SLO-Driven Horizontal Container Autoscaling

Abstract

Modern web services are required to meet critical non-functional requirements, including availability, responsiveness, scalability, and reliability, which are formalized through Service Level Agreements (SLAs). SLAs define Service Level Objectives (SLOs), such as latency, throughput, and uptime, that ensure consistent service quality. Failing to meet these objectives can incur penalties and harm a provider's reputation. At the same time, over-provisioning resources leads to unnecessary costs and inefficient utilization. Autoscaling mechanisms address this by dynamically adjusting the number of service replicas according to demand. However, conventional approaches typically rely on low-level metrics, such as CPU or memory usage, which limit the ability to optimize both SLO compliance and infrastructure costs. This paper presents an enhanced SLO-driven autoscaling methodology for containerized workloads in Kubernetes clusters, integrating response time SLO targets into the autoscaling process. The proposed approach improves decision-making over traditional autoscaling by balancing service-level performance with operational efficiency. Experimental evaluation of a prototype demonstrates clear benefits compared to the default Kubernetes Horizontal Pod Autoscaler.