BM-ALM: An Application Layer Multicasting with Behavior Monitoring Approach
Bibliographic record
Abstract
IP multicasting is the most efficient way to perform group data distribution, as it eliminates traffic redundancy and improves bandwidth utilization. Application layer multicast (ALM) has been proposed to overcome some of the limitations in IP multicasting such as scalability and deployability. Limited computing power, scarcity of bandwidth and end-host's reluctance to share bandwidth make ALM difficult to spread. In this paper, we keep eye to those problems and present an ALM that scrutinizes the commitment of the ALM nodes. Failure to provide quality of service agreement triggers performance penalty for the node in concern. Thus, every node has an obligation to its descendants; as a result, a nice collaboration among the end-hosts is achieved for group communication. It has good performance for content distribution, as it reflects physical network topology onto the overlay network constructed by the end-hosts. Tree refinement and backup path strategies are taken to better satisfy heterogeneous QoS requirements
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".