Streamlining Software Deployment: A Journey from Operating Systems to Kubernetes Orchestration
In this technical world, to implement any technology, it is necessary to create software/programs using a programming language. An Operating System (OS) is a software that acts as an interface between computer hardware components and the user. To install any operating system, it needs RAM/CPU computing units and a hard disk storage unit, as well as the image of the operating system. For installing the OS, storage is required. Installation involves storing the data of the OS.
An operating system (OS) consists of a group of programs, and to execute these programs, CPU/RAM resources are required. The process of initiating the OS is commonly referred to as booting.
The main use case for having an operating system is to run programs. There are different ways to obtain an operating system. One is bare metal, virtual machines, cloud instances, and containerization.
The traditional Bare Metal method involves installing the OS directly onto the computer. For more flexibility, virtual machines enable running multiple operating systems on one server. Cloud instances allow remote access to the OS, while containerization facilitates smoother application deployment across different setups.
Setting up an environment on bare metal involves installing the operating system directly onto the hardware, a process taking around 30 to 40 minutes. However, resource utilization is high, making it challenging to maintain a monolithic architecture.
· In a monolithic structure, each service has distinct dependency requirements, leading to long setup times and increased complexity.
· Tight coupling between components results in extended start-up and load times.
· Updating the application becomes cumbersome, requiring redeployment of the entire system, hindering the efficiency of continuous deployment.
· The monolithic structure is less reliable, as a single bug can disrupt the entire application.
· Scaling becomes challenging, and adopting new technologies is slowed down by the comprehensive impact of framework or language changes across the entire codebase.
· Even minor alterations in one section of the code can lead to unanticipated consequences throughout the system.
To address these challenges, a shift towards microservices is recommended. In a microservices architecture, each service is installed and deployed independently in different operating systems.
Virtualization can facilitate this by allowing multiple operating systems to boot up on top of a single operating system, utilizing underlying bare metal resources virtually. However, it’s important to note that resource usage remains consistent. Additionally, the time for installing the operating system, setting up dependencies on each server, and deploying the microservices may increase.
In virtualization, we can address the challenges posed by monolithic architecture, but we must then deal with boot-up times and dependency installations. To overcome these issues, a shift to deploying applications in cloud services is suggested, where the operating system’s boot-up time is reduced to just 2 to 3 minutes, and dependencies can be installed using a startup script. However, this approach may increase infrastructure costs and result in high resource utilization.
To further tackle these challenges, containerization can be employed. With containerization, the entire environment can be deployed within seconds Using a container engine, you can launch a container, i.e., an environment. Inside the container, you can deploy applications, whether they are web apps or microservices and resource utilization is minimized. Nowadays, applications are not deployed directly on bare metal. Industries use containers to deploy applications. In the market, there are different containerization products like Docker, Podman, Rocket, etc.
In a company, a developer writes code for an application in a specific programming language, and it is deployed in a container using a container engine. Since this application is critical for the company, clients connect to it over the internet. In this scenario, there is a risk that the container may experience downtime.
To ensure uninterrupted service, the IT team must monitor the application constantly. If, by chance, the container goes down, the IT team needs to promptly relaunch the application through the container. Fortunately, one advantageous aspect is that they can launch the container within a second, minimizing potential downtime.
Even though the container can be relaunched within a second, the challenge lies in relying on the IT team to manually restart the container once they become aware of the downtime.
Given the natural limitations of human response time and the sheer volume of containers to manage, the company risks damaging its reputation due to potential service disruptions. To address this challenge, it’s crucial to replace human efforts with a program running in the operating system. This program is responsible for actively monitoring the containers. The issue does not stem from the containers themselves but rather from the management process.
In the event of a container failure, the program should autonomously launch a new container and take on the additional responsibility of establishing a connection to the new container from the internet.
This proactive and automated approach helps minimize downtime and ensures a more reliable and responsive system. The program responsible for managing containers is known as Kubernetes, often abbreviated as kube or k8s.
Kubernetes is an open-source container orchestration tool designed to automate various aspects of deployment, management, and auto-scaling for containerized applications. By leveraging container orchestration, Kubernetes eliminates the need for manual intervention in processes such as deployment and scaling, streamlining and automating these tasks for increased efficiency and reliability.
Container orchestration allows you to build application services spanning multiple containers, schedule containers across a cluster, scale them, and manage their health over time. For example, if the application can handle 100 requests but exceeds that threshold or experiences high CPU and RAM utilization, Kubernetes will launch one or more containers with identical copies, resulting in parallel execution. Kubernetes ensures connectivity between these containers through a load balancer, adjusting the container count based on demand.
On top of the operating system, a container engine is installed, and containers launch on it. In case a container goes down, Kubernetes takes care of restarting it.
If a single node fails, all containers on that node are affected; to address this, Kubernetes monitors multiple container hosts in parallel using a multi-node cluster architecture.
If a container fails due to issues with the Docker host, an identical copy of the container will launch on another Docker host in a different node. Worker nodes host the container engine responsible for launching containers, and the master controller program monitors these worker nodes.
It’s important to note that Kubernetes doesn’t directly launch applications but assists containers in launching them. Kubernetes launches pods, and the container engine within each pod launches the container where the application runs. Kubernetes adds another layer by wrapping containers with a structure called a pod, which it manages.
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