Energy-aware task allocation over MANETs
Bibliographic record
Abstract
Since the emergence of mobile computing, reducing energy consumption of battery-operated computing devices has become a very active research area. The widespread popularity of mobile computing devices, such as laptops, handheld devices and cell phones, motivates this research area. Several hardware based techniques have been proposed; this has led to more energy-efficient systems. Nevertheless, it is presumed in the literature of energy-aware design that software based techniques have the potential to reduce energy demand and contribute to solve the problem. In this paper, we look into the problem of distributing computational tasks amongst a set of mobile computing devices in a mobile wireless ad hoc network (MANET) in such a way that reduces the total consumed energy. In such a distributed environment, the assignment of computational tasks to different devices plays a vital role in energy conservation. The main contributions of this paper are formulating a novel energy-aware allocation problem and proposing a heuristic-based greedy algorithm to solve it approximately. Our allocation algorithm assigns a set of computational tasks, which may communicate with each other, into a set of heterogeneous processors in such a way that minimizes the total consumed energy. Experiments show that our algorithm is near-optimal in most of the tested benchmarks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".