Managing Failures and Service Quality in the Context of NFV
Bibliographic record
Abstract
In the context of network function virtualization (NFV), virtual network functions (VNF) are the building blocks of network services (NS). VNFs are usually distributed applications composed of VNF components (VNFC). A VNFC instance is the actual consumer of resources and it is realized as a virtual machine (VM). Service availability of an NS is one of its important characteristics which has been expansively investigated in the literature. Fault tolerance is the main mechanism used to guarantee service availability. Fault tolerance relies on VNF redundancy and failover operation. It reduces the service outages when a complete failure of a VNF happens. However, complete failures are less frequent than partial failures in which only some VNFC instances of a VNF fail. Partial failures can cause service degradation and annoy tenants who usually expect a guaranteed service quality. To handle partial failures, the failover mechanism may not be ideal since it can cause a complete service outage. We, therefore, propose a solution to determine the redundancy of VNFs to guarantee the required quality of service for an NS and avoid service degradation below a defined level. We propose a framework that includes an architecture and operations to guarantee service quality and avoid potential service outage.
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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 | 0.003 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".