A Fast Class-of-Service Packet Scheduling for Ethernet Passive Optical Networks
Bibliographic record
Abstract
The mainstream dynamic bandwidth allocation (DBA) architectures for Ethernet Passive Optical Networks (EPONs) have employed two independent scheduling mechanisms in order to support quality of service: inter-ONU scheduling (timeslot assignment) and intra-ONU scheduling (priority queuing). These architectures tend to implement the inter-ONU scheduling function at the OLT, whereas the intra-ONU scheduling function at the individual ONUs. Since these scheduling functions have been separated, these architectures cannot generally yield a globally optimized bandwidth allocation. In this paper, a centralized bandwidth allocation model is proposed that implements both the scheduling functions at the OLT. A credit pooling technique is employed that enables the OLT to partition the upstream bandwidth among different class of service queues, and to prevent ONUs from monopolizing the bandwidth. High network utilization is achieved by embedding ONU scheduling decisions in time and by eliminating the channel idle-time overhead, associated with many earlier DBA schemes.
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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 | 0.001 | 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".