Set yourself up for multicloud success with the right deployment pattern
Purposeful multicloud strategies help companies advance their business goals. But navigating multicloud requires a deft architectural approach to ensure cohesion among the various cloud services. You need to customize your architecture to meet the requirements of your unique portfolio of application workloads, but fortunately you can rely on some common patterns.
These patterns are based on either distributed or redundant deployment:
- Distributed deployment patterns run applications in the most suitable computing environment by leveraging the various computing environments’ different properties and characteristics.
- Redundant deployment patterns involve deploying the same applications in multiple computing environments in order to increase capacity or resiliency.
Distributed deployment patterns
Distributed patterns seek to find a balance between managing the restrictions created by existing applications and leveraging the unique potential of each computing environment. You need to consider factors such as agility, scaling potential, security and dependability when choosing the appropriate pattern.
With a tiered hybrid deployment pattern, you deploy existing frontend applications to the public cloud on a case-by-case basis first and reuse existing backend applications, which remain in their private computing environment. However, you may eventually move backend applications to the public cloud too because the share of applications you deploy to the cloud will increase over time.
Prioritizing frontend applications makes sense because, whereas frontend applications rely on backends, the reverse is not the case. With few dependencies, frontend applications are generally easier than backends to isolate and migrate. Deploying them to the public cloud makes sense because they are more subject to frequent change than backend applications and will benefit from the flexibility the cloud enables. The cloud simplifies the setup of a continuous integration/continuous deployment (CI/CD) process for efficient, automated updates, and features such as load balancing, multiregional deployments and autoscaling boost performance.
For backends that manage data with rigorous compliance requirements, keeping them in a private computing environment may be a prudent move. Many countries require data localization, which means that companies must store and process data locally. For example, the EU’s General Data Protection Regulation (GDPR) has strict stipulations about storing personal data that may be served best with an on-premises solution.
The partitioned multicloud pattern allows you to shift workloads between different vendors’ public cloud environments. Workload portability is essential to harness the flexibility to deploy applications in the most suitable computing environment. You will need to abstract away the differences between environments so that you can move workloads between multiple computing environments.
Partitioned multicloud patterns help you to avoid vendor lock-in because you are not tied to one cloud service provider. The ability to shift workloads to alternative environments as the need arises offers protection against the risk of downtime due to outages, as well as the ability to choose the most relevant features from each provider.
Maintaining the workload portability necessary to leverage this pattern also allows you to optimize operations as you move workloads from one environment to another. However, workload portability has its drawbacks. It requires additional development, testing and operations work. Building for workload portability can also reduce the usefulness of your chosen cloud platform to the lowest common denominator, preventing workloads from leveraging the cloud provider's fully managed services. Egress costs can spiral quickly too.
Containerization helps facilitate workload portability, with Kubernetes building on it by helping companies to avoid vendor lock-in.
Analytics and ML hybrid and multicloud
With this pattern, transactional systems remain on-premises, while analytical workloads are deployed to the cloud and feed data back if required. Transactional systems perform day-to-day operations for functions such as finance, communication and sales. Analytical workloads cover applications that process or visualize data to generate insights that inform decision-making. This pattern leverages the separation between these systems to run each kind of workload in a different computing environment.
By running analytics workloads in the cloud, you can dynamically scale compute resources to process vast volumes of data quickly, without the risk of overprovisioning resources. The main cloud providers also offer comprehensive services to manage data from acquisition through the entire lifecycle.
Continuous connectivity is a requirement for running workloads in the cloud, but this is not always possible. Locations such as marine vessels, supermarkets and certain manufacturing plants may not have reliable access to the internet, but these are also key settings for the Internet of Things (IoT), which needs connectivity for the embedded sensors and computer chips to transmit and receive valuable data. This is where the edge hybrid pattern comes in: It runs time- and business-critical workloads locally, at the edge of the network, and runs all other workloads in the cloud.
Running time- and business-critical workloads locally facilitates low latency and self-sufficiency Important transactions can still take place even if internet connectivity is not reliable. Using this pattern means that you still benefit from using the cloud for a substantial proportion of your workloads. To work effectively, it is important to minimize dependencies between systems that are running at the edge and systems that are running in the cloud environment.
Redundant deployment patterns
Redundant deployment patterns involve deploying the same applications in multiple computing environments in order to boost capacity or resiliency.
A hybrid environment pattern can be either redundant or distributed. It uses public cloud environments for development, testing and UAT and runs production workloads in private data centers. Restrictions relating to regulation and compliance can make cloud migration challenging for production environments and their data but not for other environments.
Using the public cloud for development and functional testing means you can provision and tear down environments as required. It also allows you to manage costs by stopping virtual machine instances when they are not being used or provisioning environments solely on demand.
Business continuity hybrid and multicloud
Disaster Recovery (DR) planning is essential for recovering systems compromised by natural or human-induced disasters. A key element involves frequent data backups to different geographical locations to minimize the recovery point objective (RPO). Maintaining standby systems (cold, warm or hot, depending on their latency) in a second location can also help reduce the recovery time objective (RTO).
However, a more cost-effective approach is to use the public cloud for the DR environment – hence the business continuity hybrid pattern. This pattern can even reduce the Actual Recovery Time if DR is triggered because the DR environment can be spun up quicker using infrastructure as code.
The cost of managing bursty workloads using on-premises environments can quickly spiral out of control because you have to overprovision resources to accommodate periods when workloads are intense. The bursting deployment pattern relies on a private computing environment for baseline loads and bursts to the cloud only when you need to scale up. For this reason, it is better suited to batch workloads than to interactive workloads. Workload portability is key.
One of the main benefits of this pattern is that it allows you to reuse your current investments in on-premises environments. You might even be able to make more efficient use of your private computing environments because you don’t need to overprovision resources to satisfy peak demands.
Where to next
Building a hybrid or multicloud solution is fraught with complex decisions – not least of which is designing the appropriate architecture. You will need to assess your company culture, DevOps practices and tech stack before making any decisions. No single technology solution will meet your unique requirements, but the answer may lie in a version of the distributed and redundant deployment patterns we have discussed. An expert cloud partner can guide you on the best approach to harnessing multicloud for your specific business goals.