SDNQ: A Novel SDN Controller for the Management of IoT Video Flows in the Edge/Cloud Continuum
Bibliographic record
Abstract
Internet of Things (IoT) has been used to empower smart environments and applications in different domains. In some IoT systems, IoT cameras live stream video frames to edge or cloud servers to be processed by machine learning (ML) models. The processing of the video frames will aim to detect and recognize objects or other entities of interest. However, the processing of IoT video frames at cloud servers often relies on high latency due to network congestion and high load at the cloud servers. In this paper, we proposed the SDNQ controller, a software-defined networking (SDN) controller that uses a reinforcement learning (RL) agent to decide how to route and where to process video frames from IoT flows. The proposed solution considers the network and servers' status, the latency experienced by admitted video flows, and the video flows' latency requirements when deciding the routing path and the destination edge or cloud server to process a newly admitted IoT video flow. Numerical results show that the proposed solution outperforms related works at the cost of blocking a low fraction of IoT flows.
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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 | 0.000 |
| 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".