Energy-Efficient Resource Allocation for D2D-Assisted Fog Computing
Bibliographic record
Abstract
In this paper, we address the problem of energy-efficient resource allocation in a multi-device D2D-assisted fog computing scenario, where the goal is to minimize the total energy consumption subject to constraints on the transmit powers, computation resources and task processing times. The considered problem is non-convex and finding its global optimum is generally intractable; hence we propose two sub-optimal approaches to solve it. First, by investigating the relationship between the task processing time and the total energy consumption, we show how the original problem can be relaxed into a sequence of convex subproblems whose solutions can be efficiently obtained via standard algorithms. Second, to further reduce computational complexity, we propose a low-complexity heuristic resource allocation strategy which does not require calculating gradients and the Hessian matrices in the solution process. We also develop a lower bound on the total energy consumption for the considered task offloading scenario as a benchmark for comparison purpose. Computer simulations under a wide range of conditions and parameter settings show that both methods achieve a near-optimal solution in comparison to the lower bound.
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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) | 0.001 | 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 | 0.001 | 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".