Abstract
Cloud and Fog technologies are steadily gaining momentum and popularity in the research and industry circles. Both communities are wondering about the resource usage. The present work aims to predict the resource usage of a machine learning application in an edge environment, utilizing Raspberry Pies. It investigates various experimental setups and machine learning methods that are acting as benchmarks, allowing us to compare the accuracy of each setup. We propose a prediction model that leverages the time series characteristics of resource utilization employing an LSTM Recurrent Neural Network (LSTM-RNN). To conclude to a close to optimal LSTM-RNN architecture we use a genetic algorithm. For the experimental evaluation we used a real dataset constructed by training a well known model in Raspberry Pies3. The results encourage us for the applicability of our method.
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Acknowledgements
This work is part of the ACCORDION project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement “No 871793”.
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Violos, J., Psomakelis, E., Danopoulos, D., Tsanakas, S., Varvarigou, T. (2020). Using LSTM Neural Networks as Resource Utilization Predictors: The Case of Training Deep Learning Models on the Edge. In: Djemame, K., Altmann, J., Bañares, J.Á., Agmon Ben-Yehuda, O., Stankovski, V., Tuffin, B. (eds) Economics of Grids, Clouds, Systems, and Services. GECON 2020. Lecture Notes in Computer Science(), vol 12441. Springer, Cham. https://doi.org/10.1007/978-3-030-63058-4_6
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