🤖 AI Summary
Kubernetes’ native autoscaling mechanisms—relying on reactive decision-making, underutilizing application-layer signals, and employing opaque control logic—frequently violate SLOs and waste resources. To address these limitations, we propose an AIOps-driven, multi-signal collaborative autoscaling framework that jointly optimizes for SLO compliance, cost constraints, and lightweight time-series demand forecasting. This work establishes the first SLO-first, cost-aware, and safety-guaranteed unified control paradigm with inherent interpretability. By integrating multidimensional metrics into a unified model and embedding a closed-loop feedback controller, our approach ensures transparent scaling decisions and auditable operational traces. Experimental evaluation demonstrates a 31% reduction in SLO violation duration, a 24% acceleration in scaling responsiveness, and an 18% decrease in infrastructure costs—all while preserving system stability and full operational traceability.
📝 Abstract
Kubernetes provides native autoscaling mechanisms, including the Horizontal Pod Autoscaler, Vertical Pod Autoscaler, and node-level autoscalers, to enable elastic resource management for cloud-native applications. However, production environments frequently experience Service Level Objective 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 AIOps 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 SLO-aware 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 percent, improves scaling response time by 24 percent, and lowers infrastructure cost by 18 percent compared to default and tuned Kubernetes autoscaling baselines, while maintaining stable and auditable control behavior. These results demonstrate that AIOps-driven, SLO-first autoscaling can significantly improve the reliability, efficiency, and operational trustworthiness of Kubernetes-based cloud platforms.