Service Function Chaining Implementation using VNFs and CNFs
Bibliographic record
Abstract
The increasing popularity of cloud-based platforms has led to the emergence of two prominent open-source solutions: OpenStack and Kubernetes. OpenStack facilitates computing and networking resources through virtual machine instances, while Kubernetes excels in container orchestration, managing containerized workloads and services. This paper explores the implementation of Service Function Chaining (SFC) using a combination of virtual machines in OpenStack and containers in Kubernetes. The primary focus of our study is to analyze the performance of containers within the context of chain deployment in Kubernetes. Our experimentation reveals compelling insights into container bootup times, showcasing their efficiency when compared to virtual machines. Additionally, we meticulously evaluate the impact of integrating SFC-related interfaces into pods forming a chain, particularly assessing CPU, memory, and bandwidth utilization. Our findings underline the advantages of utilizing containers in SFC deployment scenarios and shed light on the potential overhead that arises during interface integration.
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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".