A Web-based Orchestrator for Dynamic Service Function Chaining Development with Kubernetes
Bibliographic record
Abstract
The research community has been moving attention from Virtual Network Function (VNF) to Cloud-native Network Function (CNF) since cloudification has brought the Network Function Virtualization (NFV) to an advanced level. It has already been demonstrated that cloud-native technology brings high flexibility and efficiency to large-scale network service deployment compared to the traditional VNF with Virtual Machines (VMs). However, more work is needed to provide a flexible and reliable Service Function Chaining (SFC) development solution in a cloud-native environment. This paper proposes a web-based orchestrator system to deploy an SFC use case consisting of multiple CNFs in a multi-node Kubernetes cluster using Network Service Mesh (NSM). We demonstrate a cloud-native SFC framework that allows users to dynamically create container-based SFC rather than the traditional VMs with NFV/SDN controller approach. Further, additional work is presented with the support of an open-source monitoring system, Prometheus, to validate the SFC path.
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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".