An energy optimizing scheduler for mobile cloud computing environments
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In mobile cloud computing, mobile devices seek to minimize computation time and/or energy consumption based on task related or user defined constraints. In earlier work [1], we proposed to minimize the total energy consumption across all the mobile devices in a cyber foraging system using a scheduler that runs in a centralized broker node, in situations where a large number of mobile devices could be expected. In this paper, we extend our earlier task scheduling problem for a large number of mobile devices to a mobile cloud computing environment. We optimally solve the task scheduling problem for task assignment to minimize the total energy consumption across the mobile devices subject to user defined constraints. Our task scheduler model at the centralized broker optimally offloads tasks and provides significant reduction in energy consumption compared to the energy consumption when tasks are offloaded from the centralized scheduler without optimization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it