Parallel Computing at the Extreme Edge: Spatiotemporal Analysis
Bibliographic record
Abstract
Multi-access Edge Computing (MEC) is a revolutionary computing paradigm that facilitates delay-sensitive and/or data-intensive applications associated with the Internet of Things (IoT). Harvesting copious yet underutilized computational resources of the Extreme Edge Devices (EEDs) is foreseen as a promising endeavor. Such EEDs offer a unique opportunity to bring the computing service closer to IoT devices to curtail delay. However, the efficacy of extreme-edge parallel computing paradigm is profoundly impacted by i) wireless device-to-device communication performance, that is required for task offloading; and ii) computing capabilities of the EEDs, that governs the execution time of each task. In this context, we propose a novel spatiotemporal framework that employs stochastic geometry and continuous time Markov chains to jointly analyze the interwoven communication and computation performance of extreme edge computing systems. Based on the incorporated framework, we study the influence of various system parameters on the task response delay. Our findings reveal the existence of an optimal number of EEDs that need to be recruited in order to minimize the task response delay. Moreover, we show that in some cases, our model can outperform the normal MEC offloading systems.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".