Data Driven Deep Neural Network Based Task Offloading on Edge Cloud Continuum
Bibliographic record
Abstract
Edge computing reduces bandwidth bottlenecks and latency by performing computations close to end-users, making it a viable option for application task offloading. Owing to its limited computational capacity, the cloud may need to be considered with the edge for offloading. Task offloading is challenging, because of the availability of limited resources, dynamically changing network conditions, and concurrent user access. Mathematical task offloading approaches may be incapable of capturing dynamic network situations in large end-to-end network models. We propose D2-TONE (Data-driven Deep Neural Network Task Offloading on the Network Edge), an approach that employs Machine Learning (ML) algorithms to accurately estimate offloading costs, such as computation and transmission costs. D2-TONE adapts holistically to dynamic network situations, and provides optimal/near-optimal offloading solutions in real-time. In addition, D2-TONE employs the distributed execution of Deep Neural Network (DNN) training tasks on the edge-cloud continuum. Experiments revealed that D2-TONE reduces the training time by 1.55 to 2.77 times, compared to baseline approaches. In addition, the edge based D2-TONE offers an improvement of 55-76% in the data processing ratio, compared to other offloading approaches.
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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) | 0.001 | 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) | 0.003 | 0.001 |
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".