DRUDGE: Dynamic Resource Usage Data Generation for Extreme Edge Devices
Bibliographic record
Abstract
Extreme Edge Computing (EEC) can drastically curtail the delay, reduce network bandwidth consumption, and enhance system performance by providing computing resources closer to the data-generating Internet of Things (IoT) devices. However, the use of Extreme Edge Devices (EEDs) in EEC presents unique challenges imposed by the inherent dynamic user-access behavior, which introduces highly dynamic resource usage. To tackle such challenges, it is crucial to enable accurate resource usage predictions, which in turn requires having reliable datasets. In this paper, we cultivate the Dynamic Resource Usage Data Generation for EEDs (DRUDGE) methodology. DRUDGE generates datasets that capture the resource usage dynamics of EEDs running diverse user-end applications in fine-grained intervals over extended periods. We present an in-depth characterization of resource utilization in EEDs and make the datasets publicly available to the research community. We examine the temporal variation of critical system metrics, such as CPU usage, memory usage, temperature, and network traffic. Furthermore, we apply various statistical tests to gain valuable insights into the data characteristics, including skewness, kurtosis, stationarity, volatility, cointegration, multi-collinearity, Granger causality, and Pearson correlation analysis. These insights inform model selection, feature engineering, and preprocessing techniques, leading to more accurate and reliable forecasts and analyses for EEC systems.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".