As a cloud architect or decision maker, when you plan to deploy an application in Google Cloud, you need to choose a deployment archetype1 that's suitable for your application. This guide describes six deployment archetypes—zonal, regional, multi-regional, global, hybrid, and multicloud, and presents use cases and design considerations for each deployment archetype. The guide also provides a comparative analysis to help you choose the deployment archetypes that meet your requirements for availability, cost, performance, and operational efficiency.
What is a deployment archetype?
A deployment archetype is an abstract, provider-independent model that you use as the foundation to build application-specific deployment architectures that meet your business and technical requirements. Each deployment archetype specifies a combination of failure domains where an application can run. These failure domains can be one or more Google Cloud zones or regions, and they can extend to include your on-premises data centers or failure domains in other cloud providers.
The following diagram shows six applications deployed in Google Cloud. Each application uses a deployment archetype that meets its specific requirements.
As the preceding diagram shows, in an architecture that uses the hybrid or multicloud deployment archetype, the cloud topology is based on one of the basic archetypes: zonal, regional, multi-regional, or global. In this sense, the hybrid and multicloud deployment archetypes can be considered as composite deployment archetypes that include one of the basic archetypes.
Choosing a deployment archetype helps to simplify subsequent decisions regarding the Google Cloud products and features that you should use. For example, for a highly available containerized application, if you choose the regional deployment archetype, then regional Google Kubernetes Engine (GKE) clusters are more appropriate than zonal GKE clusters.
When you choose a deployment archetype for an application, you need to consider tradeoffs between factors like availability, cost, and operational complexity. For example, if an application serves users in multiple countries and needs high availability, you might choose the multi-regional deployment archetype. But for an internal application that's used by employees in a single geographical region, you might prioritize cost over availability and, therefore, choose the regional deployment archetype.
Overview of the deployment archetypes
The following tabs provide definitions for the deployment archetypes and a summary of the use cases and design considerations for each.
Zonal
Your application runs within a single Google Cloud zone, as shown in the following diagram:
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| More information | See the following sections: |
Regional
Your application runs independently in two or more zones within a single Google Cloud region, as shown in the following diagram:
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| More information | See the following sections: |
Multi-regional
Your application runs independently in multiple zones across two or more Google Cloud regions. You can use DNS routing policies to route incoming traffic to the regional load balancers. The regional load balancers then distribute the traffic to the zonal replicas of the application, as shown in the following diagram:
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| More information | See the following sections: |
Global
Your application runs across Google Cloud regions worldwide, either as a globally distributed (location-unaware) stack or as regionally isolated stacks. A global anycast load balancer distributes traffic to the region that's nearest to the user. Other components of the application stack can also be global, such as the database, cache, and object store.
The following diagram shows the globally distributed variant of the global deployment archetype. A global anycast load balancer forwards requests to an application stack that's distributed across multiple regions and that uses a globally replicated database.
The following diagram shows a variant of the global deployment archetype with regionally isolated application stacks. A global anycast load balancer forwards requests to an application stack in one of the regions. All the application stacks use a single, globally replicated database.
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| Design considerations | Costs for cross-region data transfer and data replication. |
| More information | See the following sections: |
Hybrid
Certain parts of your application are deployed in Google Cloud, while other parts run on-premises, as shown in the following diagram. The topology in Google Cloud can use the zonal, regional, multi-regional, or global deployment archetype.
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| Design considerations |
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| More information | See the following sections: |
Multicloud
Some parts of your application are deployed in Google Cloud, and other parts are deployed in other cloud platforms, as shown in the following diagram. The topology in each cloud platform can use the zonal, regional, multi-regional, or global deployment archetype.
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| Design considerations |
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| More information | See the following sections: |
Contributors
Author: Kumar Dhanagopal | Cross-Product Solution Developer
Other contributors:
- Anna Berenberg | Engineering Fellow
- Anshu Kak | Distinguished Engineer
- Jeff Welsch | Director, Product Management
- Marwan Al Shawi | Partner Customer Engineer
- Sekou Page | Outbound Product Manager
- Steve McGhee | Reliability Advocate
- Victor Moreno | Product Manager, Cloud Networking
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Anna Berenberg and Brad Calder, Deployment Archetypes for Cloud Applications, ACM Computing Surveys, Volume 55, Issue 3, Article No.: 61, pp 1-48 ↩