From the details provided in the case study, we can quickly see several factors that will influence architecture decisions.
The company has customers in multiple countries, and reducing latency to customers is a priority. This calls for a multiregional deployment of services, which will also help address disaster recovery requirements. Depending on storage requirements, multiregional Cloud Storage may be needed. If a relational database is required to span regions, then Cloud Spanner may become part of the solution.
EHR Healthcare is already using Kubernetes, so Kubernetes Engine will likely be used. Depending on the level of control they need over Kubernetes, they may be able to reduce operations costs by using Autopilot mode of Kubernetes instead of Standard mode.
The company uses Microsoft Active Directory to manage identities, so you may want to use Cloud Identity with Active Directory as an identity provider (IdP) for federating identities.
To improve deployments of multiple environments, you should treat infrastructure as code using Cloud Deployment Manager or Terraform. Cloud Build, Cloud Source Repository, and Artifact Registry are key to supporting an agile continuous integration/continuous delivery.
Current logging and monitoring are insufficient given the problems with outages and ignored alert messages. Engineers may be experiencing alert fatigue caused by too many alerts that either are false positives or provide insufficient information to help resolve the incident. Cloud Monitoring and Cloud Logging will likely be included in a solution.
Helicopter Racing League
The Helicopter Racing League case study describes a global sports provider specializing in helicopter racing at regional and worldwide scales. The company streams races around the world. In addition, it provides race predictions throughout the race.
Business and Technical Considerations
The company wants to increase its use of managed artificial intelligence (AI) and machine learning (ML) services as well as serving content closer to racing fans.
The Helicopter Racing League runs its services in a public cloud provider, and initial video recording and editing is performed in the field and then uploaded to the cloud for additional processing on virtual machines. The company has truck-mounted mobile data centers deployed to race sites. An object storage system is used to store content. The deep learning platform TensorFlow is used for predictions, and it runs on VMs in the cloud.
The company is focused on expanding the use of predictive analytics and reducing latency to those watching the race. They are particularly interested in predictions about race results, mechanical failures, and crowd sentiment. They would also like to increase the telemetry data collected during races. Operational complexity should be minimized while still ensuring compliance with relevant regulations.
Specific technical requirements include increasing prediction accuracy, reducing latency for viewers, increasing post-editing video processing performance, and providing additional analytics and data mart services.
Architecture Considerations
The emphasis on AI and ML makes the Helicopter Racing League a candidate for Vertex AI services. Since they are using TensorFlow, performance may be improved using GPUs or TPUs to build machine learning models.
Improving the accuracy of predictive models will likely require additional data or larger ML models, possibly both. Cloud Pub/Sub is ideal for ingesting large volumes of telemetry data. Services can run in Kubernetes Engine with appropriate scaling configurations and using a Google Cloud global load balancer. The Helicopter Racing League should consider adopting MLOps practices, including automated CI/CD for ML pipelines, such as Vertex Pipelines.
The league has racing fans across the globe, and latency is a key consideration, so Premium Tier network services should be used over the lower-performance Standard Network Tier. Cloud CDN can be used for high-performance edge caching of recorded content to meet latency requirements.
BigQuery would be a good option for deploying data marts and supporting analytics since it scales well and is fully managed.
Mountkirk Games
The Mountkirk Games case study is about a developer of online, multiplayer games for mobile devices. It has migrated on-premises workloads to Google Cloud. It is creating a game that will enable hundreds of players to play in geospecific digital arenas. The game will include a real-time leader board.
Business and Technical Considerations
The game will be deployed on Google Kubernetes Engine (GKE) using a global load balancer along with a multiregion Cloud Spanner cluster. Some existing games that were migrated to Google Cloud are running on virtual machines although they will be eventually migrated to GKE. Popular legacy games are isolated in their own projects in the resource hierarchy while those with less traffic have been consolidated into one project.
Business sponsors of the game want to support multiple gaming devices in multiple geographic regions in a way that scales to meet demand. Server-side GPU processing will be used to render graphics that can be used on multiple platforms. Latency and costs should be minimized, and the company prefers to use managed services and pooled resources.
Structured game activity logs should be stored for analysis in the future. Mountkirk Games will be making frequent changes and want to be able to rapidly deploy new features and bug fixes.
Architecture Considerations
Mountkirk Games has completed a migration to Google Cloud using a lift-and-shift approach. Legacy games will eventually be migrated from VMs to GKE, but the new game is a higher priority.
The new game will support multiple device platforms, so some processing, like rendering graphics, will be done on the server side to ensure consistency in graphics processing and minimizing the load on players' devices. To minimize latency, plan for global load balancing and multiregion deployment of services in GKE.
Cloud Logging can ingest custom log data, so it should be used to collect game activity logs. Since Cloud Logging stores logs for only 30 days, you will likely need to create a log sink to store the data in Cloud Storage or BigQuery. Since the logs are structured and you will be analyzing the logs, storing them in BigQuery is a good option. At the time of writing, in North America the cost of active storage in BigQuery is about the same as the cost of Standard Storage in Cloud Storage. The cost of BigQuery's Long-term Storage is also about the same as Nearline Storage in Cloud Storage. Prices vary by region and may vary over time.
TerramEarth
The TerramEarth case study describes a heavy equipment manufacturer for the agriculture and mining industries. The company has hundreds of dealers in 100 countries with more than 2 million vehicles in operation. The company is growing at 20 percent annually.
Business and Technical Considerations
Vehicles generate telemetry data from sensors. Most of the data collected is compressed and uploaded after the vehicle returns to its home base. A small amount of data is transmitted in real time. Each vehicle generates from 200 to 500 MB of data per day.
