A general purpose cell sequencer/scheduler for ATM switches
Bibliographic record
Abstract
Groups of cells, such as cells belonging to different priority levels, that are all placed in one queue, can be identified by using labels or tags to distinguish them from each other. We describe a buffering device called a sequencer, which can distinguish logical queues within the same physical queue, and at the same time can successfully schedule the service among these logical queues. Scheduling the service among cells, VCs, or groups of cells in ATM switches is necessary to provide guaranteed QoS for each connection which is a major goal of ATM networks. The proposed sequencer is quite flexible and can realize different scheduling algorithms in different levels, including per VC scheduling. The sequencer can operate in real time and at very high speeds. It has a simple and modular architecture and can be implemented in a single chip. The site of the buffer can be increased simply by cascading several sequencers. The sequencer can be used as a traffic shaper, input buffer, output buffer, or a queue controller of RAM-based switches.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".