Optimization of device selection in a Mobile Ad-hoc cloud based on composition score
Bibliographic record
Abstract
While tapping onto the mobile device capabilities for execution of resource intensive tasks, it has been proven by many studies that the local device resources are unable to completely perform the task execution. Therefore, it becomes imperative that the intra-device resources are put to productive use by offloading the jobs to a remote location for execution. However, in those settings where the network infrastructure is either expensive or inconvenient to use, the traditional cloud would be beyond reachability. This gave rise to the novel “on-the-fly” forms of computing that enables a computation environment closest to the user like a Mobile Ad-hoc Cloud (MAC). Nevertheless, by following this strategy there are more complex unprecedented problems such as constant device movements, disruptions in the external device to name a few that needs to be addressed. Hence, this paper draws attention to the task scheduling process in an MAC. We propose a linear Programming based model to minimize the number of devices participating in an ad-hoc cloud composition by delineating major constraints. In doing so, it is our endeavor to provide a faster ad-hoc cloud composition formation, which leads to quicker task execution in an MAC.
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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 | 0.000 | 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 | 0.000 | 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 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".