Decentralized Edge Intelligence-Driven Network Resource Orchestration Mechanism
Bibliographic record
Abstract
With the development of artificial intelligence of things (AIoT), multi-access edge computing (MEC) becomes a key enabler to migrate cloud services to edge clients. In comparison to traditional cloud computing techniques, MEC is characterized with low transmission latency, good flexibility, adaptability and robustness. Nevertheless, traditional resource allocation methods are difficult to meet the requirements of achieving a ubiquitous, pervasive, and intelligent computation offloading strategy in high-dynamic network environments. In this article, we construct an edge intelligence-enabled cloud-edge-client collaborative network structure, and conceive a model-aided multi-agent deep deterministic policy gradient (MA2DDPG) computation offloading framework relying on both centralized training and distributed execution. Simulation results corroborate that our proposed decentralized resource orchestration platform significantly reduces the energy consumption and the transmission latency against state-of-the-art methods. Finally, we highlight open challenges and potential solutions.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".