BIER-MC: Multi-Connectivity Approach for Latency-Efficient Multicast Routing
Bibliographic record
Abstract
Integrating terrestrial networks together with non-terrestrial networks, such as Low Earth Orbit (LEO) satellites, can significantly enhance network reliability and reduce latency in unicast and multicast protocols. To use such a capability and reduce end-to-end latency, many transport-layer unicast protocols, such as Multipath TCP (MPTCP) and Multipath QUIC (MPQUIC), have adopted multi-connectivity (MC) by transmitting over multiple interfaces at the sender host. However, traditional and modern multicast protocols suppose that the sender host accesses the core network using a single path, leading to high end-to-end latency at destinations. In this regard, we first demonstrate in a real-world testbed that MC significantly improves network latency in multicasting compared to single-connectivity approaches. We then introduce a Bit Indexed Explicit Replication (BIER) Multi-Connectivity (BIER-MC) method designed to reduce end-to-end latency within the BIER protocol, as a modern multicast protocol. Our comparison indicates that BIER-MC outperforms traditional BIER implementations using multicast trees in terms of latency up to 5×, bandwidth usage up to 2.5×, and edge betweenness centrality up to 15×.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".