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Broad network access means cloud capabilities are reachable over a network through standard mechanisms usable by different kinds of client platforms. NIST lists phones, tablets, laptops and workstations as examples.^[ref]

For example, a browser interface and an application programming interface can expose the same service to interactive users and automated analyses. Network accessibility does not imply public authorization: a private cloud can restrict authenticated clients while still using broadly supported network interfaces. Latency, bandwidth and intermittent connectivity affect how an application should divide work between client and service; an accessible remote operation is not equivalent to a local memory access.

Cloud computing supplies configurable computation resources through a computer network, drawing on a shared pool that users can obtain and release with little provider intervention. The NIST definition distinguishes five characteristics: on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service. Together these describe how capacity is requested, shared, scaled and accounted for.^[ref]

For example, a group running a numerical simulation can request several virtual machines, execute independent runs, save the results and release the virtual machines. Rapid elasticity concerns changing allocated capacity as demand changes; measured service records usage such as processor time or storage. A physically distributed system can have a single operator: physical distribution and administrative control answer different questions.

Measured service is the cloud-computing characteristic in which resource use is monitored, controlled and reported through metering appropriate to the service. Examples include processor-hours, stored bytes, network transfer and active accounts. Metering makes usage visible to the provider and consumer; it does not require one universal billing formula.^[ref]

For example, a batch of simulations can record the number of machine-hours consumed independently of the number of simulations completed. A failed run may still consume resources. Selecting a meaningful unit therefore requires distinguishing allocated capacity, elapsed use and delivered work. These measurements support accounting and capacity decisions without by themselves measuring the scientific value or correctness of the results.

Multi-tenancy lets distinct consumers share a software service or computing infrastructure while retaining separate data and permitted operations. A tenant is the consumer or administrative grouping treated as one isolated customer. Resource pooling commonly assigns shared physical resources dynamically among tenants.^[ref]

For example, two laboratories can use one hosted database application. Each laboratory's users must access only their permitted records, even when both laboratories' records occupy the same database relation. Checks on each database query and limits on resource use serve different purposes: data isolation does not prevent one tenant's expensive work from delaying another. Separate virtual machines are one possible implementation boundary, rather than a requirement of multi-tenancy.

On-demand self-service lets a consumer provision a computing capability when needed without requiring a human interaction with the provider for each request. The consumer might request server time or network storage through a portal or application programming interface.^[ref]

For example, an authorized research workflow can request a temporary machine, run an analysis and release it through automated calls. The provider can still enforce quotas, permitted configurations and account policy. Self-service describes how a request is fulfilled; it does not promise unlimited capacity or remove the need for authorization. Automating repeated provisioning makes temporary resources practical while leaving lifecycle and data-retention choices explicit.

Peter Mell coauthored The NIST Definition of Cloud Computing by Peter Mell and Timothy Grance in 2011. The publication identifies on-demand self-service, broad network access, resource pooling, rapid elasticity and measured service as essential characteristics.^[ref]

The classification separates what service a consumer controls from how it is deployed. Infrastructure, platform and software services expose different management boundaries, while public, private, community and hybrid deployments describe organizational arrangements. A service can therefore be publicly accessible without giving its consumers control of the underlying operating systems.

Rapid elasticity is the ability to provision and release computing capacity quickly in response to demand, often automatically. The consumer may experience capacity as readily available even though the provider operates finite physical resources.^[ref]

For example, a service can add workers when its queue grows and remove them after the backlog clears. A control rule needs an observation signal, a scaling policy and time for new workers to become ready. Elastic capacity does not make every program parallel: a serial bottleneck, shared database or slow startup can prevent additional workers from increasing throughput. Releasing capacity also requires preserving needed state outside workers that may disappear.

Resource pooling combines provider computing resources to serve multiple consumers, with physical and virtual resources assigned and reassigned according to demand. NIST emphasizes multi-tenant service and some location independence: consumers generally select an abstraction or broad location requirement rather than one permanently assigned physical server.^[ref]

Pooling can improve utilization when consumers' peak demands occur at different times. It also requires isolation and allocation controls so that one consumer cannot read another's data or monopolize all capacity. For example, virtual machines may share a physical host while retaining separate address spaces. The resource pool and the mechanism enforcing its boundaries are distinct design responsibilities.

Timothy Grance coauthored the 2011 NIST definition of cloud computing with Peter Mell. It specifies five essential characteristics, service models and deployment models rather than equating every remotely accessed computer with a cloud.^[ref]

For example, rapid elasticity concerns provisioning and releasing capacity as demand changes; it is distinct from resource pooling, which concerns sharing provider resources among consumers. A manually provisioned remote server can lack the on-demand and elasticity characteristics. Separating these properties makes system descriptions more informative than simply attaching a cloud label.