Michele Ciavotta

Performance modeling in FaaS workloads at the edge using machine learning

This paper proposes a Machine Learning-based framework designed to predict key performance indicators, including CPU utilization, memory, and energy consumption, based on incoming workload patterns, while simultaneously forecasting potential system overload conditions, and introduces a profiling methodology that characterizes serverless functions according to their resource consumption profiles.