An infrastructure as a Service for Mobile Ad-hoc Cloud
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 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.
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 | 0.001 | 0.001 |
| Open science | 0.002 | 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