On the feasibility of service composition in a long-reach PON backhaul
Bibliographic record
Abstract
As network architectures are continuously evolving and being integrated with new technologies to meet the demands and requirements of future applications, many new possibilities and challenges emerge. Additionally, the evolution of user devices has brought other new possibilities, where devices in the same vicinity can offer and exchange services with each other. A vision that has led to developing various service discovery and composition models that aim to satisfy different constraints while ensuring a better quality of service. In this paper, we consider service discovery and composition in an optical access network, serving as a backhaul for a wireless front-haul, and examine how it can be greatly affected by the underlying bandwidth allocation scheme. We compare the performances of centralized and decentralized-based service compositions and study their side effects on upstream traffic. Numerical results demonstrate how decentralized allocation can be much better suited for supporting such service models and associated traffic in terms of both service delays and side effects on regular upstream traffic.
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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 | 0.001 |
| 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".