Introducing Seekable OCI Parallel Pull mode for Amazon EKS

Introducing Seekable OCI Parallel Pull mode for Amazon EKS Introducing Parallel Pull mode for the SOCI snapshotter Understanding image pulls SOCI Parallel Pull details Performance consideration SOCI Parallel Pull in action Tuning configuration Benchmark Getting started with SOCI Parallel Pull Mode About the authors Containerization has transformed how customers build and deploy modern cloud native applications, offering unparalleled benefits in portability, scalability, and operational efficiency. Containers provide integrated dependency management and enable a standard distribution and deployment model for any workload. With Amazon Elastic Kubernetes Service (Amazon EKS), Kubernetes has emerged as a go-to solution for customers running large-scale containerized workloads that need to efficiently scale to meet evolving needs. However, one persistent challenge continues to impact specific deployment and scaling aspects of Kubernetes workload operations. Container image pulls, particularly when working with large and complex container images, can directly impact the responsiveness and agility of your systems. With the growth of AI/ML workloads, where we see particularly large images, this directly impacts operations as images may take several minutes to pull and prepare. In our recent Under the Hood post for EKS Ultra Scale Clusters, we briefly touched on our evolving solution for this problem, Seekable OCI (SOCI) Parallel Pull. In this post, we’ll explain how container image pulls work and how they impact deployment and scaling operations, we’ll dive deeper into how SOCI parallel pull works, and finally show how it can help you improve image pull performance with your workloads on Amazon EKS. As average container image sizes have grown in recent years, container startup performance has become a critical element of modern cloud native system performance. Image pull and preparation can account for more than 75% of total startup time for new and scaling workloads. This challenge is particularly acute with the rise of AI/ML workloads on Amazon EKS. These workloads have driven significant growth in container image sizes, where images are commonly tens of gigabytes in size.