Guidance for Model-Based Systems Engineering on AWS
Overview
How it works
These technical details feature an architecture diagram to illustrate how to effectively use this solution. The architecture diagram shows the key components and their interactions, providing an overview of the architecture's structure and functionality step-by-step.
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.
Operational Excellence
Whether you are just starting out with MSBE as a tool or putting MSBE at the center of your enterprise strategy, this architecture provides the flexibility for you to get started. You can use Option A to incorporate MSBE into your existing environment or use tools like SOCA to centralize MSBE. The services in this architecture and the data lake approach enable centralized management and visibility for IT and security teams. Additionally, the architecture uses data analytics services to generate insights for engineering teams, so they can forecast how changes will impact larger systems.
Security
This architecture uses AWS Identity and Access Management (IAM) and Amazon CloudWatch to protect data. IAM provides role-based access control, giving data access privileges to only the roles that need it. With CloudWatch, you can set up metrics to monitor application activity from multiple AWS accounts within a Region.
Reliability
This architecture uses a microservices approach, which decouples services for a particular engineering function from services that support a different engineering function. By decoupling these services, you can experiment with new technologies for one function without altering the operability of other functions. The services in the Human-Machine Engineering Workflow capture, document, and respond to all events, maintaining a single “source of truth” for all event-based activity and communications.
Performance Efficiency
The services in this architecture allow for data interoperability across multiple stages of the data lifecycle. The AWS Management Console gives you visibility into data access patterns of your data, such as requests or changes to data and velocity or size of data. You can then build business logic based on traffic patterns and execute the logic with extensible APIs.
Cost Optimization
This architecture uses cost-saving features such as automation through CodePipeline, scalability through Amazon S3, and centralized administration through AWS Organizations. These features allow for early detection and correction of defects in the design process, which reduces total development costs and schedule overruns.
Sustainability
This architecture uses services that scale resources up and down based on usage. These services help monitor the throughput of the file system and dynamically adjust the throughput mode to “provisioned” or “bursting” to maximize resource optimization. With the “Detective” services in this architecture, you can visualize productivity metrics, emissions, or cost-out targets through dashboards and adjust business priorities to meet target metrics for sustainability.
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