Enhancing the Kubernetes Scheduler for Diverse Workloads in Large Clusters

Explore the challenges and solutions for enhancing the Kubernetes scheduler to handle diverse workloads in large-scale clusters. Dive into the design and implementation of custom scheduling plugins for performance optimization, pod placement, and group scheduling. Learn how to leverage the Kubernetes scheduling framework to create a custom scheduler that meets the needs of various workloads, from stateless services to machine learning applications. Discover techniques for balancing scheduling performance and quality using plugins and scheduling profiles. Gain insights into new features and enhancements of the scheduling framework, and understand the comparison between different methods of extending the vanilla scheduler. Examine case studies on gang scheduling for batch jobs and scalable scheduling, and learn about custom scheduling with multiple profiles and plugins.