Runtime Availability and Service Continuity of Containerized VNF Instances
Bibliographic record
Abstract
A Virtual Network Function (VNF) is a software implementation of a network function (e.g., firewall, router, etc.), which can be deployed as Virtual Machines (VM) utilizing the resources of the infrastructure managed by the Network Function Virtualization (NFV) Management and Orchestration (MANO). A VNF can also be containerized to benefit from the lower virtualization overhead of containers. A containerized VNF is composed of one or more Managed Container Infrastructure Objects (MCIO). To design a Network Service (NS) for the NFV framework that satisfies required service availability and continuity requirements, one has first to determine the availability, failure rate, and service disruption of the VNFs composing the NS. These characteristics of a VNF depend on the availability and failure rate of its MCIOs and the underlying infrastructure. Solutions proposed to determine these VNF characteristics at design time, consider the estimated resource availability and failure rate of a given infrastructure. However, resources assigned to VNF instances at runtime can be different in characteristics and/or performance resulting in a difference between the estimated and the actual characteristics of the VNF instances. In addition, scaling and different placements of the MCIO instances at runtime can have similar effects. Therefore, in this paper, we investigate the parameters affecting the availability, failure rate, and service disruption of a containerized VNF instance, considering the internal redundancy of the VNF and the scalability of its instances at runtime. Based on this investigation, we propose analytical models to determine the availability, failure rate, and service disruption of a containerized VNF instance.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".