What is cloud-native architecture?

Organizations turn to cloud-native architecture for increased scalability, flexibility, and resilience. But first, it's important to understand what it is and its potential pitfalls.

Lifting and shifting applications from the data center to the cloud delivers only marginal benefits. Because cloud computing breaks application functions into many microservices, porting monolithic applications to the cloud unchanged can slow them down. To take full advantage of the scalability, flexibility, and resilience of cloud platforms, organizations need to build or rearchitect applications around a cloud-native architecture.

So, what is cloud-native architecture, exactly? Although redesigning applications sounds daunting, understanding how these architectures work will enable your organization to reap the performance and cost benefits of cloud hyperscalers.

Cloud-native architecture is a structural approach to planning and implementing an environment for software development and deployment that uses resources and processes common with public clouds like Amazon Web Services, Microsoft Azure, and Google Cloud Platform. An organization could also provision cloud-native architecture in a private or hybrid cloud. This architectural method encompasses software containers, service meshes, microservices, immutable infrastructure, and declarative APIs to create an environment that is inherently scalable, extendable, and easy to manage through automation.

To better understand cloud-native architecture, it helps to look at each component individually:

• Software containers. Containers are portable, self-contained operating environments that include applications and the dependencies they need to run, such as databases, middleware, and frameworks. Teams can spin up and shut down containers quickly, as well as store them in libraries for reusability.
• Microservices. These loosely coupled, lightweight services typically perform a single function and can be chained together into an application. Services communicate with each other via well-defined application program interfaces (APIs), and users can update or replace them without affecting the integrity of the application.
• Service mesh. The service mesh adds a layer of monitoring into the application to complement a microservices architecture. The mesh tracks communication between microservices and routes requests between them to optimize performance and support observability.
• Immutable infrastructure. This infrastructure is a type of cloud computing service — also known as infrastructure-as-as-service — that teams replace rather than change using automation software and declarative code. When a host, component, or service needs to be updated or replaced, rather than patching or changing the infrastructure in the IT environment, a new version is deployed based on the end state in the playbook. This reduces the potential for errors due to configuration drift, improves reliability, and decreases your vulnerability to attack. Cloud computing environments provide the necessary automation to make immutable operations practical.
• Declarative APIs. These APIs shift the complexity of building and configuring APIs from the user to the system. Instead of specifying all the attributes necessary to deploy an API, the user specifies the end state. Then, the system automatically determines how to achieve it, dramatically reducing deployment times and the risk of errors.

Taken together, these features enable organizations to build software that is more scalable, reliable, and flexible than traditionally built software. With cloud-native architecture, teams can scale applications horizontally using large-scale distributed processing. It also enables the agile DevOps development techniques that have been adopted by 83% of IT organizations, according to Puppet.

While there are multiple ways to define cloud-native architecture, a consensus has formed around the following characteristics:

• Designed for automation. Software-defined environments minimize the need for manual controls and lend themselves well to the speed, precision, and observability benefits cloud platforms enable.
• Stateless whenever possible. Stateful applications — or those that manage and store data directly — create risk and complexity. Therefore, organizations should use them as little as possible. Stateless applications are far easier to scale, repair, roll back, and balance.
• Default to managed services. Configuring infrastructure and applications is a tedious, error-prone task that is unnecessary if someone else has already done all the work and packaged it as a managed service. The time to productivity is faster.
• Trust nothing. Traditional, perimeter-based defenses are all but useless in internet-facing cloud services. Zero-trust principles continually authenticate between components to minimize the risk of unknown elements.
• Match tools to tasks. Services-based applications are loosely coupled, meaning IT can mix and match components written in different languages and frameworks through APIs. This allows organizations to apply the best tool to each task.
• Embrace immutability. The inherently immutable nature of containerized applications means a single container image can be configured for each environment. This simplifies management and streamlines rollback, roll forward, and updates.

Organizations can combine cloud-native services to build applications with unique value enabled by the cloud, such as advanced analytics, mobile apps, and chatbots. Agile DevOps practices using cloud platforms needn’t be subject to the management overhead of traditional monolithic development. Organizations can shift development, testing, and delivery as needed between teams and geographies. Deployment is fast and global. Application development platforms live in the cloud and are always available.

Traditionally, enterprise applications have been monolithic, meaning all the functionality is bound together in a single code base. This has several structural disadvantages. First, teams must build and test the entire application as a unit, which hampers developer productivity.

Any necessary changes require teams to recompile and retest the entire application to ensure there aren’t any new problems. Documentation is slow and laborious to produce. Large applications take time to start and may run slowly. Small bugs can have unanticipated consequences due to the high level of interdependence between application components.

Cloud-native architecture, in contrast, is inherently modular and distributed. Building software this way is similar to constructing a structure out of LEGO blocks. Teams can assemble prebuilt components without needing long testing times. Additionally, teams can modularize applications so developers can work on different pieces in parallel. This adds up to a significant productivity boost for developers.

Cloud platforms are fully virtualized and, consequently, highly automated. Infrastructure is provisioned and modified in code, eliminating much of the need for manual installation and tuning. Organizations can also scale resources up and down transparently according to the application’s needs.

Deployment can also be modular. Each microservice functions independently, and teams can add or replace individual services on the fly without incurring downtime. Additionally, teams can restart or replace a poorly performing service with little or no disruption to the application.

The effect is to turbocharge developer collaboration, efficiency, and productivity. According to Puppet’s “2021 State of DevOps Report,” highly evolved DevOps organizations using cloud-native constructs deploy applications on demand compared with the monthly or longer lead times required by organizations with low levels of maturity. Highly productive teams require less than one hour’s notice to make changes and experience mean-time-to-repair rates of less than one hour. In contrast, in low-maturity organizations, changes and repairs typically require a week or more of lead time.

Despite its many virtues, cloud-native architecture also introduces complexity that is not typically seen in monolithic environments. That’s because large applications may incorporate many microservices running on a combination of clouds and on-premises infrastructure.

Such an environment is challenging to monitor using traditional observability tools. Using an automatic and intelligent observability platform eliminates the blind spots by automatically discovering applications, processes, and services running across hybrid, multicloud, and serverless environments, such as AWS serverless, in real time. Teams can capture and analyze metrics, logs, traces, and user-experience data in the context of dependencies among services and infrastructure. Administrators get direct insight into every service without scanning through screen after screen of log data.

There are many good reasons to adopt cloud-native constructs when building applications. Understanding the components of cloud-native architecture is the first step toward conquering its inherent complexity. As cloud architects, developers, and IT leaders are planning their cloud-native architecture, they should also choose which observability tools will provide the team with the best insights and most efficient IT operations.