Bibliographic record
Abstract
VC merging allows VC to be mapped onto the same VC label. It provides a scalable solution for network growth. It enables features such as stream merging in MPLS and RSVP wildcard filter to be efficiently supported over ATM. It also plays an important role in providing multipoint-to-multipoint multicast and share-tree multicasting paradigm. VC merging involves distinguishing cells from an identical merged VC label. Various approaches have been proposed to help this identification process. However, most of them incur additional buffering, protocol overhead and/or variable delay. They make the provision of QoS difficult to achieve. In this paper, we propose a novel merge-capable scheduler to support VC merging. The proposed scheduler consists of a core scheduler and a number of sub-queue sequencers forming a 2-level hierarchical structure. Using the proposed merge-capable scheduler, we can uniquely identify incoming cells while: (1) allowing cell cut-through forwarding and interleaving; (2) maintaining per-flow QoS; and (3) requiring no additional buffer and protocol overhead. We show analytically that the scheduler can guarantee a worst-case fairness and deliver a worst-case delay bound. We further analyze the scheduler performance in term of the service received by a queue and the average queuing delay experienced by a cell. The results indicate that the amount of services is fairly distributed among merging and non-merging flows according to their reservations. Moreover, the proposed merge-capable scheduler performs equally as well as its non-merging counterpart when reservations are highly utilized, even though it may introduce a minimal additional delay when utilization is low.
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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 | 0.001 |
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".