IT business enterprises are highly volatile and, staying true to the quality of dynamism, they're undergoing a drastic change. This change involves a shift in business operations from on-premises to the cloud, or from one cloud solution to another. To put it plainly, cloud migration can be defined as the shifting of digital business operations to the cloud. But how does distributed computing act as a catalyst to shifting away from monolithic computing architecture?
Having cloud networking to back your business operations also provides the added advantage of removing hardware equipment from the on-premises data center. Freeing up the on-premises data center from hardware components during the process of cloud migration has a plethora of benefits, such as improved resource allocation and scalability. The concept of cloud transparency is thus crucial to meet the SLAs governing cloud computing to ensure that business enterprise requirements are met by the external cloud service provider.
Organizational agility is one thing that business enterprises are in dire need of due to various external factors, such as changes in the environment in which they are operating. This situation is aggravated by the digitalization drive happening today, which in turn is caused by customer-driven requests. This level of drastic change can only be incorporated if the business organization is ready to accept the cloud since it's a IT infrastructure that is highly agile and rapidly scalable.
All cloud-native applications are tailor-made to function in the cloud, and all cloud computing deployment models are metered services. Metered services, commonly known as pay-per-use models, leave users with unlimited resources at their disposal but only needing to pay for the resources which are actually being utilized. This is an aberration from the traditional practice where the usage of monolithic services always extracted a fixed cost from the user. The versatility provided by cloud-native applications in integration, maintenance, development, and usage of resources drives down the overhead costs in the IT environment.
Cloud native adaptability refers to applications that are developed to run specifically in any type of cloud (private, public, or hybrid). While discussing the concept of cloud-native adaptability, the emphasis is on where the application resides in a particular instance, and its build and deployment locations are completely irrelevant. This is achieved with the help of components known as microservices, which help the application to blend into any cloud environment. Microservices is a unique approach in which a single application is an agglomeration of multiple services that are independent of each other. The random nature of the cloud makes it impossible to track an application running on it; this is where microservices comes in. Microservices can be individually scaled and automated, and orchestrations can be made seamlessly.
Microservices was actively introduced to tackle the varied constraints posed by monolithic applications, such as the difficulty of redeploying an application even for a minute change and also only having a sole, vertical scaling option provided.
AWS defines microservice architecture as "building an application as independent components that run each application process as a service, with these services communicating via a well-defined interface using lightweight APIs." While discussing microservices, another concept that often brings ambiguity is containers. In developer's jargon, a container refers to packages of software that contain all the key elements to making an application versatile in any environment. Containers are usually used to host microservices since microservices deals with only the design of the software.
Automation of the various processes associated with containers, such as networking, scaling, availability, and life cycle management, is taken care of by container orchestration tools. Cloud orchestration is unavoidable considering the large number of containers deployed in business organizations, making automation virtually impossible to avoid in cloud management. Deploying many containers may also create a bottleneck in the DevOps pipeline, which may lead to a situation called "integration hell" where the vulnerabilities in the integration process are exposed.
The most common container orchestration tool is Kubernetes, an open-source tool developed by Google. Kubernetes helps construct applications that have multiple containers, scale those containers, and manage their health.
Another container management system is Docker, which can either be used in conjunction with Kubernetes or can be deployed individually. Even though Docker and Kubernetes function similarly, Docker is used to consolidate containerized applications on one node, while Kubernetes is used to run them across a distributed processing network.
Cloud nativity can be achieved by embracing two key technologies:
Cloud-native observability leans towards full-stack observability, through which IT teams can expressly gain deep visibility into the health and performance of applications using telemetry data (logs, traces, and metrics). Scalability is the prime reason there is a huge migration to cloud-based infrastructure. Typical network and application monitoring tools cannot cope with the phenomenal amount of telemetry monitoring data that is extracted from cloud-native environments because cloud nativite adaptability is built using multiple small containers that are isolated from each other. This actively pushes the need for an observability tool that is led from the front by artificial intelligence (AI).
AI ensures that nothing is missed out on in a cloud environment—its superior analytic capacity keeps a close eye on parameters like traffic, latency, and data loss. This proactive monitoring by the observability tool also helps site reliability engineering by pinpointing errors down to the node in the cloud network along with providing path-based analysis. This improves the end-user experience.
Cloud nativite adaptability can also be efficiently implemented with observability features like root cause analysis that help to close in on the source of any untoward incident. This is particularly helpful in a heavily segmented and discrete cloud-native environment.