Scheduling for Optimal File-Transfer Delay using Chunked Random Linear Network Coding Broadcast
Bibliographic record
Abstract
We study the broadcast transmission of a single file to an arbitrary number of receivers using Random Linear Network Coding (RLNC) in a network with unreliable channels. Due to the increased computational complexity of the decoding process (especially for large files), we apply chunked RLNC (i.e., RLNC is applied within non-overlapping subsets of the file). In our work, we show the optimality of the Least Received (LR) batch scheduling policy with regards to the expected file transfer completion time. The LR policy strives to keep the receiver queues balanced . This is done by transmitting packets (corresponding to encoded batches) that are needed by the receivers with the shortest queues of successfully received packets. Furthermore, we provide formulas for the expected time for the file transmission to all receivers using the LR batch scheduling policy and the minimum achievable coding window size in the case of a pre-defined delay constraint. Moreover, we evaluate through simulations a modification of the LR policy in a more realistic system setting with reduced feedback from the receivers. Finally, we provide an initial analysis and further modifications to the LR policy for time-correlated channels and asymmetric channels.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".