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

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.