Joint Multicast and RAN Virtual Function Deployment in O-Cloud Environment
Bibliographic record
Abstract
Despite its flexible and interoperable design with open standards and virtualization, the Open Radio Access Network (O-RAN) architecture still lacks support for advanced 5G services in the multi-cloud environment to enable efficient and customizable cellular network functions. Enabling multicasting supports in O-RAN, such as 3GPP’s 5G Multicast Broadcast Service capabilities, will unlock and improve a multitude of new O-RAN use cases. Unfortunately, such integration has not yet been clearly defined in O-RAN specifications or fully investigated in prior studies. In this paper, we jointly formulate the Multicast Service Function Chain Embedding (MSE) and O-RAN functional split optimization problems in a multi-cloud environment as an Integer Linear Programming (ILP) model. We propose a heuristic algorithm with polynomial-time complexity based on feasible search optimization and dynamic programming techniques to solve this high-complexity problem. Our experimental results demonstrate that the proposed algorithm outperforms state-of-the-art baselines, approximating the optimal solution while significantly reducing computational time.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".