mountkirk games case study solution

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GCP – How To Work on MountKirk Games Case Study

What is ‘mountkirk games’ .

‘MountKirk games’ is a fictitious company name used as a case study in Google Cloud platform (GCP). The company develops mobile games for its global user base. It aims to leverage on high scalability, continuous deployment, real-time analytics, minimum latency features offered in GCP for their future multiplayer game releases.

Why this case study is important?

MountKirk games is one of the case studies included in the Google Professional Cloud Architect (PCA) certification examination. We can expect maximum of 10 questions based on any two case studies listed in the examination guide.

For tips, resources and more helpful information on PCA exam, please read my blog –  Six steps to Google Professional Cloud Architect Certification Blog

How to approach the solution ?

The solution approach consists of simple four steps method as mentioned in my previous  blog  in detail.

• Identify GCP products or services based on company requirements.
• Identify the knowledge gaps and do the relevant readings.
• Refer the industry best practices guidelines.
• Draw the solution diagram and discuss it with colleagues.

Company overview

Mountkirk Games makes online, session-based, multiplayer games for mobile platforms. They have recently started expanding to other platforms after successfully migrating their on-premises environments to Google Cloud. Their most recent endeavor is to create a retro-style first-person shooter (FPS) game that allows hundreds of simultaneous players to join a geo-specific digital arena from multiple platforms and locations. A real-time digital banner will display a global leaderboard of all the top players across every active arena.

Solution concept

The company is building a new multiplayer game that they expect to be very popular. They plan to deploy the game’s backend on Google Kubernetes Engine so they can scale rapidly and use Google’s global load balancer to route players to the closest regional game arenas. In order to keep the global leader board in sync, they plan to use a multi-region Spanner cluster (gaming database).

Existing technical environment

The existing environment was recently migrated to Google Cloud, and five games came across using lift-and-shift virtual machine migrations, with a few minor exceptions. Each new game exists in an isolated Google Cloud project nested below a folder that maintains most of the permissions (IAM) and network policies. Legacy games with low traffic have been consolidated into a single project. There are also separate environments for development and testing.

Business requirements

• Support multiple gaming platforms and multiple regions.
• Minimize latency.
• Support rapid iteration of game features.
• Optimize for dynamic scaling.
• Use managed services and pooled resources.
• Minimize costs

Products/services Identified

• Integration via Cloud Pub/Sub to ingest real-time event messages sent from a large number of mobile apps.
• Multi-regional GKE clusters with auto-scaling feature enabled for scalable environments based on user gaming activity.
• CI-CD pipeline using Cloud Repository, Jenkin builds, Docker, Container registry, Terraform tools.
• Content Delivery Network (CDN) along with Global Load Balancer provides content closer and faster to users with reduced latency within service level objective limits.

Technical requirements

• Dynamically scale based on game activity.
• Publish scoring data on a near real-time global leaderboard.
• Store game activity logs in structured files for future analysis.
• Use GPU processing to render graphics server-side for multi-platform support.
• Support eventual migration of legacy games to this new platform.
• Kubernetes Engine provides dynamic scalability feature to grow in size in case of higher traffic.
• Memorystore is in-memory database on cloud provides rapid access to scoring data on global leaderboards.
• Google Cloud Storage is the recommended place to store large files like game activity logs for future processing.
• Cloud Dataflow transforms the JSON event into structured, schema-based data.
• BigQuery – all gaming data is loaded into the BigQuery analytics engine.

Executive statement

Our last game was the first time we used Google Cloud, and it was a tremendous success. We were able to analyze player behavior and game telemetry in ways that we never could before. This success allowed us to bet on a full migration to the cloud and to start building all-new games using cloud-native design principles.

Our new game is our most ambitious to date and will open up doors for us to support more gaming platforms beyond mobile. Latency is our top priority, although cost management is the next most important challenge. As with our first cloud-based game, we have grown to expect the cloud to enable advanced analytics capabilities so we can rapidly iterate on our deployments of bug fixes and new functionality.

Proposed Solution Diagram

Based on the above requirements, we find that the central theme of the case study is ‘To provide Global gaming platform with scalability, low latency, high analytics capability’.

Solution Description

The frontend components of the gaming architecture include:

• Game platform services that provide extra-game functionality.
• Dedicated game servers that host the game.

The backend components of the gaming architecture include:

• Game state, persisted in the system of record and stored in the game database like Cloud Spanner.
• An analytics stack that stores and queries analytics and gameplay events.

In the gaming world, millions of mobile devices send the real-time event messages simultaneously. These events are ingested at scale with Cloud Pub/Sub. The Dataflow pipelines transform JSON event into structured, schema-based data for storing in BigQuery data warehouse for further analytics.

Cloud Storage is used to store large files like historical game activity logs from cloud logging for batch processing. The Dataflow pipelines processes the batch load before storing in BigQuery engine for analytics. Cloud Dataprep is a tool to visually exploring, cleaning and preparing structured and unstructured data for analysis.

Google Data Studio is a data visualisation tool which allows to create and share interactive gaming leaderboards using data in BigQuery. Other data visualization tools such as Looker, Google Sheets and Microsoft Excel are capable of directly running the BigQuery queries within them.

Further Reading –  TerramEarth case study solution. Blog

Sample Case Questions

• MountKirk Games needs to build out their streaming data analytics pipeline to feed from their game backend application. What GCP services in which order will achieve this?
• The gaming company wants to set up a real-time analytics platform for their new game. The new platform must meet their technical requirements. Which combinations of Google Cloud Technology will meet all of their requirements?
• Mountkirk Games has deployed their new backend on Google Cloud Platform (GCP). You want to create a thorough testing process for new versions of the backend before they are released to the public. You want the testing environment to scale in an economical way. How should you design the process?
• MountKirk Games uses Kubernetes and Google Kubernetes Engine and wants to Use GPU processing to render graphics server-side. But GPUs are expensive so they are planning to use preemptible GPU nodes. What do they have to perform for the above solution?

Checkout 200+ practice exam questions here –  Google Professional Cloud Architect Certification – Practice Exam   

Hope you find this case study solution useful for PCA certification examination preparation.

Written exclusively for ReviewNPrep.com – By Manoj P. (Connect with me on  LinkedIn ).

References:

• Building a Mobile Gaming Analytics Platform
• visual paradigm

Manoj Patil

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• Mountkirk Games

This lesson will help you prepare for the Professional Cloud Architect Exam. We cover the 4 case studies presented in the exam guide, explain  what they are, why they are important, and how to use them to prepare for the exam.

Learning Objectives

Examine the 4 case studies presented in the exam guide:

• EHR Healthcare
• Helicopter Racing League
• TerramEarth

Intended Audience

Anyone planning to take the Professional Cloud Architect Exam.

Prerequisites

Basic knowledge of GCP.

Daniel began his career as a Software Engineer, focusing mostly on web and mobile development. After twenty years of dealing with insufficient training and fragmented documentation, he decided to use his extensive experience to help the next generation of engineers.

Daniel has spent his most recent years designing and running technical classes for both Amazon and Microsoft. Today at Cloud Academy, he is working on building out an extensive Google Cloud training library.

When he isn’t working or tinkering in his home lab, Daniel enjoys BBQing, target shooting, and watching classic movies.

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Latest Mountkirk Games Case Study (2024) - Google Cloud (GCP) Architect

Possible architecture solution.

• Kubernetes + Agones (Gaming Server) : For multiplayer gaming
• Cloud Storage : Store game activity logs in organised files for later analysis in the cloud.
• Batch : Cloud Storage > Dataflow > BigQuery
• Streaming : Pub/Sub > Dataflow > BigQuery
• Common infra : Cloud build, KMS, IAM, Logger etc

Reference Architecture Solution diagram

There will be multiple possbile solutions for this use case, to understand the implementation better we have give reference architcture which is one of possible solution Below reference architecture depicts different aspect of Gaming Analytics cloud Platform solution like gaming server on GKE, Batch and stream pipleine and common cross cutting infrastructure components :

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Exam professional cloud architect topic 6 question 3 discussion.

For this question, refer to the Mountkirk Games case study. Mountkirk Games wants to design their solution for the future in order to take advantage of cloud and technology improvements as they become available. Which two steps should they take? (Choose two.)

• A. Store as much analytics and game activity data as financially feasible today so it can be used to train machine learning models to predict user behavior in the future.
• B. Begin packaging their game backend artifacts in container images and running them on Google Kubernetes Engine to improve the ability to scale up or down based on game activity.
• C. Set up a CI/CD pipeline using Jenkins and Spinnaker to automate canary deployments and improve development velocity.
• D. Adopt a schema versioning tool to reduce downtime when adding new game features that require storing additional player data in the database.
• E. Implement a weekly rolling maintenance process for the Linux virtual machines so they can apply critical kernel patches and package updates and reduce the risk of 0-day vulnerabilities.

BiddlyBdoyng

Sandipghosal, omermahgoub, surajkrishnamurthy, get it certification.

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Professional Cloud Architect

Certification exam guide

A Google Cloud Certified Professional Cloud Architect enables organizations to leverage Google Cloud technologies. Through an understanding of cloud architecture and Google technology, this individual designs, develops, and manages robust, secure, scalable, highly available, and dynamic solutions to drive business objectives. The Cloud Architect should be proficient in all aspects of enterprise cloud strategy, solution design, and architectural best practices. The Cloud Architect should also be experienced in software development methodologies and approaches including multi-tiered distributed applications which span multicloud or hybrid environments.

Case studies

During the exam for the Cloud Architect Certification, some of the questions may refer you to a case study that describes a fictitious business and solution concept. These case studies are intended to provide additional context to help you choose your answers. Review the case studies that may be used in the exam.

EHR Healthcare

Helicopter Racing League

Mountkirk Games

TerramEarth

Section 1: Designing and planning a cloud solution architecture (~24% of the exam)

1.1 Designing a solution infrastructure that meets business requirements. Considerations include:

    ●  Business use cases and product strategy

    ●  Cost optimization

    ●  Supporting the application design

    ●  Integration with external systems

    ●  Movement of data

    ●  Design decision trade-offs

    ●  Build, buy, modify, or deprecate

    ●  Success measurements (e.g., key performance indicators [KPI], return on investment [ROI], metrics)

    ●  Compliance and observability

1.2 Designing a solution infrastructure that meets technical requirements. Considerations include:

    ●  High availability and failover design

    ●  Elasticity of cloud resources with respect to quotas and limits

    ●  Scalability to meet growth requirements

    ●  Performance and latency

1.3 Designing network, storage, and compute resources. Considerations include:

    ●  Integration with on-premises/multicloud environments

    ●  Cloud-native networking (VPC, peering, firewalls, container networking)

    ●  Choosing data processing technologies

    ●  Choosing appropriate storage types (e.g., object, file, databases)

    ●  Choosing compute resources (e.g., preemptible, custom machine type, specialized workload)

    ●  Mapping compute needs to platform products

1.4 Creating a migration plan (i.e., documents and architectural diagrams). Considerations include:

    ●  Integrating solutions with existing systems

    ●  Migrating systems and data to support the solution

    ●  Software license mapping

    ●  Network planning

    ●  Testing and proofs of concept

    ●  Dependency management planning

1.5 Envisioning future solution improvements. Considerations include:

    ●  Cloud and technology improvements

    ●  Evolution of business needs

    ●  Evangelism and advocacy

Section 2: Managing and provisioning a solution infrastructure (~15% of the exam)

2.1 Configuring network topologies. Considerations include:

    ●  Extending to on-premises environments (hybrid networking)

    ●  Extending to a multicloud environment that may include Google Cloud to Google Cloud communication

    ●  Security protection (e.g. intrusion protection, access control, firewalls)

2.2 Configuring individual storage systems. Considerations include:

    ●  Data storage allocation

    ●  Data processing/compute provisioning

    ●  Security and access management

    ●  Network configuration for data transfer and latency

    ●  Data retention and data life cycle management

    ●  Data growth planning

2.3 Configuring compute systems. Considerations include:

    ●  Compute resource provisioning

    ●  Compute volatility configuration (preemptible vs. standard)

    ●  Network configuration for compute resources (Google Compute Engine, Google Kubernetes Engine, serverless networking)

    ●  Infrastructure orchestration, resource configuration, and patch management

    ●  Container orchestration

Section 3: Designing for security and compliance (~18% of the exam)

3.1 Designing for security. Considerations include:

    ●  Identity and access management (IAM)

    ●  Resource hierarchy (organizations, folders, projects)

    ●  Data security (key management, encryption, secret management)

    ●  Separation of duties (SoD)

    ●  Security controls (e.g., auditing, VPC Service Controls, context aware access, organization policy)

    ●  Managing customer-managed encryption keys with Cloud Key Management Service

    ●  Remote access

3.2 Designing for compliance. Considerations include:

    ●  Legislation (e.g., health record privacy, children’s privacy, data privacy, and ownership)

    ●  Commercial (e.g., sensitive data such as credit card information handling, personally identifiable information [PII])

    ●  Industry certifications (e.g., SOC 2)

    ●  Audits (including logs)

Section 4: Analyzing and optimizing technical and business processes (~18% of the exam)

4.1 Analyzing and defining technical processes. Considerations include:

    ●  Software development life cycle (SDLC)

    ●  Continuous integration / continuous deployment

    ●  Troubleshooting / root cause analysis best practices

    ●  Testing and validation of software and infrastructure

    ●  Service catalog and provisioning

    ●  Business continuity and disaster recovery

4.2 Analyzing and defining business processes. Considerations include:

    ●  Stakeholder management (e.g. influencing and facilitation)

    ●  Change management

    ●  Team assessment / skills readiness

    ●  Decision-making processes

    ●  Customer success management

    ●  Cost optimization / resource optimization (capex / opex)

4.3 Developing procedures to ensure reliability of solutions in production (e.g., chaos engineering, penetration testing)

Section 5: Managing implementation (~11% of the exam)

5.1 Advising development/operation teams to ensure successful deployment of the solution. Considerations include:

    ●  Application development

    ●  API best practices

    ●  Testing frameworks (load/unit/integration)

    ●  Data and system migration and management tooling

5.2 Interacting with Google Cloud programmatically. Considerations include:

    ●  Google Cloud Shell

    ●  Google Cloud SDK (gcloud, gsutil and bq)

    ●  Cloud Emulators (e.g. Cloud Bigtable, Datastore, Spanner, Pub/Sub, Firestore)

Section 6: Ensuring solution and operations reliability (~14% of the exam)

6.1 Monitoring/logging/profiling/alerting solution

6.2 Deployment and release management

6.3 Assisting with the support of deployed solutions

6.4 Evaluating quality control measures

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Mountkirk games case study, google cloud – mountkirk games case study.

Mountkirk Games makes online, session-based, multiplayer games for mobile platforms. They have recently started expanding to other platforms after successfully migrating their on-premises environments to Google Cloud. Their most recent endeavor is to create a retro-style first-person shooter (FPS) game that allows hundreds of simultaneous players to join a geo-specific digital arena from multiple platforms and locations. A real-time digital banner will display a global leaderboard of all the top players across every active arena.

Solution Concept

Mountkirk Games is building a new multiplayer game that they expect to be very popular. They plan to deploy the game’s backend on Google Kubernetes Engine so they can scale rapidly and use Google’s global load balancer to route players to the closest regional game arenas. In order to keep the global leader board in sync, they plan to use a multi-region Spanner cluster.

So the key here is the company wants to deploy the new game to Google Kubernetes Engine exposed globally using a Global Load Balancer and configured to scale rapidly and bring it closer to the users. Backend DB would be managed using a multi-region Cloud Spanner cluster.

Executive Statement

Our last game was the first time we used Google Cloud, and it was a tremendous success. We were able to analyze player behavior and game telemetry in ways that we never could before. This success allowed us to bet on a full migration to the cloud and to start building all-new games using cloud-native design principles. Our new game is our most ambitious to date and will open up doors for us to support more gaming platforms beyond mobile. Latency is our top priority, although cost management is the next most important challenge. As with our first cloud-based game, we have grown to expect the cloud to enable advanced analytics capabilities so we can rapidly iterate on our deployments of bug fixes and new functionality.

So the key points here are the company has moved to Google Cloud with great success and wants to build new games in the cloud. Key priorities are high performance, low latency, cost, advanced analytics, quick deployment, and time-to-market cycles.

Business Requirements

Support multiple gaming platforms.

Support multiple regions.

• Can be handled using a Global HTTP load balancer with GKE in each region.
• Can be handled using multi-region Cloud Spanner
• Other multi-regional services like Cloud Storage , Cloud Datastore, Cloud Pub/Sub , BigQuery can be used.

Support rapid iteration of game features.

• Can be handled using Deployment Manager and IaaC services like Terraform to automate infrastructure provisioning
• Cloud Build + Cloud Deploy/Spinnaker can be used for rapid continuous integration and deployment

Minimize latency

• can be reduced using a Global HTTP load balancer, which would route the user to the closest region
• using multi-regional resources like Cloud Spanner would also help reduce latency

Optimize for dynamic scaling

• can be done using GKE Cluster Autoscaler and Horizontal Pod Autoscaling to dynamically scale the nodes and applications as per the demand
• Cloud Spanner can be scaled dynamically

Use managed services and pooled resources.

• Using GKE , with Global Load Balancer for computing and Cloud Spanner would help cover the application stack using managed services

Minimize costs.

• Using minimal resources and enabling auto-scaling as per the demand would help minimize costs

Existing Technical Environment

The existing environment was recently migrated to Google Cloud, and five games came across using lift-and-shift virtual machine migrations, with a few minor exceptions. Each new game exists in an isolated Google Cloud project nested below a folder that maintains most of the permissions and network policies. Legacy games with low traffic have been consolidated into a single project. There are also separate environments for development and testing.

Key points here are the resource hierarchy exists with a project for each new game under a folder to control access using Service Control Permissions. Also, some of the small games would be hosted in a single project. There are also different environments for development, testing, and production.

Technical Requirements

Dynamically scale based on game activity.

Publish scoring data on a near-real-time global leaderboard.

• can be handled using Pub/Sub for capturing data and Cloud DataFlow for processing the data on the fly i.e real time

Store game activity logs in structured files for future analysis.

• can be handled using Cloud Storage to store logs for future analysis
• analysis can be handled using BigQuery either loading the data or using federated data source
• data can also be stored directly using BigQuery as it would provide a low-cost data storage (as compared to Bigtable) for analytics 
• another advantage of BigQuery over Bigtable in this case its multi-regional, meeting the global footprint and latency requirements

Use GPU processing to render graphics server-side for multi-platform support.

• Support eventual migration of legacy games to this new platform.

Reference Architecture

Refer to Mobile Gaming Analysis Telemetry solution

Mountkirk Games References

• Google Cloud – Mountkrik Games