Professional Cloud Architect Exam: Mountkirk Games Case Study

Professional Cloud Architect Exam: Mountkirk Games Case Study

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Question

Mountkirk Games makes online, session-based, multiplayer games for mobile platforms.

They build all of their games using some server-side integration.

Historically, they have used cloud providers to lease physical servers.

Due to the unexpected popularity of some of their games, they have had problems scaling their global audience, application servers, MySQL databases, and analytics tools.

Their current model is to write game statistics to files and send them through an ETL tool that loads them into a centralized MySQL database for reporting.

Solution Concept - Mountkirk Games is building a new game, which they expect to be very popular.

They plan to deploy the game's backend on Google Compute Engine so they can capture streaming metrics, run intensive analytics, and take advantage of its autoscaling server environment and integrate with a managed NoSQL database.

Business Requirements -Increase to a global footprintImprove uptime " downtime is loss of playersIncrease efficiency of the cloud resources we useReduce latency to all customers Technical Requirements - Requirements for Game Backend PlatformDynamically scale up or down based on game activityConnect to a transactional database service to manage user profiles and game stateStore game activity in a timeseries database service for future analysisAs the system scales, ensure that data is not lost due to processing backlogsRun hardened Linux distro Requirements for Game Analytics PlatformDynamically scale up or down based on game activityProcess incoming data on the fly directly from the game serversProcess data that arrives late because of slow mobile networksAllow queries to access at least 10 TB of historical dataProcess files that are regularly uploaded by users' mobile devices Executive Statement -

Answers

Explanations

Click on the arrows to vote for the correct answer

A. B. C. D.

C.

The best option for Mountkirk Games would be D. Use Cloud Bigtable for time series data, use Cloud Spanner for transactional data, and use BigQuery for historical data queries.

Explanation:

Business Requirements:

  • Increase to a global footprint: To achieve this requirement, Mountkirk Games can use Google Compute Engine to deploy the game's backend in multiple regions around the world.
  • Improve uptime: Mountkirk Games can achieve high availability and improve uptime by using Google's autoscaling server environment to scale up and down dynamically based on game activity. The use of hardened Linux distro can help improve security and reliability.
  • Increase efficiency of the cloud resources: Google's managed services such as Cloud Bigtable, Cloud Spanner, and BigQuery can help Mountkirk Games optimize their cloud resources.
  • Reduce latency to all customers: By deploying game backend on Google's globally distributed infrastructure, Mountkirk Games can reduce latency for players worldwide.

Technical Requirements:

  • Dynamically scale up or down based on game activity: Google Compute Engine provides autoscaling features that can scale up or down based on the game's activity.
  • Connect to a transactional database service to manage user profiles and game state: Cloud Spanner is a managed, horizontally scalable, strongly consistent, relational database service that can handle high write and read loads. It can be used to manage user profiles and game state.
  • Store game activity in a timeseries database service for future analysis: Cloud Bigtable is a fully managed, scalable NoSQL database that can store time-series data.
  • As the system scales, ensure that data is not lost due to processing backlogs: Using Google's autoscaling server environment and managed services such as Cloud Bigtable and Cloud Spanner can help ensure that data is not lost due to processing backlogs.
  • Run hardened Linux distro: Google Compute Engine allows users to choose a variety of Linux distros, including hardened Linux distros.

Requirements for Game Analytics Platform:

  • Dynamically scale up or down based on game activity: Google Compute Engine provides autoscaling features that can scale up or down based on the game's activity.
  • Process incoming data on the fly directly from the game servers: Cloud Bigtable can handle large volumes of data and provide low latency for data ingestion and processing.
  • Process data that arrives late because of slow mobile networks: Cloud Bigtable can handle late-arriving data with its time-series features.
  • Allow queries to access at least 10 TB of historical data: BigQuery is a fully managed, highly scalable data warehouse that can handle petabyte-scale data. It can be used to run historical data queries.
  • Process files that are regularly uploaded by users' mobile devices: Google Cloud Storage can be used to store user-generated files, and Cloud Dataflow can be used to process them.

Therefore, the best option for Mountkirk Games would be to use Cloud Bigtable for time series data, use Cloud Spanner for transactional data, and use BigQuery for historical data queries. This solution satisfies all the business requirements and technical requirements of Mountkirk Games.

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