OSCAR-P and aMLLibrary: Performance Profiling and Prediction of Computing Continua Applications

Abstract

This paper proposes an auto-profiling tool for OSCAR, an open-source platform able to support serverless computing in cloud and edge environments. The tool, named OSCAR-P, is designed to automatically test a specified application workflow on different hardware and node combinations, obtaining relevant information on the execution time of the individual components. It then uses the collected data to build performance models using machine learning, making it possible to predict the performance of the application on unseen configurations. The preliminary evaluation of the performance models accuracy is promising, showing a mean absolute percentage error for extrapolation lower than 10%.