An infrastructure as a Service for Mobile Ad-hoc Cloud
Bibliographic record
Abstract
In this era of growing mobile device technology, the direction of growth is moving towards providing powerful computational capabilities and expanding memory in the device. Nevertheless, this growth has objectively put a lot of the device computational power to an unused state which calls for a better management of intra-device resources. Over a period of time, it has been studied that a mobile “edge-cloud” formed by these devices could be as productive or close to the productivity of the public cloud in terms of providing a service. However, the ease of access to this pool of devices is much more arbitrary and based purely on the needs of the user. This could categorically be summed as the building block of a cloud built for providing an infrastructure for various services that can be processed with volunteer node participation. This representation of cloud formation to engender a constellation of devices in turn providing a service is the basis for the concept of Mobile Ad-hoc Cloud Computing. In this manuscript, an Infrastructure as a Service paradigm in Mobile Ad-hoc Cloud Computing is delineated. A novel architecture for discovering a dedicated pool of devices and the dependencies it should satisfy while formation of this pool for computation is designed. Moreover, a peer-to-peer composition algorithm to form this dedicated resource pool is proposed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".