A Column Generation Algorithm for Dedicated-Protection O-RAN VNF Deployment
Bibliographic record
Abstract
The Open Radio Access Network (O-RAN) architecture brings openness, intelligence, and virtualization to RANs, allowing multi-vendor existence, achieving economics of scale, and enabling intelligent management and orchestration. O-RAN components such as near real-time RAN Intelligent Controllers (RICs), O-RAN Central Units (O-CUs), and O-RAN Distributed Unit (O-DUs) can be considered as virtual network functions hosted on the O-Cloud. This virtualization allows network service providers to disaggregate O-RAN functions from their hard-ware, enabling dynamic instantiation of services and reducing their capital and operating costs. However, with openness and virtualization, availability guarantees become more difficult to maintain as the network is now prone to both software and hardware failures. In this paper, we investigate a decomposition model for the design of reliable 0-RAN deployment under a dedicated virtual network function (VNF)-protection scheme. The proposed model maximizes the network's yearly availability by providing a placement decision for all 0-RAN VNFs and their backup instances. The model is solved by a column generation algorithm making it a scalable algorithm for large-scale 0-RAN deployments. Extensive computational results show that the algorithm can produce ε-optimal solutions with negligible ε (less than 0.1%) in reasonable computational times. These results significantly enlarge the exact solutions of the state-of-the-art algorithms for this problem.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".