Cloud-native is a term that is used to describe the information that is container-based environments. Now let us proceed further and understand some more details for the cloud-native application. It is that cloud-native technologies that are used to develop an application that is built with services packaged in containers, deployed as a micro-services and along with that managed on elastic infrastructure through agile DevOps processes and continuous delivery workflows.
Here when we talk about operational work, it means that the team would manage the infrastructure resource allocations to traditional applications and that is getting checked manually. It is that cloud-native application is deployed as well as are on infrastructure that is being abstracted from the underlying computing, storage, and networking primitives.
It is that the developers and the operators who are dealing with this new breed of application which does not directly interact with application programming interfaces (APIs). This is being exposed by the infrastructure providers. It is that now, the orchestrator who actually handles the resources and make its allocation work done automatically. It is also entirely dependent upon the policies and the procedures that are being set out by the DevOps teams. It is that the entire life-cycle of the application and the handling of resource allocation were set by the team. Also, it is the controller and scheduler, who is playing an important role in the orchestration engine.
Cloud-native applications like, Kubernetes, is the one who exposes a flat network which is overlaid by as well as on existing networking topologies and primitives of cloud providers. Similarly, the native storage layer is often abstracted to expose logical volumes that are integrated with containers. Operators can allocate storage quotas and network policies that are accessed by developers and resource administrators. The infrastructure abstraction not only addresses the need for portability across cloud environments but also lets developers take advantage of emerging patterns to build and deploy applications. Orchestration managers become the deployment target, irrespective of the underlying infrastructure that may be based on physical servers or virtual machines, private clouds, or public clouds.
Now let us proceed further, and have a look at the best practices that are taken into consideration for the successful integration of cloud-native applications.
- Packaged as lightweight containers: Cloud-native applications are a collection of independent and autonomous services that are easily packaged and are lightweight containers. Unlike virtual machines, containers can scale-out and scale-in rapidly. Since the unit of scaling shifts to containers, infrastructure utilization is optimized.
- Developed with best-of-breed languages and frameworks: Each service of a cloud-native application is developed using the language and framework best suited for the functionality. Cloud-native applications are polyglot; services use a variety of languages, runtimes, and frameworks. For example, developers may build a real-time streaming service based on WebSockets, developed in Node.js, while choosing Python and Flask for exposing the API. The fine-grained approach to developing micro-services lets them choose the best language and framework for a specific job.
- Designed as loosely coupled micro-services: Services that belong to the same application discover each other through the application runtime. They exist independently of other services. Elastic infrastructure and application architectures, when integrated correctly, can
be scaled-out with efficiency and high performance. - Centered around APIs for interaction and collaboration: Cloud-native services use lightweight APIs that are based on protocols such as representational state transfer (REST), Google’s open-source remote procedure call (RPC), or NATS. REST is used as the lowest common denominator to expose APIs over hypertext transfer protocol (HTTP). For performance, GRPC is typically used for internal communication among services. NATS has publish-subscribe features that enable asynchronous communication within the application.
- Architected with a clean separation of stateless and statefull services: Services that are persistent and durable follow a different pattern that assures higher availability and resiliency. Stateless services exist independent of statefull services. There is a connection here to how storage plays into container usage. Persistence is a factor that has to be increasingly viewed in context with the state, statelessness, and — some would argue — micro-storage environments.
- Isolated from the server and operating system dependencies: Cloud-native applications don’t have an affinity for any particular operating system or individual machine. They operate at a higher abstraction level. The only exception is when a micro-service needs certain capabilities, including solid-state drives (SSDs) and graphics processing units (GPUs), that may be exclusively offered by a subset of machines.
- Deployed on self-service, elastic, cloud infrastructure: Cloud-native applications are deployed on virtual, shared, and elastic infrastructure. They may align with the underlying infrastructure to dynamically grow and shrink — adjusting themselves to the varying load.
- Managed through agile DevOps processes: Each service of a cloud-native application goes through an independent life cycle, which is managed through an agile DevOps process. Multiple continuous integration/continuous delivery (CI/CD) pipelines may work in tandem to deploy and manage a cloud-native application.
- Automated capabilities: Cloud-native applications can be highly automated. They play well with the concept of infrastructure as code. Indeed, a certain level of automation is required simply to manage these large and complex applications.
- Defined, policy-driven resource allocation: Finally, cloud-native applications align with the governance model defined through a set of policies. They adhere to policies such as central processing unit (CPU) and storage quotas, and network policies that allocate resources to services. For example, in an enterprise scenario, central IT can define policies to allocate resources for each department. Developers and DevOps teams in each department have complete access and ownership to their share of resources.
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