Toward a Unified Characterization of Mapping Algorithms in Cloud and MPSoC Environments Using a Literature-Based Approach
Bibliographic record
Abstract
To address the growing demand for high-quality multimedia applications on mobile devices, stronger smartphones based on multicore processors are becoming the mainstream. However, processing power is still behind the required resources for some multimedia applications such as new video coding standards. Mobile cloud computing is a solution for this concern. In order to efficiently distribute tasks onto an infrastructure of devices, a mapping algorithm is required. This algorithm should determine the suitable device in the cloud and should also allocate the corresponding core in the selected multicore device. Providing a suitable algorithm considering the two domains of cloud and Multiprocessor System on Chip (MPSoC) simultaneously is a challenge. Describing the mapping algorithms with a set of descriptive elements makes differentiation among them easier. Unification of these features on both domains results in simpler mapping algorithms. In this paper, we present a generic representation for the mapping algorithms in the MPSoC and cloud environments. Grounded Theory has been applied to identify the main features of the mapping algorithms. The obtained features are organized in more abstract categories based on their similarities. In order to evaluate the results, a few sample mapping algorithms from each of the cloud and MPSoC domains have been successfully characterized with the obtained features.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".