Cloud-native is a development approach that encourages developers to build apps especially for the cloud. This is mainly because of the huge differences between traditional on-prem environments and various cloud architectures. This article attempts to provide a comprehensive definition of cloud-native, and reviews five trends that are set to impact the cloud-native ecosystem.
Depending on the source, cloud-native can mean two slightly different things. Some sources define cloud-native applications as applications that are designed for and used in the cloud. Others extend the requirements, specifying that in order to be cloud-native, applications must also include the use of containers, automation, and microservices.
A cloud-native application is distinct from legacy applications in that it doesn’t have to be adapted to take full advantage of cloud infrastructure. Rather it is designed, tested, deployed, and updated entirely in the cloud.
You can use cloud-native applications equally efficiently in private, public, or hybrid clouds. Many applications are also compatible with multi-cloud strategies since applications can often be distributed across resources with minimal impact.
The extended definition of cloud-nativity comes from guidelines created by the Cloud-Native Computing Foundation (CNCF). The CNCF is a non-profit organization dedicated to advancing cloud-native technologies and encouraging standardized adoption of cloud-native technologies.
Specifically, the CNCF dictates that in order to be considered cloud-native, technologies must include microservices architecture, dynamic orchestration, and the use of containers.
The reasoning for this is that microservices ensure that resources are optimized, containers ensure that technologies are deployed in cloud environments, and dynamic orchestration ensures that containers remain scalable and highly-available. Scalability and high-availability are both important markers of cloud services.
As technologies have advanced and cloud adoption has grown, cloud-native technologies are in increasing demand. Many organizations are realizing that cloud-nativity can provide significant benefits over legacy technologies. Vendors and developers are working to fill gaps in demand and are focusing on the following cloud-native trends.
Multi-cloud is a strategy that enables organizations to combine cloud services from a variety of vendors. It allows you to select the services that best fit your needs and budget and prevents frustrations caused by vendor lock-in. With a multi-cloud strategy, organizations can have a mix of public, private, and hybrid cloud resources.
These strategies are made possible by cloud-native technologies and are also best supported by cloud-native applications. For example, Kubernetes, the most popular container orchestration platform. Kubernetes enables organizations to manage applications and workloads across environments, making it easier to adopt cloud-native applications regardless of the vendor an application is hosted on.
While Kubernetes is highly popular, it is also notoriously complex to deploy, maintain, and upgrade. To address this complexity, many managed services are being developed. These services benefit organizations that would otherwise struggle to deploy multi-cloud systems. These services allow companies of all sizes to adopt cloud services and cloud-native applications with ease, driving the popularity of this strategy.
As containerized workloads gain in popularity DevOps teams have adapted how they manage their toolchains, environments, and applications. Many teams are beginning to manage their various cloud infrastructures in the same way they manage application code. This method is known as GitOps.
GitOps can enable teams to use version control methods to manage infrastructure and environment changes with existing pipelines. It enables teams to more easily automate configuration changes and updates, supporting the dynamic orchestration that is needed for cloud-nativity. This implementation of infrastructure as code can also help remove some of the barriers that prevent development teams from operating as smoothly as they could.
cloud-native technologies started with services and applications hosted in data centers but these technologies are increasingly moving to edge devices. As Internet of things (IoT) devices grow in popularity and technologies like 5G are made accessible, this movement will continue.
When organizations can move cloud-native services to edge devices, they can speed response times and provide better services to customers. Cloud-nativity in edge devices also makes these devices easier to incorporate into networks and can help standardize specifications. Both of these aspects can help make edge technologies more secure and reliable.
Site Reliability Engineering (SRE) is a method developed by Google for running production systems. It takes practices and methods from software engineering and applies those practices to infrastructure and operations management. Similar to GitOps, SRE enables teams to automate many infrastructure and operations issues and adapts well to cloud-nativity.
While many teams have begun implementing SRE practices since its introduction, not all are doing so successfully. However, as the drive for cloud-nativity increases, these practices are likely to become increasingly important to ensure that systems remain operational at speed and scale. This means that organizations need to work to improve implementations and ensure that the adoption of SRE methods is consistent.
Although not essentially a cloud-native development machine learning operations (MLOps) has the potential to open some new technological doors. MLOps is a process that applies DevOps practices and tools to machine learning operations. It enables data scientists to dynamically manage machine learning environments and automate many of the routine processes that slow the development of machine learning.
The availability of these tools and practices can help bring machine learning and cloud-nativity together in a way that was previously challenging or prohibitive. By facilitating smoother integration of ML processes into cloud pipelines, cloud-native developers can gain new opportunities to incorporate AI and analytics services. Likewise, data scientists can leverage cloud-native resources to speed the training and development of their algorithms.
Cloud-native apps are, first and foremost, built for the cloud. That means you need to take the cloud into consideration while developing, rather than having to modify your app later on after the release. To help developers follow specific standards, the CNCF organization extended the definition, requiring the use of microservices, containers, and dynamic orchestration.
In the past few years, the basic cloud-native requirements have expended, and today’s cloud-native development leverages a wide range of useful and cutting edge technologies. This includes GitOps, edge computing, SRE, and MLOps. As Kubernetes continues to gain popularity, we will see much more of cloud-native computing, and a wide range of innovative improvements that stack on top of this technology.