Overview
The Scale-Out Computing on AWS solution helps customers deploy and operate a multiuser environment for computationally intensive workflows, such as computer-aided engineering (CAE). The solution features a large selection of compute resources, a fast network backbone, unlimited storage, and budget and cost management directly integrated within AWS.
Benefits
Scale-Out Computing on AWS deploys and sets up an example UI with a common set of APIs that the administrator and users can use to interact with their Amazon EC2 cluster.
The solution leverages Desktop Cloud Visualization (DCV) graphical sessions to help users easily access the cluster to perform any pre- and post-processing visualization actions.
Schedulers and application logs are ingested in real-time and stored in the data lake for further processing.
The solution is deployed with a collection of scripts that are customizable and can be extended to help users collect data and run common cluster tasks.
Technical details
This solution allows you to deploy the AWS CloudFormation template using a custom installer in your hosted repository for production environments. The CloudFormation template deploys the following architecture consisting of eight components:
Step 1
Amazon EC2 Auto Scaling to automatically provision the resources necessary to run cluster user tasks such as scale-out compute jobs.
Step 2
This solution also deploys Amazon Elastic File System (Amazon EFS) for persistent storage, Amazon Simple Storage Service (Amazon S3) for persistent logs, and optional parallel file system Amazon FSx for Lustre.
Step 3
At its core, the Amazon Elastic Compute Cloud (Amazon EC2) instance implements a scheduler, which dynamically provisions AWS resources required for jobs submitted by users. The scheduler instance also hosts web interface which allows users and administrators to interact with the environment.
Step 4
Launch a 2D or 3D Workstation that uses NICE Desktop Cloud Visualization (DCV), that can be used to submit batch jobs and run GUI tools.
Step 5
Security services and resources that are used include AWS Secrets Manager, AWS Certificate Manager, Security Groups, and AWS Identity and Access Management (IAM).
Step 6
AWS Lambda functions to verify the required prerequisites and create a default signed certificate for an Application Load Balancer (ALB) to manage access to DCV workstation sessions.
Step 7
An Amazon OpenSearch Service cluster to store job and host information.
Step 8
Elastic Load Balancing is used to ensure accessibility across Availability Zones, and Cost Allocation Tags are used with AWS Cost Explorer.
Step 1
Amazon EC2 Auto Scaling to automatically provision the resources necessary to run cluster user tasks such as scale-out compute jobs.
Step 2
This solution also deploys Amazon Elastic File System (Amazon EFS) for persistent storage, Amazon Simple Storage Service (Amazon S3) for persistent logs, and optional parallel file system Amazon FSx for Lustre.
Step 3
At its core, the Amazon Elastic Compute Cloud (Amazon EC2) instance implements a scheduler, which dynamically provisions AWS resources required for jobs submitted by users. The scheduler instance also hosts web interface which allows users and administrators to interact with the environment.
Step 4
Launch a 2D or 3D Workstation that uses NICE Desktop Cloud Visualization (DCV), that can be used to submit batch jobs and run GUI tools.
Step 5
Security services and resources that are used include AWS Secrets Manager, AWS Certificate Manager, Security Groups, and AWS Identity and Access Management (IAM).
Step 6
AWS Lambda functions to verify the required prerequisites and create a default signed certificate for an Application Load Balancer (ALB) to manage access to DCV workstation sessions.
Step 7
An Amazon OpenSearch Service cluster to store job and host information.
Step 8
Elastic Load Balancing is used to ensure accessibility across Availability Zones, and Cost Allocation Tags are used with AWS Cost Explorer.
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