DRUDGE: Dynamic Resource Usage Data Generation for Extreme Edge Devices

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.