Performance Measurement of IoT Traffic Through SRv6 Network Programming
Bibliographic record
Abstract
Segment Routing over IPv6 (SRv6) is a modern networking technology for source routing that is envisioned to achieve high reliability, availability, and scalability of current IoT networks. Based on the IoT use-case scenario, traffic requires processing at both the edge and the cloud, and, thus it must be forwarded through the service provider’s core networks. Using SRv6, IoT traffic can be forwarded over IPv6 without additional protocol overhead. However, performance measures are still needed to evaluate SRv6 behaviors or functions. To this end, the goal of this paper is to develop a realistic IoT traffic profile and use it to measure the performance of SRv6 behaviors. In particular, an SRv6 programming model is proposed that consists of three modules: Orchestrator, System Under Test (SUT), and Traffic Generator (TG). The proposed model considers IoT traffic forwarding through core and ensures reliability and quality of service (QoS). Then, different benchmarking methodologies, i.e., no-drop rate (NDR), partial drop rate (PDR), and maximum receive rate (MRR) are implemented to measure the performance of SRv6 policy headend and endpoint behaviors. Finally, a novel finder algorithm for MRR is proposed to automate the MRR benchmarking in the Linux kernel deployment. Implementation results are used to provide insights on the use of SRv6 behaviors to forward different IoT applications traffic based on the functional service requirements.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.008 | 0.002 |
| 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 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".