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- Guidance for Reducing Workbench Time and Cost on AWS
Guidance for Reducing Workbench Time and Cost on AWS
Overview
How it works
This architecture diagram shows how to run workbench test results and parameters on AWS so chip designers and contract manufactures can run test jobs and quickly analyze test results, saving both time and money.
Well-Architected Pillars
The architecture diagram above is an example of a Solution created with Well-Architected best practices in mind. To be fully Well-Architected, you should follow as many Well-Architected best practices as possible.
Lambda helps you scale your workbench testing process through serverless architecture. API Gateway helps you build reliable production workbench endpoints that allow for easy migration from one API to the next and the ability to connect with these endpoints from anywhere in the world.
By using AWS Virtual Private Network (AWS VPN), you can secure your connections when transferring data and also while using the AWS console. AWS VPN is a flexible way to create secure connections between on-premises hardware and AWS.
Data parsing is often the most compute-intensive step of this architecture diagram. By using Amazon EC2 Auto Scaling, you can scale compute resources as needed and automatically replace failed instances. This reduces the amount of time it takes to parse data so that spikes in demand or failed instances don’t interrupt your workloads.
Using AWS Glue, you can consolidate, optimize, and streamline workbench data. This helps you avoid lengthy delays and eliminate redundancies. AWS Glue requires little to no prior experience, so you can have this service up and running in minutes.
With Amazon EC2 Auto Scaling, workbench testing will only use what is required and will shut down compute resources when they are no longer needed, meaning you no longer have to pay for idle resources. Additionally, Amazon DocumentDB is a managed service, which helps you save on administrative and infrastructure costs traditionally associated with running your own database instances.
Rather than having idle resources using up power and cooling, you can reserve compute through Compute Savings Plans, so you use only what your workloads require. Additionally, using Lambda to run serverless functions helps you consume only a fraction of the compute resources typically required for similar workloads that run in on-premises environments.
Disclaimer
The sample code; software libraries; command line tools; proofs of concept; templates; or other related technology (including any of the foregoing that are provided by our personnel) is provided to you as AWS Content under the AWS Customer Agreement, or the relevant written agreement between you and AWS (whichever applies). You should not use this AWS Content in your production accounts, or on production or other critical data. You are responsible for testing, securing, and optimizing the AWS Content, such as sample code, as appropriate for production grade use based on your specific quality control practices and standards. Deploying AWS Content may incur AWS charges for creating or using AWS chargeable resources, such as running Amazon EC2 instances or using Amazon S3 storage.
References to third-party services or organizations in this Guidance do not imply an endorsement, sponsorship, or affiliation between Amazon or AWS and the third party. Guidance from AWS is a technical starting point, and you can customize your integration with third-party services when you deploy the architecture.
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