![]() Windows workloads containers can be large, resulting in container download and extraction taking longer than compute time. To learn more about Microsoft licensing options for Amazon ECS optimized AMIs, refer to the Licensing – Windows Server section in the AWS and Microsoft FAQ.Īmazon EC2-based clusters are well suited for batch workloads, allowing for more direct access to the underlying infrastructure – for example, if GPU access or access to instance storage or Amazon Elastic Block Store (EBS) is required. In this solution, we rely on Amazon ECS optimized Windows AMIs provided by AWS.įor more information on Windows AMIs, refer to the Microsoft Software Supplemental License for Windows Container Base Image. ![]() Individual tasks run independently and isolated from one another as Docker containers. We leverage Amazon ECS to provide the compute capacity for Windows batch jobs using Amazon Elastic Compute Cloud (Amazon EC2). The web UI user database can also be integrated with external identity providers and supports common enterprise authentication protocols. You can use this to run workflows, configure inputs, and monitor progress. Once a DAG is initialized and registered with Amazon MWAA, you can take a no-code approach towards running your Windows batch jobs. Examples include scaling up a cluster for subsequent parallel processing or scaling it down once an upstream batch job has completed. Next to running the actual batch compute jobs, tasks in a workflow can be used to perform actions on the underlying infrastructure. ![]() In the context of Airflow, workflows are defined using Direct Acyclic Graphs (DAGs) written in Python, which allows for flexible modeling of complex dependencies between tasks. For example, for a particular workflow, you may first need to pre-process the source data, then run simulations and aggregate the results. The following diagram shows a high-level overview of the solution with the individual layers described in more detail.įigure 1: Architectural high level design showing orchestration (MWAA), compute (ECS) and storage layer (S3) Orchestration layerĪmazon MWAA is used to orchestrate compute tasks and model dependencies between them. We will provide step-by-step instructions and AWS CloudFormation templates, allowing you to go hands-on and experiment with the setup and customize it to your needs. We will show you how to leverage Amazon Managed Workflows for Apache Airflow (Amazon MWAA) to orchestrate batch jobs with Amazon Elastic Container Service (Amazon ECS), which provides the compute runtime for Windows Containers. This blog post provides a reusable and general framework for running Windows Server batch processing workloads on AWS. ![]() As a result, they resort to custom-built solutions, which require significant upfront implementation and ongoing maintenance efforts. These users are not able to switch between operating systems easily because of software dependencies. Customers are now starting to leverage the cloud to modernize and automate batch workflows involving Microsoft Windows Server. Traditionally, batch processing has been a domain of the Linux operating systems, which is natively supported by AWS services, such as AWS Batch and AWS ParallelCluster. These outputs can be used to produce simulation results, analyze large datasets, train AI/ML models, or render digital media content. Customers use batch processing as a non-interactive way of computation to calculate outputs. Cloud computing provides elastic on-demand access to large amounts of computing resources and enables economically efficient and technically flexible solutions naturally suited for computing at scale.īatch processing is a requirement for many scale-out computing solutions. Computing at-scale solutions is required in many industries and domains.
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