow-level dynamics. Simulations using OMNeT++ 4 have been conducted to validate our analytical results, and compare the performance of different scheduling algorithms comprehensively.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".