A PSO model with VM migration and transmission power control for low Service Delay in the multiple cloudlets ECC scenario
Bibliographic record
Abstract
Mobile devices are naturally limited due to their portable sizes and will therefore never be equal to their desktop counterparts. To overcome this, Edge Cloud Computing can be utilized to execute tasks on behalf of the devices, allowing them to run applications that would normally be too demanding. In this service model, it is important to maintain a low Service Delay to keep the service transparent to the user. This can be achieved by focusing on lowering the Transmission Delay and Processing Delay. While existing approaches in the literature focus on one of those two, we postulate that only when considering both delays you can efficiently lower Service Delay and provide quality to all applications. In order to do that while being feasible, we propose a method based on Particle Swarm Optimization for lowering Service Delay in Edge Cloud Computing. Our proposal is shown to be close to optimality while still maintaining a low execution time for multiple cloudlets scenarios. Moreover, our proposal outperforms existing approaches from the literature with single focus on computation or communication, even in situations with high processing and transmission burdens, proving the superiority of a dual focus approach.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".