Orchestrating network function virtualization platform: Migration or re-instantiation?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Network function virtualization (NFV) provokes the evolution of network functions to overcome various challenges facing the network service providers (NSPs). To exploit the advantages of the virtualization technology, NFV platforms should use the cloud environment to provide their services. Typically, an NFV service is represented by a service function chain (SFC) that consists of multiple virtualized network functions (VNFs). Hosting and orchestrating these VNFs in a cloud environment are challenging tasks. In this paper, we discuss the VNF orchestration problem from the perspective of VNF's migration and re-instantiation mechanism to achieve carrier grade-aware NFV services in a cloud-based platform. This paper also provides detailed insights on the NFV system modeling, building blocks, and various challenges hindering its cloud adoption. Also, a novel mixed integer linear programming (MILP) optimization model is proposed as a solution to facilitate the NFV platform orchestration in a cloud environment. The model decides between triggering either VNF's migration or re instantiation while achieving minimal downtime of the VNF, satisfying carrier grade requirements, and finding an optimal placement for the migrated or re-instantiated VNF that minimizes the SFC delays. The proposed model is compared to two availability-agnostic greedy algorithms. The simulation results show that finding an optimized decision whether to migrate or re-instantiate a VNF while associating it with an optimal placement can achieve a minimal VNF's downtime and SFCs delays and can enhance the quality of service accordingly.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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