Design of Experiments-Based Adaptive Scheduling in Kubernetes for Performance and Cost Optimization

In a Kubernetes environment, the resource allocation for Pods has a direct impact on both performance and cost. When resource sizes are determined based on user experience, under-provisioning can lead to performance degradation and execution instability, while over-provisioning can result in resource waste and increased costs. To address these issues, this study proposes an adaptive scheduling method that employs the Design of Experiments (DoE) approach to determine the optimal resource size for each application with minimal experimentation and integrates the results into a custom Kubernetes scheduler. Experiments were conducted in a Kubernetes-based cloud environment using five applications with diverse workload characteristics, including CPU-intensive, memory-intensive, and AI inference workloads. The results show that the proposed method improved the performance score—calculated as the harmonic mean of execution time and cost—by an average of approximately 1.5 times (ranging from 1.15 to 1.59 times) compared with the conventional maximum resource allocation approach. Moreover, for all applications, the difference in mean scores before and after optimal resource allocation was statistically significant (p-value < 0.05). The proposed approach demonstrates scalability for achieving both resource efficiency and service-level agreement (SLA) compliance across various workload environments.