A Multi-User Tasks Offloading Scheme for Integrated Edge-Fog-Cloud Computing Environments
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
This paper presents a multi-user, multi-class and multi-layer edge computing-based framework for effective task offloading and computation processes. Important system requirements that were not captured in the existing multi-layer solutions such as offloading, computations and deadline requirements were captured in the system modeling, while both wireless communications and task computation constraints were considered. We considered three layers system, where each device offloads its generated tasks in each time slot to any selected layer for computation. On its arrival at such a selected layer, the task is only accepted if the queue size is below the pre-defined threshold, otherwise, such a task is offloaded to the next layer. Tasks were classified into class 1 and class 2 tasks following tasks’ quality of service requirements. We adopted stochastic geometry, parallel computing and queueing theory techniques to model the performance of the considered integrated edge-fog-cloud computing environment and obtained analysis for various performance metrics of interest. The obtained analyses demonstrate the importance of multi-layer and multi-class edge computing systems towards improving the experience of both delay-sensitive and mission-critical applications in any task offloading environment.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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