Archetype-Aware Predictive Autoscaling with Uncertainty Quantification for Serverless Workloads on Kubernetes

Abstract: High-performance extreme computing (HPEC) platforms increasingly adopt serverless paradigms, yet face challenges in efficiently managing highly dynamic workloads while maintaining service-level objectives (SLOs). We propose AAPA, an archetype-aware predictive autoscaling system that leverages weak supervision to automatically classify 300\,000\,+ workload windows into four archetypes (PERIODIC, SPIKE, RAMP, STATIONARY_NOISY) with 99.8\% accuracy. Evaluation on publicly available Azure Functions traces shows that AAPA reduces SLO violations by up to 50\%, improves response time by 40\%, albeit with a 2--8\,$\times$ increase in resource cost under spike-heavy loads.