Today's computers have much processing power, including fast CPU speeds and storage options for your resources. Packaging code with all required components like libraries, frameworks, and other dependencies in an isolated container lets you deploy and scale your applications faster.  

With a new container, you can run your packages on every runtime and on any infrastructure you want. Containers let you run your applications independently of the environment or operating system, package all your dependencies within the container, and isolate it, making it easier to manage.

In this article, we'll look at the following:

  • Containerization
  • Virtualization
  • Containers vs. Virtual Machine

Virtual Machines

In the traditional deployment scenarios, when working on deploying your application, you will have hardware and an operating system where you will run multiple applications for every package and library using the Hypervisor. In the long run, you'll be consuming a lot of processors, RAM, storage, network, and cards. For example, if you are running Python and other libraries, each will have its packages and dependencies. In the long run, there can be a conflict as some applications need some libraries specific to some versions, and others might need a different version of the same library. The installation process in the traditional VM system is time-consuming as you must run multiple commands that check for various packages. In virtual machines, we have multiple operating systems running on one piece of hardware, and a host operating system that acts as a Hypervisor that determines who gets access to each hardware. In the  hardware, we have partitions were we install the full operating system with all binaries and libraries. Each partition has its own completely separate operating system all running on top of the shared Hypervisor.

With our virtual machines, we can split our hypervisors into different machines, and we're creating separate servers and workstations out of one, and each machine is independent of the other. The virtual machine simulates the hardware for you to install all the applications and dependencies. This lets you run multiple applications on the same infrastructure however because each VM is running it's own operating system they tend to be bulky and slow.  


Containerization

Containers make building, shipping, and deploying your applications more accessible and straightforward. You can move the container around your Workflow without worrying about the dependencies it needs to run. In Docker, the container engine will help you create and package the dependencies and libraries with it, making it faster and easier for you to ship.  Containers provide virtualization for the OS and all your applicaitons and resources are run by a single Kernel which makes everything faster and more efficient. The Container Engine replaces the hypervisor in Containerization. The Container Engine will expose parts of the host operating system into the partitions.  The partitions only contains the binaries and libraries that you need for your workload. The partitions doesn't contain the entire operating system.

Containers provide infinite portability because we can define what we want to be built in a single Dockerfile. Containers allow you to package your workloads and run a single command to install all your dependencies seamlessly. In containerization, we are dealing with process isolation, where you want to be sure that your applications can only see what's necessary for them to build and run.

Key Differences

Feature Virtual Machine Containers
Startup Time Minutes Milliseconds
Disk Space Consumes more disk space with more VMs Use container images with the particular application.
Portability Can move between hardware if they are running the same hypervisor. Very easy to move around and can run anywhere: Server, laptop, AWS Cloud, Azure.
Efficiency Have entire copies of operating systems, so they consume more RAM, CPU, and Disk Space. Far more efficient with more performance and efficiency gains.
Operating system/Kernel Dedicated operating system and kernels per virtual machine. If one VM crashes, it doesn’t affect the other. Uses a shared Kernel, which makes them more efficient and faster, but if the Kernel crashes, it affects the entire container.
Provisioning VMs can easily be moved to a new host Containers are destroyed and recreated rather than moving
Usage More resource usage Less resource usage

Conclusion

Many containers and services are built on hardware that is much cheaper than standard servers, and they can be scaled and placed in a single host using Containers. Virtualization helps solve the problem of underused resources by creating a virtualization layer between the hardware components and the user. Containerization makes it possible to build and run your applications anywhere with a compatible operating system and control plane, thus providing operational efficiency.

We can eventually run virtual machines and containers not as contending systems but as tools that can work together depending on our use case.

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