RUMP: Resource Usage Multi-Step Prediction in Extreme Edge Computing

Abstract

Extreme Edge Computing that leverages the copious yet underutilized computational resources of Extreme Edge Devices (EEDs) has gained significant momentum lately. Estimating the computational capabilities of EEDs can be strenuously challenging since EEDs are user-owned devices, and are thus subject to a highly dynamic user access behavior (i.e., dynamic resource usage). In this paper, we propose the Resource Usage Multi-step Prediction (RUMP) scheme. RUMP is the first scheme that strives to enable multistep-ahead prediction of the dynamic resource usage of EEDs (i.e., workers) in a computationally efficient way, while providing a relatively high prediction accuracy. Towards that end, RUMP exploits the use of the Hierarchical Dirichlet Process-Hidden Semi-Markov Model (HDP-HSMM). In addition, RUMP uses the Simple and Exponential Moving Average (SMA&EMA) and Savitzky-Golay (SG) filters to improve the prediction accuracy. We scrupulously study the trade-off between computational efficiency, prediction accuracy, practicality, and adaptability of the underlying prediction model by conducting complexity analysis, performing various experiments on a testbed of heterogeneous workers, and comparing the HDP-HSMM model used in RUMP to three other prominent prediction models in different dynamic resource usage scenarios. Extensive evaluations show that RUMP achieves a 91% categorical multi-step prediction accuracy and renders a small performance gap of 6% on average in terms of the Mean Absolute Error (MAE) compared to representatives of state-of-the-art prediction models, while yielding a low computational complexity.