Extending to a multi-cloud environment
In this we will learn about Extending with on-premises/multi-cloud environments. Moreover, we will discusses hybrid and multi-cloud deployments, architecture patterns, and network topologies. This part explores common hybrid and multi-cloud architecture patterns.
However, every enterprise has a unique portfolio of application workloads that place requirements and constraints on the architecture of a hybrid or multi-cloud setup. Although you must design and tailor your architecture to meet these constraints and requirements, you can rely on some common patterns. The patterns fall into two categories:
- Firstly, patterns that rely on a distributed deployment of applications. The aim of these patterns is to run an application in the computing environment that suits it best, capitalizing on the different properties and characteristics of computing environments.
- Secondly, patterns that are based on redundant deployments of applications. In these patterns, you deploy the same applications in multiple computing environments, with the aim of increasing capacity or resiliency.
Distributed deployment patterns
When you migrate from a classic computing environment to a hybrid or multi-cloud setup, consider the constraints that existing applications impose. You also want to capitalize on the unique capabilities that each computing environment offers. These distributed patterns aim to strike a thoughtful balance between both objectives.
Tiered hybrid
Most applications can be categorized as either frontend or backend.
- Firstly, frontend applications are directly exposed to end users or devices. As a result, these applications are often performance sensitive and might be subject to frequent releases as new features and improvements are developed.
- Secondly, backend applications usually focus on managing data. Key challenges for such applications include handling data in volume and securing it appropriately.
However, the idea of the tiered hybrid pattern is to focus first on deploying existing frontend applications to the public cloud. In this pattern, you reuse existing backend applications that stay in their private computing environment.
The following diagram shows a typical tiered hybrid pattern.
Advantages
Focusing on frontend applications first has several advantages:
- Firstly, frontend applications depend on backends and occasionally on other frontends, but backends do not depend on frontends.
- Secondly, because frontend applications often are stateless or do not manage data by themselves, they tend to be less challenging to migrate.
Deploying existing or newly developed frontend applications to the public cloud offers several key advantages:
- Firstly, many frontend applications are subject to frequent changes. Running these applications in the public cloud simplifies the setup of a continuous integration/continuous deployment (CI/CD) process that you can use to roll out updates in an efficient and automated manner.
- Secondly, performance-sensitive frontends and frontends that are subject to frequent changes can benefit substantially from the load balancing, multi-regional deployments, and autoscaling features that a cloud deployment enables.
- Lastly, whether they are implementing user interfaces or APIs, or handling IoT (Internet of Things) data ingestion, frontend applications can benefit from the capabilities that cloud services such as Firebase, Cloud CDN, or Cloud IoT offer.
Partitioned multi-cloud
The partitioned multi-cloud pattern combines multiple public cloud environments, operated by different vendors, in a way that gives you the flexibility to deploy an application in the optimal computing environment.
However, you can maintain the ability to shift workloads as needed from one public cloud environment to another, in which case, workload portability becomes a key requirement. When you deploy workloads to multiple computing environments and want to maintain the ability to move workloads between environments, you must abstract away the differences between the environments. Further, Google Cloud provides a rich set of services that you can use to deploy your workloads in different ways.
Advantages
Here are some key advantages of the partitioned multi-cloud pattern:
- Firstly, you can avoid vendor lock-in. This pattern helps lower strategic risk and provides you with the flexibility to change plans or partnerships later.
- Secondly, when you keep workloads portable, you can optimize your operations by shifting workloads between computing environments.
Analytics hybrid and multi-cloud
In enterprise systems, most workloads fall into these categories:
- Firstly, transactional workloads include interactive applications like sales, financial processing, enterprise resource planning, or communication.
- Secondly, analytics workloads include applications that transform, analyze, refine, or visualize data to aid decision-making processes.
Although analytics systems obtain their data from transactional systems by either querying APIs or accessing databases, in most enterprises, analytics and transactional systems tend to be separated and loosely coupled. The idea of the analytics hybrid and multi-cloud pattern is to capitalize on this pre-existing split by running the two kinds of workloads in two different computing environments. However, raw data is first extracted from workloads that are running in the private computing environment and then loaded into Google Cloud, where it is used for analytical processing.
Advantages
Running analytics workloads in the cloud has several key advantages:
- Firstly, analytics workloads often need to process substantial amounts of data and can be bursty. So they are especially well suited to being deployed in a public cloud environment. However, by dynamically scaling compute resources, you can quickly process large datasets while avoiding upfront investments.
- Secondly, Google Cloud provides a rich set of services to manage data throughout its entire lifecycle, ranging from initial acquisition through processing and analyzing to final visualization.
- Next, Cloud Storage is good for building a data lake.
- Lastly, Ingress traffic—moving data from the private computing environment to Google Cloud—is free of charge.
Business continuity hybrid and multi-cloud
Ideally, mission-critical systems are set up in a way that makes them resilient during disasters. By replicating systems and data over multiple geographical regions and avoiding single points of failure. You can minimize the risks of a natural disaster that affects local infrastructure. However, this approach does not address the risk of outages that are caused by human error or software defects.
Advantages
Using the public cloud for business continuity offers a number of advantages:
- Firstly, because Google Cloud has over a dozen regions to choose from. However, you can use it to back up or replicate data to a different site within the same continent.
- Secondly, stopped VM instances incur storage costs only and are substantially cheaper than VM instances that are running. This is so you can minimize the cost of maintaining cold standby systems.
- Lastly, the pay-per-use model of Google Cloud ensures that you pay only for storage and compute capacity that you actually use. And, you can grow or shrink your DR environment as needed.
Reference: Google Documentation