Autoscaling GPU inference workloads in Kubernetes remains challenging due to the reactive and threshold-based nature of default mechanisms such as the Horizontal Pod Autoscaler (HPA), which struggle under dynamic and bursty traffic patterns and lack integration with GPU-level metrics. We present KIS-S, a unified framework that combines KISim, a GPU-aware Kubernetes Inference Simulator, with KIScaler, a Proximal Policy Optimization (PPO)-based autoscaler. KIScaler learns latency-aware and resource-efficient scaling policies entirely in simulation, and is directly deployed without retraining. Experiments across four traffic patterns show that KIScaler improves average reward by 75.2%, reduces P95 latency up to 6.7x over CPU baselines, and generalizes without retraining. Our work bridges the gap between reactive autoscaling and intelligent orchestration for scalable GPU-accelerated environments.
@article{arxiv.2507.07932,
title = {KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling},
author = {Guilin Zhang and Wulan Guo and Ziqi Tan and Qiang Guan and Hailong Jiang},
journal= {arXiv preprint arXiv:2507.07932},
year = {2025}
}