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.
A. Marchese, O. Tomarchio· International Conference on...· 0 citations
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.
A. Marchese, O. Tomarchio· IEEE International Conferenc...· 0 citations
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