ArcheScale-Guard: Archetype-Aware Predictive Autoscaling with Uncertainty Quantification for Serverless Computing

Serverless computing platforms face significant challenges in managing cold start latency while optimizing resource costs. Traditional reactive autoscaling policies fail to anticipate workload fluctuations, while single-model predictive approaches struggle with the heterogeneous nature of real-world workloads. We present ArcheScale-Guard, a two-stage predictive autoscaling framework that combines workload archetype classification with uncertainty-aware demand forecasting. In the first stage, we employ Dynamic Time Warping (DTW) based k-medoids clustering to identify distinct workload archetypes (bursty, periodic, gradual). In the second stage, archetype-specific quantile regression models provide probabilistic…