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For others, the effort to customize an install to a virtualization framework might not be worth the payoff; gains in performance or convenience may very well be minimal. Typical library virtualization schemes mostly utilize .

Libraries with staff who develop software likely have more complex requirements. The systems already mentioned — KVM, VMware, Xen, Virtualbox, along with products like DOSBox [11], a multiplatform emulator specifically written to run DOS games — are machine-level emulators.

To run most of the serious business of libraries, you needed serious equipment, and, as a result, machine rooms were often jumbled messes of shelves, wires and air conditioning units.

With the advent of Linux running on microcomputers, these rooms became slightly smaller and maybe slightly less complex, but it wasn’t until the early 2000s that the real sea change came for the server room — the rapid adoption of easily implementable virtualization [1], which allowed running multiple discrete operating systems on a single machine.

Although virtualization in the modern sense actually happened as early as the 1960s with VMs for IBM System/360 machines [2], and both the 286 and 386 chips contained some species of virtualization [3], it wasn’t until 2001 when VMWare introduced its x86 virtualization products that Linux-based virtualization really took off.

With technologies like KVM [4] and Xen [5] looming large for local deployment and products like Amazon Web Services [6] and the Open Stack [7] framework claiming the cloud, the jumbled, messy, white-box-PC-and-CDROM-tower-laden server room is more or less extinct; today, you can create multiple virtual machines out of minimal physical machines.

At its core, Docker is a virtualization framework focused around running applications and not around emulating hardware, which seems facile at first but underscores the critical difference between operating system-level virtualization software like Docker and machine-level virtualization.

To demonstrate how Docker works, I’ve created a container that converts the Markdown that this article was originally written in into HTML via pandoc [25] and serves it using the Python Simple HTTPServer module.

You can get the Docker container from the Github repository [26] for this article.

But this type of virtualization can be resource-intensive and inefficient.

When running multiple virtual machines on a single computer, you might quickly run into the limits of the physical machine by allocating large disk drives or carving out multiple Gigabytes of dedicated RAM for each VM instance.

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