Performance modeling in FaaS workloads at the edge using machine learning

Abstract The Function as a Service (FaaS) paradigm has emerged as a compelling architectural model for both cloud and edge computing environments, enabling the execution of self-contained functions triggered by specific events while abstracting from developers infrastructure management complexities such as load balancing and auto-scaling. In FaaS-enabled clusters, particularly within resource-constrained edge environments, precise resource consumption estimation becomes critical to optimize resource utilization, minimize latency, prevent system overloads, and ensure scalability. This paper addresses performance modeling challenges in FaaS-enabled distributed and decentralized edge computing systems, operating at the granularity level of both nodes and individual functions. We propose 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. Moreover, our approach introduces a profiling methodology that characterizes serverless functions according to their resource consumption profiles, thereby enabling accurate prediction of node-level resource demands without requiring detailed knowledge of individual deployed functions. Experimental validation demonstrates that our predictive models achieve 97% accuracy in anticipating node overload scenarios, providing a robust foundation for proactive resource management in edge-based FaaS deployments. Moreover, while our best individual, function-based regression models predict node-level CPU, RAM and power consumption with a Mean Absolute Percentage Error below 9% on average, our experiments highlight the effectiveness of function profiling and cluster-based modeling. On one hand a novel multi-target regressor based on a permutation-invariant neural architecture is proved to generalize effectively across previously unseen workload compositions in the tested scenarios, with $$R^2$$ scores ranging from 0.94 to 0.98, i.e., aligned with the performance of specialized single-target regression models. On the other hand, cluster-level models generalize effectively to previously unseen functions within the same usage class, maintaining prediction errors within practically acceptable ranges and often in the single-digit percentage range.