Merging engine implementation for intra-frame sharing in multi-tenant virtual passive optical networks
Bibliographic record
Abstract
High up-front capital expenditures impede the widespread deployment of passive optical networks (PONs). A multi-tenant solution, in which multiple network operators virtually share PON infrastructure and bandwidth resources, can result in significant cost savings. First, we investigate the viability of virtual PONs, in which virtual network operators share a portion of the upstream bandwidth in a frame. Each PON schedules its frame-level capacity using a dedicated dynamic bandwidth assignment algorithm, resulting in the generation of an independent bandwidth map (BMap). Second, we implement at the optical line terminal a merging engine that combines individual virtual BMaps into a single physical BMap, which is then transmitted to all optical network units. Our novel traffic merging algorithm is capable of consolidating traffic across all transmission containers. We apply this merging engine on top of the 10-Gbit-capable PON module and validate our approach using network simulator 3. The results show that our proposed intra-frame sharing technique, when integrated with our traffic merging algorithm, significantly outperforms the standard inter-frame sharing approach in terms of both latency and packet loss. Furthermore, we observed that best-effort traffic is affected most for all resource constraint scenarios of intra-frame sharing.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".