An SLO-Driven Feedback Controller for Kubernetes Horizontal Pod Autoscaling

Abstract

Modern web services are expected to meet key non-functional requirements—such as availability, responsiveness, scalability, and reliability—typically formalized through Service Level Agreements (SLAs). These agreements specify Service Level Objectives (SLOs), including metrics such as latency, throughput, and uptime, to guarantee consistent service quality. Failure to meet these targets can result in financial penalties and reputational damage, while excessive resource provisioning leads to wasted costs and inefficiencies.Autoscaling techniques help address this challenge by dynamically adjusting the number of service replicas based on demand. However, traditional autoscaling methods mainly depend on low-level indicators such as CPU and memory usage, limiting their effectiveness in balancing SLO compliance with cost efficiency. This paper introduces an enhanced feedback-control-based autoscaling approach for containerized applications, specifically tailored for Kubernetes environments, which directly incorporates response time SLOs into scaling decisions. By doing so, the proposed method enhances scaling accuracy and achieves a better trade-off between performance and resource utilization. Experimental results from a prototype implementation show significant improvements over the standard Kubernetes Horizontal Pod Autoscaler and another autoscaling methodology proposed in literature.