A Novel SDN-enabled Edge Computing Load Balancing Scheme for IoT Video Analytics
Bibliographic record
Abstract
Edge computing has been designed to deploy resources in the proximity of IoT devices, which reduces latency and network overhead. Nevertheless, resources on edge servers are limited and must efficiently be managed. In this paper, we propose a novel software-defined networking (SDN)-based scheme to balance the computation resource requests among a network of edge servers aimed at supporting IoT video analytics streaming applications. In the proposed solution, programmable switches periodically report the IoT video streaming workload forwarded to each edge server. This information is then used at the SDN controller to estimate the incoming and outgoing traffic load at edge servers and balance IoT video streaming among them, by updating routing tables at the programmable switches. The performance of the proposed solution is evaluated and compared to related schemes through extensive simulations using the Mininet emulator. Obtained results show that the proposed solution can reduce up to 21 % of average latency with 20 % load saving in each edge server, compared to deterministic and random-based related solutions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".