Head-of-Line Access Delay-Based Scheduling Algorithm for Flow-Level Dynamics
Bibliographic record
Abstract
Scheduling algorithm design is a critical and challenging issue in multiuser wireless networks with dynamic flows. The well-known Queue-length-based MaxWeight (QMW) scheduling algorithm can achieve throughput-optimality if there only exist persistent flows that are long-lived with infinite traffic arrival. In this paper, we propose a head-of-line access delay (HAD)-based scheduling algorithm and show that it is throughput-optimal when the flows are dynamic, i.e., they are short-lived with finite data to transmit. HAD is an online algorithm and does not require prior knowledge of the statistics of the arrival traffic and channel information. We also develop the Markov analytic model to study system performance and reveal important properties of the proposed HAD scheduling algorithm. To reduce the complexity of the analysis, we further study two approximation methods corresponding to different arrival traffic intensity. Performance evaluation shows that the HAD scheduling algorithm can outperform the classic QMW and stabilize the system at the presence of flow-level dynamics. Compared to the other existing algorithms, HAD is practical to implement with a better delay performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".