Dynamic Worker Availability Prediction at the Extreme Edge
Bibliographic record
Abstract
Leveraging the copious yet underutilized computational resources of end devices, also known as Extreme Edge Devices (EEDs), can significantly enhance the performance of various Internet of Things (IoT) applications. However, EEDs are heterogeneous and user-owned devices, which causes their availability to be highly unreliable. In this paper, we propose the Dynamic Worker Availability Prediction (DWAP) scheme. DWAP is the first scheme that predicts the availability of EEDs (i.e., workers) and adapts to the highly dynamic computing environment at the extreme edge. DWAP employs the Continuous-Time Markov Model (CTMC) to forecast the availability of workers in the upcoming time step. It does so while continuously fine-tuning the model parameters to incorporate newly available data. We use a dataset that consists of real-world Google cluster workload data traces. Extensive evaluations show that DWAP significantly outperforms a representative of state-of-the-art prediction schemes by up to 74% and 59% in terms of the Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), respectively. In addition, DWAP yields 97% and 48% reduction in task drop rate compared to prominent availability-unaware and availability-based resource allocation schemes, respectively.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".