Enhancing Kubernetes Auto-Scaling: Leveraging Metrics for Improved Workload Performance

Kubernetes is an open-source production-grade container orchestration platform, that can enable high availability and scalability for various types of workloads. Maximizing the performance and reducing the cost are two major challenges modern applications encounter. To achieve this, resource management and proactively deploying resources to meet specific application requirements becomes utmost important. Adopting Kubernetes auto-scaler to fit one’s needs are important to maximize the performance. This study aims to perform a comprehensive analysis of Kubernetes auto-scaling policies. This paper also lists out the various parameters considered for auto-scaling, and prediction methods used to efficiently handle resource requirements of applications. The research findings reveal a scarcity in the existing work regarding the variety of workload based auto-scaling and custom metrics. This paper provides a concise overview of a forthcoming research endeavor that explores the utilization of custom metrics in conjunction with auto-scaling.