An archetype-aware autoscaler that tailors Kubernetes scaling to four workload shapes, with confidence-based hedging, cuts SLO violations up to 50% in simulation at 2 to 8 times the resource cost.
Harmonizing efficiency and practicability: optimizing resource utilization in serverless computing with jiagu,
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AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes
An archetype-aware autoscaler that tailors Kubernetes scaling to four workload shapes, with confidence-based hedging, cuts SLO violations up to 50% in simulation at 2 to 8 times the resource cost.