Contrary to popular belief, Lorem Ipsum is not simply random text. It has roots in a piece of classical Latin literature from 45 BC, making it over 2000 years old. Richard McClintock, a Latin professor at Hampden-Sydney College in Virginia, looked up one of the more obscure Latin words, consectetur, from a Lorem Ipsum passage, and going through the cites of the word in classical literature,

discovered the undoubtable source. Lorem Ipsum comes from sections 1.10.32 and 1.10.33 of "de Finibus Bonorum et Malorum" (The Extremes of Good and Evil) by Cicero, written in 45 BC. This book is a treatise on the theory of ethics, very popular during the Renaissance. The first line of Lorem Ipsum, "Lorem ipsum dolor sit amet..", comes from a line in section 1.10.32.

Some resources, notably memory and storage space, have a notion of "location", and one can distinguish contiguous allocations from non-contiguous allocations. For example, allocating 1 GiB of memory in a single block, versus allocating it in 1,024 blocks each of size 1 MiB. The latter is known as fragmentation, and often severely impacts performance, so contiguous free space is a subcategory of the general resource of storage space.

One can also distinguish compressible resources from in compressible resources.^[1] Compressible resources, generally throughput ones such as CPU and network bandwidth, can be throttled benignly: the user will be slowed down proportionally to the throttling, but will otherwise proceed normally. Other resources, generally storage ones such as memory, cannot be throttled without either causing failure (if a process cannot allocate enough memory, it typically cannot run) or severe performance degradation, such as due to thrashing (if a working set does not fit into memory and requires frequent paging, progress will slow significantly). The distinction is not always sharp; as mentioned, a paging system can allow main memory (primary storage) to be compressed (by paging to hard drive (secondary storage)), and some systems allow discardable memory for caches, which is compressible without disastrous performance impact. Electrical power is to some degree compressible: without power (or without sufficient voltage) an electrical device cannot run, and will stop or crash, but some devices, notably mobile phones, can allow degraded operation at reduced power consumption, or can allow the device to be suspended but not terminated, with much lower power consumption.

The simplest computational resources are computation time, the number of steps necessary to solve a problem, and memory space, the amount of storage needed while solving the problem, but many more complicated resources have been defined.^{[citation needed]}

A computational problem is generally^{[citation needed]} defined in terms of its action on any valid input. Examples of problems might be "given an integer n, determine whether n is prime", or "given two numbers x and y, calculate the product x*y". As the inputs get bigger, the amount of computational resources needed to solve a problem will increase. Thus, the resources needed to solve a problem are described in terms of asymptotic analysis, by identifying the resources as a function of the length or size of the input. Resource usage is often partially quantified using Big O notation.

Computational resources are useful because we can study which problems can be computed in a certain amount of each computational resource. In this way, we can determine whether algorithms for solving the problem are optimal and we can make statements about an algorithm's efficiency. The set of all of the computational problems that can be solved using a certain amount of a certain computational resource is a complexity class, and relationships between different complexity classes are one of the most important topics in complexity theory.

Utility computing, or The Computer Utility, is a service provisioning model in which a service provider makes computing resources and infrastructure management available to the customer as needed, and charges them for specific usage rather than a flat rate. Like other types of on-demand computing (such as grid computing), the utility model seeks to maximize the efficient use of resources and/or minimize associated costs. Utility is the packaging of system resources, such as computation, storage and services, as a metered service. This model has the advantage of a low or no initial cost to acquire computer resources; instead, resources are essentially rented.

This repackaging of computing services became the foundation of the shift to "on demand" computing, software as a service and cloud computing models that further propagated the idea of computing, application and network as a service.

There was some initial skepticism about such a significant shift.^[1] However, the new model of computing caught on and eventually became mainstream.

IBM, HP and Microsoft were early leaders in the new field of utility computing, with their business units and researchers working on the architecture, payment and development challenges of the new computing model. Google, Amazon and others started to take the lead in 2008, as they established their own utility services for computing, storage and applications.

Utility computing can support grid computing which has the characteristic of very large computations or sudden peaks in demand which are supported via a large number of computers.

"Utility computing" has usually envisioned some form of virtualization so that the amount of storage or computing power available is considerably larger than that of a single time-sharing computer. Multiple servers are used on the "back end" to make this possible. These might be a dedicated computer cluster specifically built for the purpose of being rented out, or even an under-utilized supercomputer. The technique of running a single calculation on multiple computers is known as distributed computing.

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