Overview

AWS brings solutions to the above challenges. You can benefit from:

• Scalability and Elasticity. The following technologies and solutions bring scalability object storage and databases, compute and supporting essential technologies that provide virtually unlimited space for your MBSE application. We focus on AWS Managed Services that provide dynamic scaling and minimize the management of your MBSE IT workloads. AWS provides both relational and non-relational, including graph databases and object storage. You can even bring serverless databases such as Aurora Serverless for MySQL or PostgreSQL, non-relational (key-value) as Amazon DynamoDB, graph native database such as Amazon Neptune or Amazon S3 as a data-lake with AWS Glue to catalog your model data or Amazon Kinesis for data warehouse. Since MBSE is a critical cornerstone, any technology backing MBSE must also be highly durable and reliable.

  Another aspect of scalability is the “scalability of engineering” attained by enhancing the interoperability of engineering tools, engineering collaboration, and managing the complexity that arises from multi-stakeholder environments and bringing low-latency applications online globally. You can more easily adopt new technologies to your MBSE - or augment value-delivering technologies such as AI/ML, HPC, Analytics - as a “plug-in” through APIs with the size desired of resources managed by AWS. This whitepaper also proposes augmenting micro-process level engineering collaboration based on events and messages as an extension to MBSE.

• Agility for IT and Engineering. You can bring agility not only to IT operations, but also to engineering operations. For the most, the agility offered by cloud to the IT operations might be apparent. On the other hand, the agility of the engineering through the cloud can be hidden. Indeed, agility is brought to you by automation, “experimentation” culture that automatically deploys and dismantles resources as desired, ease of engineering collaboration enabled by workflow management, transparency of operations based on events and messages and harnessing single source of data/information cataloged by AWS services. AWS’s data lake (we call it Artifact Store and Data Catalog for MBSE) can ease the file and data collaboration among the teams and suppliers where you can assign granular access management to relevant parties with desired privileges. AWS’s tools to catalog that data would further reduce the process of data query by engineers. Moreover, event-based architectures that we will be discussing would provide visibility to micro-level engineering transactions. This brings more transparency in real-time, which means fewer emails, presentations, and meetings – allowing for a focus on the engineering activities themselves. You can also bring Shared Services Platform (SSP) to centrally manage, govern and deploy necessary resources for your engineering and IT teams. SSP can also act as the basis for the all mentioned modules in this whitepaper. Similarly, you can have Service Catalog to create a selection of technologies that can be deployed on-demand. The technologies and approaches explained here opens Continuous Integration (CI) and Continuous Deployment (CD) capabilities for your organization.

• Interoperability and Flexibility. You can bring flexibility and interoperability to your existing MBSE and MBSE related functions. You can augment new capabilities now or over time, as the technology progresses, with a microservices approach. You can abstract data continuity layers and bring orchestration tools using AWS Application Integration Services to enhance the interoperability of your MBSE. You can employ a Digital Continuity Layer as discussed in the following sections. AWS breadth and depth of application integration services - mostly based on serverless technologies such as messaging, message broker services, queuing, workflow management, Restful APIs and GraphQL APIs, and more - can enable flexibility and interoperability for your MBSE solution. The additional flexibility supports hybrid architectures, where you can build solutions partially using the cloud. You can employ local AWS technologies in your offices/data centers or to the edge using AWS Outposts or build high-speed, reliable private connections between your design offices/datacenters/edge locations and AWS cloud such as AWS Storage Gateway and Amazon Direct Connect.

• Experiment More. With 200+ services in AWS, you can promote innovation through experimentation for your IT and engineering teams. The main idea is to minimize your time spent maintaining and managing infrastructure and repeatable activities. Solutions and services such as Shared Services Platform (SSP), Service Catalog, AWS CodePipeline can be used for custom build and on-demand and ephemeral resources for your teams to experiment with. With more than 400+ different types of chips as of August 2021 and automatic deployment, your engineering teams can also perform on-demand deployments for high-fidelity simulations requiring large compute power. Then you can bring automation to catalog that data and store the results in Artifact Store (discussed above). In this way, MBSE can also embrace high-fidelity simulations, which may not be possible with on-premises infrastructure or manual processes. The outcome is to increase efficiency and give you back more time to focus on value delivering activities such as innovation and experimentation. 

• MBSE in Global Scale.  230+ points of AWS presence serving 245+ countries and territories, and AWS global network backbone would help you go global in MBSE and MBSE related services to bring low latency applications. This is especially important for addressing data sovereignty, geographical access management, while providing low-latency MBSE service. Moreover, MBSE’s value in bringing usable models, product and process can be performed globally using read-replications, global tables/databases and primary/secondary connections and serverless IaS deployments. This means, you can keep your data in the primary location that you decide makes sense, while providing read-access or API aces optimized for the edge such as Amazon CloudFront, AWS Lambda@Edge or API Gateway optimized at the edge. In addition, you can use AWS Global Accelerator to employ static IPs always available globally, even though a fail-over happens in your MBSE backend.

• Extending MBSE to Edge. With more processing power embedded into the devices, vehicles and manufacturing, it becomes almost a necessity to extend existing (on-cloud or on-premises) technologies to the edge. this is especially important for traceability of changes from design to production or in the reverse order; production (ie. Defects or issues) to the design. MBSE would be fed by data representing the overall lifecycle of a product. Hence, MBSE extends to full product lifecycle which requires a native, reliable, offline mode capable, light weight processing power, storage, AI/ML and connection between your MBSE and the devices/vehicles/machines at the edge and the production lines. In addition, the design process would be extended to the production to speed up experimentation to bring in more agile practices or just react quickly to production defects or changes stemmed from either side. Hence, you may be looking for extending automation from design with MBSE to production. For example, you can automate 3D printing prototyping from the design to the edge and get feedback information from the production to the design team through AWS IoT.

• AI/ML in every layer of MBSE. We especially see significant value of NLP (Natural Language Processing) technologies in MBSE. For example, you can use Amazon Textract to automatically read your hand-written legacy documents/tables or Amazon Comprehend to establish formal semantics among models of disciplines for your ontology using Amazon Comprehend custom entities. Amazon Translate can be used to provide better international collaboration. The other level of AI/ML is through the Amazon Neptune ML. Since the graph database is dynamic, you can employ large variety of ML algorithms for networks such as finding patterns, anomaly detection, shortest distance, hub finding, unused branches and more. Since these data change in time, you can get insights of your operations, supply chain, design cycles and other processes. For example, Amazon Forecast can be used for such purpose. Finally, you can bring large amount of AI/ML algorithms using Amazon SageMaker AI. Especially, for the NLP, you can use Hugging Face with Amazon SageMaker AI. We believe the AI/ML part would make the different in MBSE applications and will be the future in this technology field.

• Serverless Technologies: Another key, promising technology to enhance MBSE services is serverless. This is due to the transactional operations of MBSE - based on changes, commits, simulation, overall CRUD activities. Hence, serverless can not only take over all of management from your shoulders, also bring cost effectiveness. As mentioned before, serverless is not limited to compute such as AWS Lambda, but databases, workflow managements, messaging, APIs, and more.

• Granular Access and Identity Management and Security: Our customers would like to enable agility with international, multi-supplier collaboration - all while ensuring that the data is not accessed outside of the defined setting and secured in transit and at-rest. Identity and Access management and Security is such a large and deep topic that this whitepaper provides high-level information regarding MBSE and provides links on this topic.

• Experience. Our customers come with different needs and targets regarding MBSE. Since 2006, AWS has helped millions of customers from diverse industries.