The buzz around the cloud

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?

Benefits of cloud computing

  • Easier scaling: Cloud resources like virtual machines can be accessed using an IP address uniquely associated with that virtual machine. This swift access eliminates the issue of server underutilization and also ups the auto-scalability of virtual resources according to the traffic; if there is a sudden rise in the resources required in the cloud infrastructure, autoscaling kicks in. If metrics like CPU utilization, disk utilization, or memory utilization breach predefined threshold values, the server space can be scaled up or down as per the circumstance.
  • Enables cloud transparency: Changes taking place in the cloud environment need to be continuously monitored because the dynamic nature of the cloud brings with it constant changes in the servicing and the prices required to maintain it. Thus, cloud transparency, which is a continuous process, needs to be viewed as an integral element of any cloud setup, irrespective of its type. Parameters like uptime of the server and availability of the network need to have transparent threshold levels that can be subjected to change according to the changing requirements of the system.
  • Better collaboration: A proper internet connection ensures that users can access data from anywhere without facing limitations imposed by the geography of the place they initiate the request from.
  • Data protection: Cloud vendors provide protection from data loss by offering disaster recovery features. All the crucial workloads can be shifted to disaster recovery sites in case of a contingency, and the normal workflow can be resumed.

Impact of the cloud on budgetary requirements

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 tool

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.

The building blocks of cloud-native applications

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.

Kubernetes: The container orchestrator

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.

Achieving cloud-native adaptability

Cloud nativity can be achieved by embracing two key technologies:

  • Secrets management: All applications in an IT environment rely on some sort of credential to establish communication between other applications and data. These credentials are referred to as secrets, and they perform the function of protecting the containers. DevOps activities increasingly rely on containers to act as a catalyst in the development process. So, these containers are secured using secrets management, wherein the security system uses role-based access control (RBAC) to authenticate requests to access the containers.
  • Continuous integration (CI): Multiple cloud orchestration tools are available for DevOps engineers to use, enabling them to run and maintain applications across clusters. However, this is only possible if their cloud-native CI process has an efficient cloud orchestration integration that permits the addition and management of multiple clusters.

What's in store for cloud-native adaptability: Observability lending a hand

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.

OpManager Plus: A holistic observability solution

  • With containerized applications gathering much traction, Kubernetes too has become unavoidable ancillary software. The health of Kubernetes-hosted applications running inside a cluster needs to be actively monitored to identify any errors. Mean time to detect can be brought down with a proper Kubernetes monitoring tool like OpManager Plus, ensuring that the reliability quotient of the applications is always high.
  • Managing all Docker deployments irrespective of their location and orchestrator is a difficult task. Monitor all the key Docker performance metrics and generate reports using OpManager Plus's Docker monitoring.
  • The challenge of monitoring containers, container orchestrators, and distributed applications deployed using containers has been made easier with OpManager's cloud monitoring.
  • Automate the process of setting thresholds for key metrics and help the process of autoscaling in your cloud-native environment.Adaptive thresholds in OpManager make the process of manual threshold setting redundant.
  • Monitor all the layers of your cloud infrastructure to get instant reports on the source of problems identified with AI-enabled root cause analysis. Enabled by observability, OpManager Plus's root cause analysis feature has taken its monitoring capabilities to greater heights.

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