SDN-Based Service Function Chaining in Kubernetes Using Network Service Mesh with Kernel-type Network Interfaces for Industrial IoT
Bibliographic record
Abstract
The increasing adoption of Virtual Network Functions (VNFs) and Kubernetes for Industrial IoT applications has sparked interest in Service Function Chaining (SFC) deployment within Kubernetes environments. However, deploying SFC in Kubernetes requires a flexible and manageable network infrastructure, which Kubernetes alone cannot provide. Network Service Mesh (NSM) has emerged as a popular solution by offering layer 2 and 3 service mesh capabilities with various features tailored for Kubernetes clusters. Despite NSM’s support for SFC, it needs to meet the requirements for industrial IoT use cases due to its lack of integration with Software-Defined Networking (SDN) controllers and its incapacity to provide Kernel-type network interfaces for Network Function (NF) pods. This paper proposes a solution to address these limitations by integrating NSM with an SDN controller and enabling Kernel-type interfaces for NF pods. Our experimental results demonstrate that while the NSM-based solution is overshadowed by OvS-based SFC deployment regarding maximum throughput, latency, and packet loss, it offers valuable features such as ease of use and elimination of manual configuration requirements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".