Abstract
Academic research institutions are at a precipice. They have historically been constrained to supporting classic “job” style workloads. With the growth of new workflow practices such as streaming data, science gateways, and more “dynamic” research using lambda-like functions, they must now support a variety of workloads.
In this talk, Lindsey and Bob will discuss some difficulties faced by academic institutions and how Kubernetes offers an extensible solution to support the future of research. They will present a selection of projects currently benefiting from Kubernetes enabled tools, like Argo, Kubeflow, and kube-batch. These workflows will be demonstrated using specific examples from two large research institutions: Compute Canada, Canada’s national computation research consortium and the University of Michigan, one of the largest public Universities in the United States.
