Containers vs. Virtual Machines: Key Differences and Use Cases

With all this buzz around technology, there is a discussion around containers versus virtual machines (VMs). Containers and VMs are both virtualization technologies that package computing environments housing various IT components and isolate them from the underlying system. However, these tools have separate strengths and weaknesses, making them suitable for different applications. 

By distinguishing between containers and VMs, you can deploy applications faster, streamline troubleshooting processes, allocate resources more efficiently, and maintain system security.

So, let’s discuss what they do, their differences, and how they compare to virtualization technologies. Let’s get started.

What Are Containers?

Containers are used to package and isolate applications and their dependencies in a lightweight and portable environment. Since they carry the application code, system tools, and settings, containers allow applications to run smoothly in various computing environments, whether on a developer’s laptop, in a traditional data center, or even in the cloud.

What Are Virtual Machines?

Virtual Machines, on the other hand, are designed to provide hardware virtualization. They are software-created computers that simulate real computers with their own CPU, memory, storage, and operating systems. This is done so they can operate in isolated environments to maintain better organization and security.

Differences Between a Container vs. a Virtual Machine

Now, let’s examine the significant differences that make containers and VMs favorable in different circumstances and compare them in various aspects.

Container vs VM: How They Work

Containers function by bundling all application-related resources, such as code, runtime, libraries, and configurations, into a container image that specifies all these components. The container runtime then uses the details within the image to allocate resources from the host OS, similar to assembling a Lego model using the image’s instructions. This ensures that each container has its own isolated space within the OS, allowing it to operate without interruption.

VMs utilize a hypervisor, which mediates between the physical hardware and the VMs. The hypervisor allocates the resources needed for each VM, such as CPU, memory, and storage. The VM itself is a software file that includes the virtual hardware configuration. This allows the VM to run programs and function like a regular computer.

Container vs. Virtual Machine: Isolation

Alright, let’s unpack arguably one of the most important aspects of both of these technologies: the isolation capabilities. While they share similarities to a certain extent, this is where things truly begin to diverge.

First, let’s take a look at containers. Although containers share the host kernel, they manage to stay completely isolated from each other using namespaces and cgroups. Their roles are such that namespaces isolate processes and networks from each other, while cgroups manage resource allocations and utilization. 

Containers share the host machine’s OS kernel. While this gives you considerably lower resource overhead than VMs, you might be vulnerable to security threats if you don’t provide your container with the proper security configurations.

Now, let’s compare virtual machines to the isolation provided by containers. One key factor to consider is that each VM runs on its own dedicated operating system, acting as a barrier providing a stronger isolation level between different instances. This isolation is achieved through the hypervisor, which manages access to physical resources and network interfaces. Although VMs offer better isolation in this regard, it comes at the cost of requiring more resources and longer boot-up times.

Container vs. Virtual Machine: Portability

Regarding portability, we don’t mean physically transporting containers or VMs; we refer to how easily applications can be moved from one computing environment to another. 

Thanks to their lightweight nature, containers boast excellent portability. This is because container images are only a few megabytes, containing just the application code and its dependencies, such as libraries. This makes containers ideal for various deployment methods, like cloud deployments, where applications might need to transition between different platforms or infrastructures. 

However, containers’ portability does have limitations; for example, when running on Linux distributions, they may not function properly if specific libraries are missing in the environment.

Because VMs run their own OS, they can be ported between different computers as long as the system’s hardware architecture is compatible. However, since VMs also require their own compatible guest OS, the larger GB requirement contributes to their notorious startup slowness. Apart from that, porting VMs between different environments with different OSs can be challenging as it requires configuration adjustments.

Container vs. Virtual Machine: Tools

By now, you probably understand the basic functionalities of these technologies and how they work. However, you can step this up by using some tools. Both containers and VMs can be paired with tools to maximize their use.

Here are some recommended tools you could use for containers.

Container Runtime Engines can create containers, execute them, and delete them. The most popular option for Container Runtime Engines is Docker Engine because it offers many features. 

Next, let’s have a look at such key features: 

• Docker is compatible with different OSs like Windows, Linux, and macOS, giving it the versatility to help developers target different environments. 
• It gives users a friendly user experience with features like Dockerfiles to define container images and the Docker CLI to manage containers with intuitive commands.
• There’s a vast ecosystem of tools and integrations, including public registries like Docker Hub, where you can get pre-built container images, and orchestration platforms like Kubernetes.
• Docker is an open-source tool with a community contributing to its development and accessibility, making it available to a wide range of users without any licensing costs.

The main tool used for VMs is the hypervisor. This tool manages resource allocation between VMs and provides isolation. Popular hypervisors include VMware, VirtualBox, and Hyper-V.

When to Use Containers

The scenarios detail key areas in which to use containers.

Building Cloud-Native Apps

Cloud-native applications are specifically designed to take advantage of cloud environments’ scalability and elasticity. Because containers are lightweight and portable, they would be great for deploying cloud-native applications. Developers can deploy and scale applications quickly and efficiently with containers.

Microservices Architecture

If you know enough about virtualization to have heard of containers and VMs, you’ve probably also heard of microservices. If not, well, the name is pretty self-explanatory. The microservice architecture breaks down an application into smaller services. Containers are ideal for packaging these microservices because each service can be developed and deployed in separate containers, housing the microservice and its dependencies.

Stateless Applications

Seeing as these types of applications don’t maintain any data on their own and rely on external storage, containers are a great fit for this. Due to simple management, easy scaling, and portability, stateless applications can thrive within containers. Since containers are ephemeral and can be recreated easily, you don’t have to worry about data loss when a container restarts.

When to Use Virtual Machines

Now that we’ve seen where containers can be used, let’s look at virtual machines’ applications.

Legacy Applications

Legacy applications are older programs designed for specific operating systems. People looking to adopt legacy systems often face the issue that modern systems rarely support them. You can get around this by using a virtual machine (VM), which allows you to install the older OS within the VM and run the legacy application there.

Monolithic Workloads

These are programs built as a single unit. Because of their complex dependencies, they can be difficult to deploy and scale. VMs can be a solution because they provide a way to encapsulate these applications along with the associated dependencies, making it easier to manage and scale them as you go.

Workloads With High CPU or Memory Demands

Workloads like scientific computing, 3D rendering, and machine learning (ML) require a lot of computing resources, such as CPU and memory. Using VMs, you can set aside specific amounts of processing power and memory for each virtual machine. This can help ensure these resource-intensive applications receive the needed resources without affecting the rest of the system.

Key Takeaways About Containers and Virtual Machines

Although VMs and containers are both highly competent virtualization tools, they offer distinct advantages that make them suitable for different scenarios. Containers excel in portability speed and provide tools like Docker for swift operations, while VMs prioritize tight security through isolation.