An SLO-driven and cost-aware autoscaling framework for Kubernetes

This is the authors' abstract. We don't add key points for this paper.

Kubernetes provides native autoscaling mechanisms, including the Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA), and node-level autoscalers, to enable elastic resource management for cloud-native applications. However, production environments frequently experience Service Level Objective (SLO) violations and cost inefficiencies due to reactive scaling behavior, limited use of application-level signals, and opaque control logic. This paper investigates how Kubernetes autoscaling can be enhanced using AI-Ops principles to jointly satisfy SLO and cost constraints under diverse workload patterns without compromising safety or operational transparency. We present a gap-driven analysis of existing autoscaling approaches and propose a safe and explainable multi-signal autoscaling framework that integrates SLOaware and cost-conscious control with lightweight demand forecasting. Experimental evaluation using representative microservice and event-driven workloads shows that the proposed approach reduces SLO violation duration by up to 31%, improves scaling response time by 24%, and lowers infrastructure cost by 18% compared to default and tuned Kubernetes autoscaling baselines, while maintaining stable and auditable control behavior. These results demonstrate that AI-Ops–driven, SLOfirst autoscaling can significantly improve the reliability, efficiency, and operational trustworthiness of Kubernetes-based cloud platforms.