VNF Placement and Dynamic NUMA Node Selection Through Core Consolidation at the Edge and Cloud
Why this work is in the frame
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Bibliographic record
Abstract
The recent networking trends driven primarily by the different virtualization technologies, such as Network Function Virtualization (NFV) and Service Function Chaining (SFC) pave the way for next-generation network services. In the 5G and beyond era, such services usually have strict delay requirements and the wider adoption of the distribution of their computational needs across the Edge-to-Cloud continuum is certainly a step in the right direction. However, the majority of the optimization solutions for placing the virtualized services so far focus on server selection, leaving other areas such as the impact of Non-Uniform Memory Access (NUMA) and CPU core selection underexplored. In this work, we herein formulate the problem of placing services as SFCs on an Edge/Cloud infrastructure, as a Mixed Integer Programming (MIP) problem. Then, we propose a heuristic algorithm called “Dynamic numa node Selection through Cores consolidation – DySCo" to solve it, which optimizes the placement in terms of server, NUMA and core selection. To the best of our knowledge, this is the first attempt to optimize network service placement in an Edge-Cloud interplay. Extensive simulation evaluation shows that DySCo is able to perform close to optimal while finding a solution in a real time fashion. Compared to a mix of baselines and modified solutions from the literature to treat this new problem, DySCo reduces on average the deployment cost by 17.53% and the delay by 28.88% for a given SFC.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it