Network Slicing for Edge-Cloud Orchestrated Networks via Online Convex Optimization
Bibliographic record
Abstract
In this paper, we study a network slicing framework for edge-cloud orchestrated networks. The framework integrates multi-access edge computing (MEC), allowing devices to offload computation-intensive tasks to Radio Access Networks (RAN) at the edge, with the flexibility to process tasks in the cloud based on resource availability and cost. In order to make workload distribution and resource allocation decisions for the edge and cloud resources, an Online Convex Optimization (OCO) approach is tailored. This approach leverages predictions to make the resource allocation and workload distribution decisions. The algorithm aims to optimize the long-term system cost while satisfying the Quality of Service (QoS) constraints, particularly in terms of minimizing delays. The proposed model encompasses various costs, including the costs for edge and cloud computing resources, communication resources, delay violations, and slice reconfiguration. Through simulations, we demonstrate the efficacy of the algorithm in reducing the costs associated with network slicing.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".