Tensor-Based Lyapunov Deep Neural Networks Offloading Control Strategy With Cloud–Fog–Edge Orchestration
Bibliographic record
Abstract
Using DNN (Deep Neural Networks) models to obtain high Quality of Services (QoS) through the cloud has become increasingly popular nowadays. Users want to use DNN by their edges (such as smartphones) anytime and anywhere. For most small and medium-sized enterprises, cloud computing resources are limited. Temporary exhaustion of resources may cause obvious service delay. For users, if all tasks are done locally, the battery capacity of the edge is too small to support such huge computing tasks. To remove this contradiction, we propose a Tensor-based Lyapunov DNN Offloading Control (TLDOC) strategy. First, we offload DNN computational tasks to cloud-fog-edge from an overall perspective. That is, layers in DNN are considered as basic offloadable objects. Second, we provide an original tensor-based Lyapunov equation and the entire process is derived in the tensor space. Lastly, we consider more key limiting factors (e.g., cellular data and remaining edge energy) to achieve better QoS. Via above contributions, our strategy reduces service delay and energy consumption for DNN cloud-fog-edge orchestration. Our experiments include two parts - detail and comparison. The experimental detail verifies that TLDOC strategy is practical and stable. Compared experiments show that our strategy could provide better QoS than existing methods on efficiency and energy saving.
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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) | 0.001 | 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".