Distributed delay constrained multicast routing algorithm with efficient fault recovery
Bibliographic record
Abstract
Abstract Existing distributed delay constrained multicast routing algorithms construct a multicast tree in a sequential fashion and need to be restarted when failures occur during the multicast tree construction phase or during an on‐going multicast session. This article proposes an efficient distributed delay constrained multicast routing algorithm that constructs a multicast tree in a concurrent fashion by taking advantage of the concurrency in the underlying distributed computation. The proposed algorithm has a message complexity of O( m n ) and time complexity of O( n ) in the worst case, where m is the number of destinations and n is the number of nodes in the network. It constructs multicast trees with the same tree costs as the ones constructed by well‐known algorithms such as DKPP and DSHP while utilizing 409 to 1734 times fewer messages and 56 to 364 times less time than these algorithms under comparable success rate ratios. The proposed algorithm has been augmented with a fault recovery mechanism that efficiently constructs a multicast tree when failures occur during the tree construction phase and recovers from any failure in the multicast tree during an on‐going multicast session without interrupting the running traffic on the unaffected portion of the tree. © 2005 Wiley Periodicals, Inc. NETWORKS, Vol. 47(1), 37–51 2006
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".