Reliability-Oriented and Resource-Efficient Service Function Chain Construction and Backup
Why this work is in the frame
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Bibliographic record
Abstract
In the network function virtualization (NFV) environment, network services are usually provided in the form of service function chains (SFCs), which defines the link order of virtual network functions required in service requests and are mapped to the physical network. Although NFV facilitates the flexible provision of network services, service interruptions may occur as a result of software and hardware failures. Current solutions mostly use the backup method to ensure the reliability of SFCs. However, these methods ignore the SFC construction phase that has an impact on reliability. Besides, the resource efficiency still requires improvement. To address these issues, reliability-oriented SFC construction and backup problems are investigated in this work. First, an instance-sharing and reliable construction algorithm (ISRCA) is proposed to aggregate multiple SFCs into a service function graph (SFG), and perform reliability screening for the SFG set. After mapping the SFG to the physical network, a node-ranking algorithm with centrality and reliability (NRCR) is proposed for backup node selection and backup instance deployment to improve the reliability of SFCs that have not met the requirements. Experimental results demonstrate that under the premise of ensuring reliability, the proposed backup method can reduce the consumption of bandwidth resources by about 11.7%, when combined with the proposed construction method, it can further reduce the backup resources by 13.9%.
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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