# AAPA: An Archetype-Aware Predictive Autoscaler with Uncertainty Quantification for Serverless Workloads on Kubernetes

arXiv: https://arxiv.org/abs/2507.05653 · HTML: https://mlsanity.com/paper/2507.05653/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2025.

## Built on by (most cited first, 3 of 3)

- [When Does Deep RL Beat Calibrated Baselines? A Benchmark Study on Adaptive Resource Control](https://mlsanity.com/paper/2605.26418/) (2026)
- [EvalStop: Using World Feedback to Detect and Correct Reward Overoptimization in Multi-Tenant RLHF Platforms](https://mlsanity.com/paper/2606.04145/) (2026)
- [Winning by Peeking: Unenforced Budgets and Test-Set Selection Inflate Short-Budget AutoML Comparisons](https://mlsanity.com/paper/2608.07303/) (2026)

## Abstract (arXiv)

Serverless platforms such as Kubernetes are increasingly adopted in high-performance computing, yet autoscaling remains challenging under highly dynamic and heterogeneous workloads. Existing approaches often rely on uniform reactive policies or unconditioned predictive models, ignoring both workload semantics and prediction uncertainty. We present AAPA, an archetype-aware predictive autoscaler that classifies workloads into four behavioral patterns -- SPIKE, PERIODIC, RAMP, and STATIONARY -- and applies tailored scaling strategies with confidence-based adjustments. To support reproducible evaluation, we release AAPAset, a weakly labeled dataset of 300,000 Azure Functions workload windows spanning diverse patterns. AAPA reduces SLO violations by up to 50% and lowers latency by 40% compared to Kubernetes HPA, albeit at 2-8x higher resource usage under spike-dominated conditions. To assess trade-offs, we propose the Resource Efficiency Index (REI), a unified metric balancing performance, cost, and scaling smoothness. Our results demonstrate the importance of modeling workload heterogeneity and uncertainty in autoscaling design.