A scalable multicast routing protocol for building shortest path trees
Bibliographic record
Abstract
Abstract Scalability is a great concern in the design of multicast routing protocols for the global Internet. Building shortest path trees (SPT) is currently one of the most widely used approaches to supporting multicast routing because of the simplicity and low per‐destination cost of such trees. However, the construction of an SPT typically involves high protocol overhead, which leads to the scalability problem as the number of concurrent multicast sessions increases. In this paper, we present a destination‐initiated shortest path tree (DSPT) routing protocol. The design objective is to effectively reduce the protocol overhead associated with SPT constructions for providing scalable multicast. To achieve this objective, we introduce destination‐initiated joining operations in constructing SPTs. With DSPT, each router receiving a request to join a specific multicast group makes a local decision on selecting its parent node through which it connects to the existing tree. A source‐rooted SPT is built as a result of such collaborative operations at nodes. DSPT requires only limited routing information at routers. Analytical results demonstrate that DSPT scales well with respect to computation, storage and communication overhead when the number of concurrent multicast requests is large. Simulation experiments are also conducted to verify the correctness of the theoretically deduced analytical results. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".