Bibliographic record
Abstract
Mobile Cloud-Computing (MCC) is a term introduced by Marc Baccue in 2009 20 that popularized the idea of using cloud-hosted components as a means to overcome the resource-constraints of mobile devices. But as the smartphones and tablets overcame their resource-constraints, the meaning of the term MCC changed. Nowadays MCC is mainly associated with using mobile devices to engage cloud-hosted services and to a lesser extend with combining multiple mobile devices (e.g. cloud of devices). However, as the number of users with multiple mobile devices increases there is a growing demand for enabling apps on mobile devices to share hardware and software resources. This in turn leads to questions regarding decentralized interaction, coordination and resource sharing among multiple mobile devices. This paper focusses on the “horizontal scalability” of apps e.g. the ability to combine multiple mobile devices (executing the same mobile app) into a single compute environment that utilizes all available hardware and software resources in a decentralized manner. One possible approach to achieve this is by designing mobile apps as sets of RESTful micro-services and to allow these services to communicate via low-bandwidth IoT communication protocols. This paper presents the results of our performance evaluations using RESTful micro-services on mobile devices that communicate via the IoT protocol CoAP in different WIFI environments.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".