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This chapter is from the book

This chapter is from the book

What Is UML Used For?

During the five years since UML began, I have seen steady growth in the UML user base and in the number and variety of applications and uses for UML. My users have been nothing if not inventive, and I have seen uses for UML that I would never have thought of.

Server Consolidation

Naturally, the most common applications of UML are the obvious ones. Virtualization has become a hot area of the computer industry, and UML is being used for the same things as other virtualization technologies. Server consolidation is a major one, both internally within organizations and externally between them. Internal consolidation usually takes the form of moving several physical servers into the same number of virtual machines running on a single physical host. External consolidation is usually an ISP or hosting company offering to rent UML instances to the public just as they rent physical servers. Here, multiple organizations end up sharing physical hardware with each other.

The main attraction is cost savings. Computer hardware has become so powerful and so cheap that the old model of one service, or maybe two, per machine now results in hardware that is almost totally idle. There is no technical reason that many services, and their data and configurations, couldn't be copied onto a single server. However, it is easier in many cases to copy each entire server into a virtual machine and run them all unchanged on a single host. It is less risky since the configuration of each is the same as on the physical server, so moving it poses no chance of upsetting an already-debugged environment.

In other cases, virtual servers may offer organizational or political benefits. Different services may be run by different organizations, and putting them on a single physical server would require giving the root password to each organization. The owner of the hardware would naturally tend to feel queasy about this, as would any given organization with respect to the others. A virtual server neatly solves this by giving each service its own virtual machine with its own root password. Having root privileges in a virtual machine in no way requires root privileges on the host. Thus, the services are isolated from the physical host, as well as from each other. If one of them gets messed up, it won't affect the host or the other services.

Moving from production to development, UML virtual machines are commonly used to set up and test environments before they go live in production. Any type of environment from a single service running on a single machine to a network running many services can be tested on a single physical host. In the latter case, you would set up a virtual network of UMLs on the host, run the appropriate services on the virtual hosts, and test the network to see that it behaves properly.

In a complex situation like this, UML shines because of the ease of setting up and shutting down a virtual network. This is simply a matter of running a set of commands, which can be scripted. Doing this without using virtual machines would require setting up a network of physical machines, which is vastly more expensive in terms of time, effort, space, and hardware. You would have to find the hardware, from systems to network cables, find some space to put it in, hook it all together, install and configure software, and test it all. In addition to the extra time and other resources this takes, compared to a virtual test environment, none of this can be automated.

In contrast, with a UML testbed, this can be completely automated. It is possible, and fairly easy, to full automate the configuration and booting of a virtual network and the testing of services running on that network. With some work, this can be reduced to a single script that can be run with one command. In addition, you can make changes to the network configuration by changing the scripts that set it up, rather than rewiring and rearranging hardware. Different people can also work independently on different areas of the environment by booting virtual networks on their own workstations. Doing this in a physical environment would require separate physical testbeds for each person working on the project.

Implementing this sort of testbed using UML systems instead of physical ones results in the near-elimination of hardware requirements, much greater parallelism of development and testing, and greatly reduced turnaround time on configuration changes. This can reduce the time needed for testing and improve the quality of the subsequent deployment by increasing the amount and variety of testing that's possible in a virtual environment.

A number of open source projects, and certainly a much larger number of private projects, use UML in this way. Here are a couple that I am aware of.

  • Openswan (http://www.openswan.org), the open source IPSec project, uses a UML network for nightly regression testing and its kernel development.
  • BusyBox (http://www.busybox.net), a small-footprint set of Linux utilities, uses UML for its testing.

Education

Consider moving the sort of UML setup I just described from a corporate environment to an educational one. Instead of having a temporary virtual staging environment, you would have a permanent virtual environment in which students will wreak havoc and, in doing so, hopefully learn something.

Now, the point of setting up a complicated network with interrelated services running on it is simply to get it working in the virtual environment, rather than to replicate it onto a physical network once it's debugged. Students will be assigned to make things work, and once they do (or don't), the whole thing will be torn down and replaced with the next assignment.

The educational uses of UML are legion, including courses that involve any sort of system administration and many that involve programming. System administration requires the students to have root privileges on the machines they are learning on. Doing this with physical machines on a physical network is problematic, to say the least.

As root, a student can completely destroy the system software (and possibly damage the hardware). With the system on a physical network, a student with privileges can make the network unusable by, wittingly or unwittingly, spoofing IP addresses, setting up rogue DNS or DHCP servers, or poisoning ARP (Address Resolution Protocol) [1] caches on other machines on the network.

These problems all have solutions in a physical environment. Machines can be completely reimaged between boots to undo whatever damage was done to the system software. The physical network can be isolated from any other networks on which people are trying to do real work. However, all this takes planning, setup, time, and resources that just aren't needed when using a UML environment.

The boot disk of a UML instance is simply a file in the host's filesystem. Instead of reimaging the disk of a physical machine between boots, the old UML root filesystem file can be deleted and replaced with a copy of the original. As will be described in later chapters, UML has a technology called COW (Copy-On-Write) files, which allow changes to a filesystem to be stored in a host file separate from the filesystem itself. Using this, undoing changes to a filesystem is simply a matter of deleting the file that contains the changes. Thus, reimaging a UML system takes a fraction of a second, rather than the minutes that reimaging a disk can take.

Looking at the network, a virtual network of UMLs is by default isolated from everything else. It takes effort, and privileges on the host, to allow a virtual network to communicate with a physical one. In addition, an isolated physical network is likely to have a group of students on it, so that one sufficiently malign or incompetent student could prevent any of the others from getting anything done. With a UML instance, it is feasible (and the simplest option) to give each student a private network. Then, an incompetent student can't mess up anyone else's network.

UML is also commonly used for learning kernel-level programming. For novice to intermediate kernel programming students, UML is a perfect environment in which to learn. It provides an authentic kernel to modify, with the development and debugging tools that should already be familiar. In addition, the hardware underneath this kernel is virtualized and thus better behaved than physical hardware. Failures will be caused by buggy software, not by misbehaving devices. So, students can concentrate on debugging the code rather than diagnosing broken or flaky hardware.

Obviously, dealing with broken, flaky, slightly out-of-spec, not-quite-standards-compliant devices are an essential part of an expert kernel developer's repertoire. To reach that exalted status, it is necessary to do development on physical machines. But learning within a UML environment can take you most of the way there.

Over the years, I have heard of education institutions teaching many sort of Linux administration courses using UML. Some commercial companies even offer system administration courses over the Internet using UML. Each student is assigned a personal UML, which is accessible over the Internet, and uses it to complete the coursework.

Development

Moving from system administration to development, I've seen a number of programming courses that use UML instances. Kernel-level programming is the most obvious place for UMLs. A system-level programming course is similar to a system administration course in that each student should have a dedicated machine. Anyone learning kernel programming is probably going to crash the machine, so you can't really teach such a course on a shared machine.

UML instances have all the advantages already described, plus a couple of bonuses. The biggest extra is that, as a normal process running on the host, a UML instance can be debugged with all the tools that someone learning system development is presumably already familiar with. It can be run under the control of gdb, where the student can set breakpoints, step through code, examine data, and do everything else you can do with gdb. The rest of the Linux development environment works as well with UML as with anything else. This includes gprof and gcov for profiling and test coverage and strace and ltrace for system call and library tracing.

Another bonus is that, for tracking down tricky timing bugs, the debugging tool of last resort, the print statement, can be used to dump data out to the host without affecting the timing of events within the UML kernel. With a physical machine, this ranges from extremely hard to impossible. Anything you do to store information for later retrieval can, and probably will, change the timing enough to obscure the bug you are chasing. With a UML instance, time is virtual, and it stops whenever the virtual machine isn't in the host's userspace, as it is when it enters the host kernel to log data to a file.

A popular use for UML is development for hardware that does not yet exist. Usually, this is for a piece of embedded hardware—an appliance of some sort that runs Linux but doesn't expose it. Developing the software inside UML allows the software and hardware development to run in parallel. Until the actual devices are available, the software can be developed in a UML instance that is emulating the hardware.

Examples of this are hard to come by because embedded developers are notoriously close-lipped, but I know of a major networking equipment manufacturer that is doing development with UML. The device will consist of several systems hooked together with an internal network. This is being simulated by a script that runs a set of UML instances (one per system in the device) with a virtual network running between them and a virtual network to the outside. The software is controlling the instances in exactly the same that it will control the systems within the final device.

Going outside the embedded device market, UML is used to simulate large systems. A UML instance can have a very large amount of memory, lots of processors, and lots of devices. It can have more of all these things than the host can, making it an ideal way to simulate a larger system than you can buy. In addition to simulating large systems, UML can also simulate clusters. A couple of open source clustering systems and a larger number of cluster components, such as filesystems and heartbeats, have been developed using UML and are distributed in a form that will run within a set of UMLs.

Disaster Recovery Practice

A fourth area of UML use, which is sort of a combination of the previous two, is disaster recovery practice. It's a combination in the sense that this would normally be done in a corporate environment, but the UML virtual machines are used for training.

The idea is that you make a virtual copy of a service or set of services, mess it up somehow, and figure out how to fix it. There will likely be requirements beyond simply fixing what is broken. You may require that the still-working parts of the service not be shut down or that the recovery be done in the least amount of time or with the smallest number of operations.

The benefits of this are similar to those mentioned earlier. Virtual environments are far more convenient to set up, so these sorts of exercises become far easier when virtual machines are available. In many cases, they simply become possible since hardware can't be dedicated to disaster recovery practice. The system administration staff can practice separately at their desks, and, given a well-chosen set of exercises, they can be well prepared when disaster strikes.

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