CASMaT: Characteristic-Aware SFC Mapping for Telesurgery Systems in Cloud-Edge Continuum
Bibliographic record
Abstract
Network Function Virtualization (NFV) empowers Internet Service Providers (ISPs) to place Virtual Network Functions (VNFs) efficiently in order to enhance the network performance without incurring a high cost. In this environment, Service Function Chains (SFCs) always need to steer the traffic through a sequence of VNF instances. Therefore, ISPs must adopt a suitable SFC embedding strategy to bolster their revenue. However, existing VNF placement and chaining methodologies harbour unrealistic assumptions, as they tackle the mapping problem from a generic standpoint, overlooking the distinctive characteristics of the constituent VNFs within a chain. Hence, they are not efficient when the strict requirements of life-critical applications, such as telesurgery, need to be satisfied. In this paper, taking into account the strict requirements of telesurgery-like systems, we present a cost-efficient characteristic-aware SFC mapping method for telesurgery Systems in the Cloud-Edge continuum. We formulate this problem as a Binary Linear Programming (BLP) model to embed SFC requests at minimal cost. Also, we propose an innovative heuristic algorithm that allocates each VNF based on its distinctive characteristics. Simulation results demonstrate that taking the characteristics of the VNFs into account when addressing the placement problem improves the system performance notably.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".