Mobile Cloud Computing: Offloading Mobile Processing to the Cloud
Bibliographic record
Abstract
The current proliferation of mobile systems, such as smart phones and tablets, has let to their adoption as the primary computing platforms for many users. This trend suggests that designers will continue to aim towards the convergence of functionality on a single mobile device (such as phone + mp3 player + camera + Web browser + GPS + mobile apps + sensors). However, this conjunction penalizes the mobile system both with respect to computational resources such as processor speed, memory consumption, disk capacity, and in weight, size, ergonomics and the component most important to users, battery life. Therefore, energy consumption and response time are major concerns when executing complex algorithms on mobile devices because they require significant resources to solve intricate problems. Offloading mobile processing is an excellent solution to augment mobile capabilities by migrating computation to powerful infrastructures. Current cloud computing environments for performing complex and data intensive computation remotely are likely to be an excellent solution for offloading computation and data processing from mobile devices restricted by reduced resources. This research uses cloud computing as processing platform for intensive-computation workloads while measuring energy consumption and response times on a Samsung Galaxy S5 Android mobile phone running Android 4.1OS.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".