What is cloud native architecture?

Over the past 20 years, more organizations have adopted cloud technology and cloud-based infrastructure to keep systems up to date in real time. This brings a change in how internal IT systems run, how developers maintain them, and how they affect day-to-day operations.

Cloud native architectures provide observability, scalability, and automation. They provide a modernized framework for businesses to deliver applications to a large number of end users and constantly improve how to keep systems online. Organizations that treat the cloud as a rehosting strategy and do not re-architect or reconfigure for cloud native during their migration will miss out on the benefits.

Let’s take a look at what exactly cloud native architecture is, its benefits and drawbacks, and its main design principles.

Cloud native architecture is an approach to how engineers design, configure, and operate workloads in the cloud to leverage cloud computing. It focuses on how to optimize system architectures for cloud infrastructure – and provide speed and agility for business applications. Cloud native applications are designed to run in the cloud and use cloud infrastructure from their inception, instead of built on-premises and then migrated in a lift and shift fashion.

With cloud native architectures, businesses can increase developer efficiency, reduce costs, and ensure environment availability. Such dynamic systems provide these benefits through horizontal scaling, distributed processing, and automating failed component replacement.

Fundamentally, cloud native architectures have a microservices setup. Unlike traditional monolithic applications, this allows for engineers to break down applications into individual services to be more agile, traceable, and scalable. These applications and processes run in software containers as isolated units, with multiple containers efficiently packed onto servers/nodes. Cloud native architectures let engineers test workflows, deploy services, and integrate automation tools. 

A comparison of microservices vs. monolithic architectures.

Cloud native architectures have a technological foundation of containers and microservices. Beyond the specific technology, there are general patterns that are associated with cloud native.

• Pay as you go: Charges based on resource use and allows organizations to provision resources when engineers execute code and only pay for when its applications are in use.
• Self-service infrastructure: Infrastructure as a service (IaaS) means engineers don’t have to spend time managing individual resources – everything is managed through a software layer that automatically scales underlying infrastructure as they provision applications.
• Managed services: Service providers can help an organization or specific teams run tasks or operate components (such as Google Kubernetes Engine), such as cloud migration, configuration, optimization, security, and maintenance. This leaves internal staff free to work on value added features that more directly support the business.
• Globally distributed architecture: Decentralized components help engineers install and manage cloud native applications across their entire organization. With distributed architecture, engineers feel like they are using a single machine while getting benefits such as high scalability, transparency, and fault tolerance.
• 12-Factor Methodology: This approach helps manage cloud native application growth and simultaneously minimizes software entropy costs. To implement this 12-factor workflow, organizations must use a single codebase for all of an application’s deployments and isolate services to reduce system dependencies. Engineers should separate configuration and application code and run stateless processes.
• Infrastructure as Code (IaC) and automation: Also known as software-defined infrastructure, this practice takes any underlying hardware and makes it into code so that engineers can simply provision resources from a software UI. This allows for teams to implement automation for resource management and scaling.
• Automated recovery: Cloud native architecture lets engineers build resiliency measures for data and services that can easily recover and to reduce mean time to remediation or get services back online.

Beyond helping modern businesses drive innovation and providing scalability, cloud native architectures provide:

Optimized costs

Traditional IT environments require organizations to pay for all possible infrastructure – regardless of how much is used at any given time. Cloud native architectures allow organizations to pay for just what they use based on data, service, and storage requirements. By designing applications and infrastructure in a cloud native way with bin packing and right sizing you can ensure that you get the most for what you pay for.

Adaptability and scalability

Cloud native architecture is designed to adapt to changing business requirements. Organizations can scale how many containers and services they need depending on business directives and application requirements, whether it’s an increase for compute-intensive needs or decrease during traffic lulls.

Reduced vendor lock-in

With open source and multi-cloud configuration options, organizations are less reliant on one specific cloud provider than they may have been in the past. Furthermore, the use of open source software for cloud native (such as Kubernetes and Prometheus) means that organizations are less tied to a specific vendor and can use what technology is best for the business or specific workflows.

Troubleshooting, made easier

Microservices – and therefore cloud native architectures – are distributed and isolated. This means that when a service goes down, engineers only lose part of the system. Teams can set up alerts and monitors for their individual services to know when customers are experiencing issues. With distributed tracing and open source observability industry-wide standards,observability tools oversee all parts of your stack. Through tracing, engineers can see the entire path  a request takes through your microservices to identify affected services.

Automation and flexibility

Cloud native architectures don’t rely on traditional, waterfall software development methods. With DevOps and CI/CD methodology, engineering teams can push out updates, new features, and bug fixes on a daily (or even hourly) basis – instead of monthly. Cloud native uses a constant feedback-and-deployment development cycle to regularly send out updates that benefit end users and keep applications running. With automation tools, engineers can focus on more value-add tasks, instead of constantly fighting fires.

Simplified IT provisioning

With cloud based architectures, engineers can spin up as many containers or datastores as necessary with the push of a button and track it through a software management layer. Cloud native environments are designed to grow and shrink as needed for data storage and traffic requirements and cloud providers make it easy for organizations to request more storage when necessary.

Real time analytics and guideline compliance

Cloud native architectures are designed with data growth in mind. It is built so that engineers can easily access and leverage written data and access it from the cloud – instead of needing to retrieve it from a physical hard drive in a data center. Engineers can also update metrics rules and labels in real time to ensure accurate data workflows and a low cost to data ratio. Plus, most cloud providers are HIPAA and GDPR compliant for industry specific data requirements.

Moving to a cloud native architecture isn’t a simple lift-and-shift process. It also requires a change in organizational structure and software development methods for a successful implementation. Before adopting cloud native architecture, organizations should consider:

Security

Open source tools bring a lot of benefits to cloud native architecture, but they can increase security risks because of unmaintained code, unapproved changes, outdated versions, and publicly available code. This requires organizations to map out incredibly detailed security measures to protect their data – and leverage any possible tools from cloud providers.

Staffing

Cloud native environments require engineers that are knowledgeable in DevOps methodologies, managing data cardinality, monitoring configurations, containerization, microservices, open source tooling management, and how to work with cloud native tools. This can require investment in internal training, increasing self-education budgets or recruiting the top available talent to help everything run smoothly.

Resource management

Microservices are a foundational component of cloud native architecture – but due to how quickly engineers can spin them up – they can also be hard to monitor and get a continuously accurate picture of resource use. Unlike traditional servers, cloud servers can run multiple containerized microservices. This not only increases the number of resources engineers must monitor, but how often they collect data to get an accurate environment overview.

Performance and scalability

How well an organization’s internal systems run in the cloud directly ties to end user experience. Engineers need tooling that simultaneously supports high availability, resilience, and scalability for modifying, managing and monitoring their applications. Depending on the size of an environment, this can become increasingly tricky as cloud native infrastructure scales quicker than on-premises hardware and has high data cardinality levels. Testing for performance can also take longer as engineers must individually test components and workflow changes.

Data portability and interoperability

Not all organizations store application data in the cloud. Without the right tools, engineers may have trouble porting data onto a cloud native application, which reduces overall system visibility. Additionally, not all cloud providers support all possible data translation applications, which may make interoperability and data portability more difficult or time consuming – especially if proprietary tools are in place.

Adopting a cloud native architecture requires a shift in methodology of how the technical staff oversees and maintains the environment. The principles to keep in mind are:

Principle 1: Incorporate automation at the start

Cloud native architecture can quickly become complex, so engineers should design automated processes wherever they can to help repair, scale, and deploy system components. Tools can help teams automate infrastructure provisioning, incident coordination, CI/CD, canary testing, rollbacks, load scaling, recovery, and observability metrics.

Principle 2: Be smart with state

There’s a lot of data that goes through cloud native environments, so engineers must be mindful about when and how their system stores data, and use stateless components whenever possible. With stateless infrastructure, engineers can quickly scale, repair, rollback and load balance within their environment.

Principle 3: Rely on managed services

Most cloud providers offer managed services to help engineers increase functionality without increasing headcount. Managed services adoption can depend on cost, skill gaps, and operational overhead. Generally organizations can find service providers to manage open source deployments, proprietary software, or for a specific service engineers can’t support.

Principle 4: Apply authentication for security

While traditional architectures deploy a lot of perimeter security measures, cloud native architectures use internet-facing services, and face a lot of external threats, which makes the idea of a security “perimeter” inaccurate. Engineers should practice defense-in-depth and apply authentication between individual components and use script injecting and rate limiting to create a resilient and trustworthy architecture.

Principle 5: Continuously architect

Cloud native systems and architectures are constantly changing to suit business application requirements. This requires engineers to always improve, optimize, and refine their architecture to avoid an outdated system that drives up costs, increases troubleshooting times, and creates more confusion. For an organization to successfully adopt cloud native architecture, its engineering teams must think of it as a living, breathing organism – instead of a stagnant object.

Organizations that adopt a cloud native architecture need to have the tools that can keep up with its complexity and dynamicity. Observability tools are there to help engineers understand system behavior and make data-informed decisions on when and how to scale environments as needed, address complexity, and manage the ephemerality of containers and microservices.

Traditional application performance monitoring software isn’t designed for such architectures and often can’t keep up with the scale of cloud native architecture. Observability not only lets organizations oversee all the components of cloud native architecture, but it can control data growth, reduce vendor lock-in, help developers understand their systems, and keep costs in line.

Most importantly, observability helps keep cloud native environments reliable. The software helps developers address customer-facing issues quickly, proactively place alerts and data quotas, and maintain a work-life balance.

The Observability Data Optimization Cycle helps engineers take control of all the data that their cloud native architecture produces – and use it to optimize, rework, and refine their systems to run efficiently, reduce cardinality spikes, and meet customer demands. Over the past year, Chronosphere customers, on average, have seen a 60% reduction in their data volumes.

At Chronosphere, features such as the Metrics Usage Analyzer and Shaping Impact Rules allow teams to see the most expensive metrics and aggregation rules to make sure they’re getting the most value out of all the data their cloud native architecture produces. With the right data at the right cost, teams that run cloud native architectures can focus on the big picture – instead of worrying about going over budget or over resource usage.