Preemptive bandwidth allocation protocol for multicast, multi-streams environments
Bibliographic record
Abstract
In this paper, we present a protocol that allocates resources in communication networks in order to assure specific QoS characteristics as requested by new connections. The design takes into consideration the possibility for the network allocation to adapt to application requirements.The proposed protocol uses a Bandwidth Preemptive Algorithm that permits adaptive bandwidth allocation in multicast, multi-stream environments. This design has been inspired by the one proposed by Sakate [1] where a centralized methodology is used. In our approach, we use a distributed methodology where we change the behavior of the communication service and allow the continuation of the service under more severe conditions. In other words, when there is a lack of bandwidth for a new connection, the communication service will try to find the missing bandwidth within the existent connections (or streams) when looking for a feasible path on a hop-by-hop basis, starting from the destination to an a on-tree node.
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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 | 0.000 | 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".