Haste: Practical Online Network Coding in a Multicast Switch
Bibliographic record
Abstract
The use of network coding has been shown to improve throughput in input-queued multicast switches, but not without costs of computational complexity and delays. In this paper, we investigate the design of efficient online network coding algorithms in a switch with multicast traffic. We present Haste, an online opportunistic coding algorithm designed to streamline the computation when network coding is involved in a network switch with multicast traffic. Haste enjoys the advantage of incurring no decoding delays, which reduces packet delays compared with existing network coding algorithms on switches. We have conducted extensive simulations to show the efficiency of Haste, and implemented an emulation framework to emulate input-queued switches using asynchronous network sockets. Our emulation framework is able to process actual UDP traffic using Haste with online network coding, and to show convincing evidence that Haste is suitable for practical use, and is beneficial in multicast switches.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 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".