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Kubernetes historically focused on service-type workloads, support for load balancing, rolling-updates, spreading and autoscaling are few examples of features the community built for service workloads. While support for Batch workloads lagged in Kubernetes core, recent progress has been made to make Kubernetes a native home for batch workloads including major feature and scalability enhancements to the Job API and the establishments of the batch working group. This panel will discuss what is still missing in core k8s for batch support, what functionalities do we need to push upstream, and what should continue to be loosely defined so that we don't impose specific semantics on how batch jobs should run on k8s.
Abdullah is a staff software engineer at Google and sig-scheduling and working group batch co-chair. He works on Kubernetes and Google Kubernetes Engine, focusing on scheduling and batch workloads.
Wilfred is a Principal Software Engineer with Cloudera, based in Melbourne Australia. He has worked as a software engineer for more than 25 years. Involved in multiple open source projects for over 10 years. Tech lead of the Apache YuniKorn project. He is an Apache YuniKorn PMC member... Read More →
Computer Scientist, Lawrence Livermore National Laboratory
Daniel Milroy is a Computer Scientist at the Center for Applied Scientific Computing at the Lawrence Livermore National Laboratory. His research focuses on graph-based scheduling and resource representation and management for high performance computing (HPC) and cloud converged environments... Read More →
Diana is a Senior Software Engineer at IBM T. J. Watson Research Center focusing on developing cutting-edge resource management solutions for a large set of system platforms ranging from web server applications to virtual machines orchestration and now cloud container platforms. She... Read More →
Monday October 24, 2022 4:00pm - 4:40pm EDT
Room 252 AB
