Dynamic Worker Availability Prediction at the Extreme Edge

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.