Cloud computing - wikipedia Cloud computing For the winner of the 2017 Preakness Stakes, see Cloud Computing ( horse ). Cloud computing metaphor : the group of networked elements providing services need not be individually addressed or managed by users ; instead, the entire provider - managed suite of hardware and software can be thought of as an amorphous cloud. Cloud computing is an information technology ( IT ) paradigm that enables ubiquitous access to shared pools of configurable system resources and higher - level services that can be rapidly provisioned with minimal management effort, often over the Internet. Cloud computing relies on sharing of resources to achieve coherence and economies of scale, similar to a public utility. Third - party clouds enable organizations to focus on their core businesses instead of expending resources on computer infrastructure and maintenance. Advocates note that cloud computing allows companies to avoid or minimize up - front IT infrastructure costs. Proponents also claim that cloud computing allows enterprises to get their applications up and running faster, with improved manageability and less maintenance, and that it enables IT teams to more rapidly adjust resources to meet fluctuating and unpredictable demand. Cloud providers typically use a `` pay - as - you - go '' model, which can lead to unexpected operating expenses if administrators are not familiarized with cloud - pricing models. Since the launch of Amazon EC2 in 2006, the availability of high - capacity networks, low - cost computers and storage devices as well as the widespread adoption of hardware virtualization, service - oriented architecture, and autonomic and utility computing has led to growth in cloud computing. Contents 1 History 1.1 Early history 1.2 2000s 1.3 2010s 2 Similar concepts 3 Characteristics 4 Service models 4.1 Infrastructure as a service ( IaaS ) 4.2 Platform as a service ( PaaS ) 4.3 Software as a service ( SaaS ) 4.4 Mobile `` backend '' as a service ( MBaaS ) 4.5 Serverless computing 5 Deployment models 5.1 Private cloud 5.2 Public cloud 5.3 Hybrid cloud 5.4 Others 5.4. 1 Community cloud 5.4. 2 Distributed cloud 5.4. 3 Multicloud 5.4. 4 Big Data cloud 5.4. 5 HPC cloud 6 Architecture 6.1 Cloud engineering 7 Security and privacy 8 Limitations and disadvantages 9 Emerging trends 10 See also 11 References 12 Further reading 13 External links History While the term `` cloud computing '' was popularized with Amazon.com releasing its Elastic Compute Cloud product in 2006, references to the phrase `` cloud computing '' appeared as early as 1996, with the first known mention in a Compaq internal document. The cloud symbol was used to represent networks of computing equipment in the original ARPANET by as early as 1977, and the CSNET by 1981 -- both predecessors to the Internet itself. The word cloud was used as a metaphor for the Internet and a standardized cloud - like shape was used to denote a network on telephony schematics. With this simplification, the implication is that the specifics of how the end points of a network are connected are not relevant for the purposes of understanding the diagram. The term cloud was used to refer to platforms for distributed computing as early as 1993, when Apple spin - off General Magic and AT&T used it in describing their ( paired ) Telescript and PersonaLink technologies. In Wired 's April 1994 feature `` Bill and Andy 's Excellent Adventure II '', Andy Hertzfeld commented on Telescript, General Magic 's distributed programming language : `` The beauty of Telescript... is that now, instead of just having a device to program, we now have the entire Cloud out there, where a single program can go and travel to many different sources of information and create sort of a virtual service. No one had conceived that before. The example Jim White ( the designer of Telescript, X. 400 and ASN. 1 ) uses now is a date - arranging service where a software agent goes to the flower store and orders flowers and then goes to the ticket shop and gets the tickets for the show, and everything is communicated to both parties. '' Early history During the 1960s, the initial concepts of time - sharing became popularized via RJE ( Remote Job Entry ) ; this terminology was mostly associated with large vendors such as IBM and DEC. Full - time - sharing solutions were available by the early 1970s on such platforms as Multics ( on GE hardware ), Cambridge CTSS, and the earliest UNIX ports ( on DEC hardware ). Yet, the `` data center '' model where users submitted jobs to operators to run on IBM mainframes was overwhelmingly predominant. In the 1990s, telecommunications companies, who previously offered primarily dedicated point - to - point data circuits, began offering virtual private network ( VPN ) services with comparable quality of service, but at a lower cost. By switching traffic as they saw fit to balance server use, they could use overall network bandwidth more effectively. They began to use the cloud symbol to denote the demarcation point between what the provider was responsible for and what users were responsible for. Cloud computing extended this boundary to cover all servers as well as the network infrastructure. As computers became more diffused, scientists and technologists explored ways to make large - scale computing power available to more users through time - sharing. They experimented with algorithms to optimize the infrastructure, platform, and applications to prioritize CPUs and increase efficiency for end users. 2000s This section needs expansion. You can help by adding to it. ( November 2017 ) Since 2000, cloud computing has come into existence. In August 2006, Amazon introduced its Elastic Compute Cloud. In April 2008, Google released Google App Engine in beta. In early 2008, NASA 's OpenNebula, enhanced in the RESERVOIR European Commission - funded project, became the first open - source software for deploying private and hybrid clouds, and for the federation of clouds. By mid-2008, Gartner saw an opportunity for cloud computing `` to shape the relationship among consumers of IT services, those who use IT services and those who sell them '' and observed that `` organizations are switching from company - owned hardware and software assets to per - use service - based models '' so that the `` projected shift to computing... will result in dramatic growth in IT products in some areas and significant reductions in other areas. '' 2010s This section needs expansion. You can help by adding to it. ( November 2017 ) In February 2010, Microsoft released Microsoft Azure, which was announced in October 2008. In July 2010, Rackspace Hosting and NASA jointly launched an open - source cloud - software initiative known as OpenStack. The OpenStack project intended to help organizations offering cloud - computing services running on standard hardware. The early code came from NASA 's Nebula platform as well as from Rackspace 's Cloud Files platform. As an open source offering and along with other open - source solutions such as CloudStack, Ganeti and OpenNebula, it has attracted attention by several key communities. Several studies aim at comparing these open sources offerings based on a set of criteria. On March 1, 2011, IBM announced the IBM SmartCloud framework to support Smarter Planet. Among the various components of the Smarter Computing foundation, cloud computing is a critical part. On June 7, 2012, Oracle announced the Oracle Cloud. This cloud offering is poised to be the first to provide users with access to an integrated set of IT solutions, including the Applications ( SaaS ), Platform ( PaaS ), and Infrastructure ( IaaS ) layers. In May 2012, Google Compute Engine was released in preview, before being rolled out into General Availability in December 2013. Similar concepts The goal of cloud computing is to allow users to take benefit from all of these technologies, without the need for deep knowledge about or expertise with each one of them. The cloud aims to cut costs, and helps the users focus on their core business instead of being impeded by IT obstacles. The main enabling technology for cloud computing is virtualization. Virtualization software separates a physical computing device into one or more `` virtual '' devices, each of which can be easily used and managed to perform computing tasks. With operating system -- level virtualization essentially creating a scalable system of multiple independent computing devices, idle computing resources can be allocated and used more efficiently. Virtualization provides the agility required to speed up IT operations, and reduces cost by increasing infrastructure utilization. Autonomic computing automates the process through which the user can provision resources on - demand. By minimizing user involvement, automation speeds up the process, reduces labor costs and reduces the possibility of human errors. Users routinely face difficult business problems. Cloud computing adopts concepts from Service - oriented Architecture ( SOA ) that can help the user break these problems into services that can be integrated to provide a solution. Cloud computing provides all of its resources as services, and makes use of the well - established standards and best practices gained in the domain of SOA to allow global and easy access to cloud services in a standardized way. Cloud computing also leverages concepts from utility computing to provide metrics for the services used. Such metrics are at the core of the public cloud pay - per - use models. In addition, measured services are an essential part of the feedback loop in autonomic computing, allowing services to scale on - demand and to perform automatic failure recovery. Cloud computing is a kind of grid computing ; it has evolved by addressing the QoS ( quality of service ) and reliability problems. Cloud computing provides the tools and technologies to build data / compute intensive parallel applications with much more affordable prices compared to traditional parallel computing techniques. Cloud computing shares characteristics with : Client -- server model -- Client -- server computing refers broadly to any distributed application that distinguishes between service providers ( servers ) and service requestors ( clients ). Computer bureau -- A service bureau providing computer services, particularly from the 1960s to 1980s. Grid computing -- `` A form of distributed and parallel computing, whereby a ' super and virtual computer ' is composed of a cluster of networked, loosely coupled computers acting in concert to perform very large tasks. '' Fog computing -- Distributed computing paradigm that provides data, compute, storage and application services closer to client or near - user edge devices, such as network routers. Furthermore, fog computing handles data at the network level, on smart devices and on the end - user client side ( e.g. mobile devices ), instead of sending data to a remote location for processing. Mainframe computer -- Powerful computers used mainly by large organizations for critical applications, typically bulk data processing such as : census ; industry and consumer statistics ; police and secret intelligence services ; enterprise resource planning ; and financial transaction processing. Utility computing -- The `` packaging of computing resources, such as computation and storage, as a metered service similar to a traditional public utility, such as electricity. '' Peer - to - peer -- A distributed architecture without the need for central coordination. Participants are both suppliers and consumers of resources ( in contrast to the traditional client -- server model ). Green computing Cloud sandbox -- A live, isolated computer environment in which a program, code or file can run without affecting the application in which it runs. Characteristics Cloud computing exhibits the following key characteristics : Agility for organizations may be improved, as cloud computing may increase users ' flexibility with re-provisioning, adding, or expanding technological infrastructure resources. Cost reductions are claimed by cloud providers. A public - cloud delivery model converts capital expenditures ( e.g., buying servers ) to operational expenditure. This purportedly lowers barriers to entry, as infrastructure is typically provided by a third party and need not be purchased for one - time or infrequent intensive computing tasks. Pricing on a utility computing basis is `` fine - grained '', with usage - based billing options. As well, less in - house IT skills are required for implementation of projects that use cloud computing. The e-FISCAL project 's state - of - the - art repository contains several articles looking into cost aspects in more detail, most of them concluding that costs savings depend on the type of activities supported and the type of infrastructure available in - house. Device and location independence enable users to access systems using a web browser regardless of their location or what device they use ( e.g., PC, mobile phone ). As infrastructure is off - site ( typically provided by a third - party ) and accessed via the Internet, users can connect to it from anywhere. Maintenance of cloud computing applications is easier, because they do not need to be installed on each user 's computer and can be accessed from different places ( e.g., different work locations, while travelling, etc. ). Multitenancy enables sharing of resources and costs across a large pool of users thus allowing for : centralization of infrastructure in locations with lower costs ( such as real estate, electricity, etc. ) peak - load capacity increases ( users need not engineer and pay for the resources and equipment to meet their highest possible load - levels ) utilisation and efficiency improvements for systems that are often only 10 -- 20 % utilised. Performance is monitored by IT experts from the service provider, and consistent and loosely coupled architectures are constructed using web services as the system interface. Resource pooling is the provider 's computing resources are commingle to serve multiple consumers using a multi-tenant model with different physical and virtual resources dynamically assigned and reassigned according to user demand. There is a sense of location independence in that the consumer generally have no control or knowledge over the exact location of the provided resource. ( 1 ) Productivity may be increased when multiple users can work on the same data simultaneously, rather than waiting for it to be saved and emailed. Time may be saved as information does not need to be re-entered when fields are matched, nor do users need to install application software upgrades to their computer. Reliability improves with the use of multiple redundant sites, which makes well - designed cloud computing suitable for business continuity and disaster recovery. Scalability and elasticity via dynamic ( `` on - demand '' ) provisioning of resources on a fine - grained, self - service basis in near real - time ( Note, the VM startup time varies by VM type, location, OS and cloud providers ), without users having to engineer for peak loads. This gives the ability to scale up when the usage need increases or down if resources are not being used. Security can improve due to centralization of data, increased security - focused resources, etc., but concerns can persist about loss of control over certain sensitive data, and the lack of security for stored kernels. Security is often as good as or better than other traditional systems, in part because service providers are able to devote resources to solving security issues that many customers can not afford to tackle or which they lack the technical skills to address. However, the complexity of security is greatly increased when data is distributed over a wider area or over a greater number of devices, as well as in multi-tenant systems shared by unrelated users. In addition, user access to security audit logs may be difficult or impossible. Private cloud installations are in part motivated by users ' desire to retain control over the infrastructure and avoid losing control of information security. The National Institute of Standards and Technology 's definition of cloud computing identifies `` five essential characteristics '' : On - demand self - service. A consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with each service provider. Broad network access. Capabilities are available over the network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms ( e.g., mobile phones, tablets, laptops, and workstations ). Resource pooling. The provider 's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to consumer demand. Rapid elasticity. Capabilities can be elastically provisioned and released, in some cases automatically, to scale rapidly outward and inward commensurate with demand. To the consumer, the capabilities available for provisioning often appear unlimited and can be appropriated in any quantity at any time. Measured service. Cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service ( e.g., storage, processing, bandwidth, and active user accounts ). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service. -- National Institute of Standards and Technology Service models Cloud computing service models arranged as layers in a stack Though service - oriented architecture advocates `` everything as a service '' ( with the acronyms EaaS or XaaS, or simply aas ), cloud - computing providers offer their `` services '' according to different models, of which the three standard models per NIST are Infrastructure as a Service ( IaaS ), Platform as a Service ( PaaS ), and Software as a Service ( SaaS ). These models offer increasing abstraction ; they are thus often portrayed as a layers in a stack : infrastructure -, platform - and software - as - a-service, but these need not be related. For example, one can provide SaaS implemented on physical machines ( bare metal ), without using underlying PaaS or IaaS layers, and conversely one can run a program on IaaS and access it directly, without wrapping it as SaaS. Infrastructure as a service ( IaaS ) Main article : Infrastructure as a service `` Infrastructure as a service '' ( IaaS ) refers to online services that provide high - level APIs used to dereference various low - level details of underlying network infrastructure like physical computing resources, location, data partitioning, scaling, security, backup etc. A hypervisor, such as Xen, Oracle VirtualBox, Oracle VM, KVM, VMware ESX / ESXi, or Hyper - V, LXD, runs the virtual machines as guests. Pools of hypervisors within the cloud operational system can support large numbers of virtual machines and the ability to scale services up and down according to customers ' varying requirements. Linux containers run in isolated partitions of a single Linux kernel running directly on the physical hardware. Linux cgroups and namespaces are the underlying Linux kernel technologies used to isolate, secure and manage the containers. Containerisation offers higher performance than virtualization, because there is no hypervisor overhead. Also, container capacity auto - scales dynamically with computing load, which eliminates the problem of over-provisioning and enables usage - based billing. IaaS clouds often offer additional resources such as a virtual - machine disk - image library, raw block storage, file or object storage, firewalls, load balancers, IP addresses, virtual local area networks ( VLANs ), and software bundles. The NIST 's definition of cloud computing describes IaaS as `` where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, and deployed applications ; and possibly limited control of select networking components ( e.g., host firewalls ). '' IaaS - cloud providers supply these resources on - demand from their large pools of equipment installed in data centers. For wide - area connectivity, customers can use either the Internet or carrier clouds ( dedicated virtual private networks ). To deploy their applications, cloud users install operating - system images and their application software on the cloud infrastructure. In this model, the cloud user patches and maintains the operating systems and the application software. Cloud providers typically bill IaaS services on a utility computing basis : cost reflects the amount of resources allocated and consumed. Platform as a service ( PaaS ) Main article : Platform as a service The NIST 's definition of cloud computing defines Platform as a Service as : The capability provided to the consumer is to deploy onto the cloud infrastructure consumer - created or acquired applications created using programming languages, libraries, services, and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, or storage, but has control over the deployed applications and possibly configuration settings for the application - hosting environment. PaaS vendors offer a development environment to application developers. The provider typically develops toolkit and standards for development and channels for distribution and payment. In the PaaS models, cloud providers deliver a computing platform, typically including operating system, programming - language execution environment, database, and web server. Application developers can develop and run their software solutions on a cloud platform without the cost and complexity of buying and managing the underlying hardware and software layers. With some PaaS offers like Microsoft Azure, Oracle Cloud Platform and Google App Engine, the underlying computer and storage resources scale automatically to match application demand so that the cloud user does not have to allocate resources manually. The latter has also been proposed by an architecture aiming to facilitate real - time in cloud environments. Even more specific application types can be provided via PaaS, such as media encoding as provided by services like bitcodin.com or media.io. Some integration and data management providers have also embraced specialized applications of PaaS as delivery models for data solutions. Examples include iPaaS ( Integration Platform as a Service ) and dPaaS ( Data Platform as a Service ). iPaaS enables customers to develop, execute and govern integration flows. Under the iPaaS integration model, customers drive the development and deployment of integrations without installing or managing any hardware or middleware. dPaaS delivers integration -- and data - management -- products as a fully managed service. Under the dPaaS model, the PaaS provider, not the customer, manages the development and execution of data solutions by building tailored data applications for the customer. dPaaS users retain transparency and control over data through data - visualization tools. Platform as a Service ( PaaS ) consumers do not manage or control the underlying cloud infrastructure including network, servers, operating systems, or storage, but have control over the deployed applications and possibly configuration settings for the application - hosting environment. A recent specialized PaaS is the Blockchain as a Service ( BaaS ), that some vendors such as IBM Bluemix and Oracle Cloud Platform have already included in their PaaS offering. Software as a service ( SaaS ) Main article : Software as a service The NIST 's definition of cloud computing defines Software as a Service as : The capability provided to the consumer is to use the provider 's applications running on a cloud infrastructure. The applications are accessible from various client devices through either a thin client interface, such as a web browser ( e.g., web - based email ), or a program interface. The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user - specific application configuration settings. In the software as a service ( SaaS ) model, users gain access to application software and databases. Cloud providers manage the infrastructure and platforms that run the applications. SaaS is sometimes referred to as `` on - demand software '' and is usually priced on a pay - per - use basis or using a subscription fee. In the SaaS model, cloud providers install and operate application software in the cloud and cloud users access the software from cloud clients. Cloud users do not manage the cloud infrastructure and platform where the application runs. This eliminates the need to install and run the application on the cloud user 's own computers, which simplifies maintenance and support. Cloud applications differ from other applications in their scalability -- which can be achieved by cloning tasks onto multiple virtual machines at run - time to meet changing work demand. Load balancers distribute the work over the set of virtual machines. This process is transparent to the cloud user, who sees only a single access - point. To accommodate a large number of cloud users, cloud applications can be multitenant, meaning that any machine may serve more than one cloud - user organization. The pricing model for SaaS applications is typically a monthly or yearly flat fee per user, so prices become scalable and adjustable if users are added or removed at any point. Proponents claim that SaaS gives a business the potential to reduce IT operational costs by outsourcing hardware and software maintenance and support to the cloud provider. This enables the business to reallocate IT operations costs away from hardware / software spending and from personnel expenses, towards meeting other goals. In addition, with applications hosted centrally, updates can be released without the need for users to install new software. One drawback of SaaS comes with storing the users ' data on the cloud provider 's server. As a result, there could be unauthorized access to the data. Mobile `` backend '' as a service ( mBaaS ) Main article : Mobile backend as a service In the mobile `` backend '' as a service ( m ) model, also known as backend as a service ( BaaS ), web app and mobile app developers are provided with a way to link their applications to cloud storage and cloud computing services with application programming interfaces ( APIs ) exposed to their applications and custom software development kits ( SDKs ). Services include user management, push notifications, integration with social networking services and more. This is a relatively recent model in cloud computing, with most BaaS startups dating from 2011 or later but trends indicate that these services are gaining significant mainstream traction with enterprise consumers. Serverless computing Main article : Serverless computing Serverless computing is a cloud computing code execution model in which the cloud provider fully manages starting and stopping virtual machines as necessary to serve requests, and requests are billed by an abstract measure of the resources required to satisfy the request, rather than per virtual machine, per hour. Despite the name, it does not actually involve running code without servers. Serverless computing is so named because the business or person that owns the system does not have to purchase, rent or provision servers or virtual machines for the back - end code to run on. Deployment models Cloud computing types Private cloud Private cloud is cloud infrastructure operated solely for a single organization, whether managed internally or by a third - party, and hosted either internally or externally. Undertaking a private cloud project requires significant engagement to virtualize the business environment, and requires the organization to reevaluate decisions about existing resources. It can improve business, but every step in the project raises security issues that must be addressed to prevent serious vulnerabilities. Self - run data centers are generally capital intensive. They have a significant physical footprint, requiring allocations of space, hardware, and environmental controls. These assets have to be refreshed periodically, resulting in additional capital expenditures. They have attracted criticism because users `` still have to buy, build, and manage them '' and thus do not benefit from less hands - on management, essentially `` ( lacking ) the economic model that makes cloud computing such an intriguing concept ''. Public cloud A cloud is called a `` public cloud '' when the services are rendered over a network that is open for public use. Public cloud services may be free. Technically there may be little or no difference between public and private cloud architecture, however, security consideration may be substantially different for services ( applications, storage, and other resources ) that are made available by a service provider for a public audience and when communication is effected over a non-trusted network. Generally, public cloud service providers like Amazon Web Services ( AWS ), Oracle, Microsoft and Google own and operate the infrastructure at their data center and access is generally via the Internet. AWS, Oracle, Microsoft, and Google also offer direct connect services called `` AWS Direct Connect '', `` Oracle FastConnect '', `` Azure ExpressRoute '', and `` Cloud Interconnect '' respectively, such connections require customers to purchase or lease a private connection to a peering point offered by the cloud provider. Hybrid cloud Hybrid cloud is a composition of two or more clouds ( private, community or public ) that remain distinct entities but are bound together, offering the benefits of multiple deployment models. Hybrid cloud can also mean the ability to connect collocation, managed and / or dedicated services with cloud resources. Gartner defines a hybrid cloud service as a cloud computing service that is composed of some combination of private, public and community cloud services, from different service providers. A hybrid cloud service crosses isolation and provider boundaries so that it ca n't be simply put in one category of private, public, or community cloud service. It allows one to extend either the capacity or the capability of a cloud service, by aggregation, integration or customization with another cloud service. Varied use cases for hybrid cloud composition exist. For example, an organization may store sensitive client data in house on a private cloud application, but interconnect that application to a business intelligence application provided on a public cloud as a software service. This example of hybrid cloud extends the capabilities of the enterprise to deliver a specific business service through the addition of externally available public cloud services. Hybrid cloud adoption depends on a number of factors such as data security and compliance requirements, level of control needed over data, and the applications an organization uses. Another example of hybrid cloud is one where IT organizations use public cloud computing resources to meet temporary capacity needs that can not be met by the private cloud. This capability enables hybrid clouds to employ cloud bursting for scaling across clouds. Cloud bursting is an application deployment model in which an application runs in a private cloud or data center and `` bursts '' to a public cloud when the demand for computing capacity increases. A primary advantage of cloud bursting and a hybrid cloud model is that an organization pays for extra compute resources only when they are needed. Cloud bursting enables data centers to create an in - house IT infrastructure that supports average workloads, and use cloud resources from public or private clouds, during spikes in processing demands. The specialized model of hybrid cloud, which is built atop heterogeneous hardware, is called `` Cross-platform Hybrid Cloud ''. A cross-platform hybrid cloud is usually powered by different CPU architectures, for example, x86 - 64 and ARM, underneath. Users can transparently deploy and scale applications without knowledge of the cloud 's hardware diversity. This kind of cloud emerges from the raise of ARM - based system - on - chip for server - class computing. Others Community cloud Community cloud shares infrastructure between several organizations from a specific community with common concerns ( security, compliance, jurisdiction, etc. ), whether managed internally or by a third - party, and either hosted internally or externally. The costs are spread over fewer users than a public cloud ( but more than a private cloud ), so only some of the cost savings potential of cloud computing are realized. Distributed cloud A cloud computing platform can be assembled from a distributed set of machines in different locations, connected to a single network or hub service. It is possible to distinguish between two types of distributed clouds : public - resource computing and volunteer cloud. Public - resource computing -- This type of distributed cloud results from an expansive definition of cloud computing, because they are more akin to distributed computing than cloud computing. Nonetheless, it is considered a sub-class of cloud computing, and some examples include distributed computing platforms such as BOINC and Folding@Home. Volunteer cloud -- Volunteer cloud computing is characterized as the intersection of public - resource computing and cloud computing, where a cloud computing infrastructure is built using volunteered resources. Many challenges arise from this type of infrastructure, because of the volatility of the resources used to built it and the dynamic environment it operates in. It can also be called peer - to - peer clouds, or ad - hoc clouds. An interesting effort in such direction is Cloud@Home, it aims to implement a cloud computing infrastructure using volunteered resources providing a business - model to incentivize contributions through financial restitution. Multi-cloud Main article : Multicloud Multicloud is the use of multiple cloud computing services in a single heterogeneous architecture to reduce reliance on single vendors, increase flexibility through choice, mitigate against disasters, etc. It differs from hybrid cloud in that it refers to multiple cloud services, rather than multiple deployment modes ( public, private, legacy ). Big data cloud The issues of transferring large amounts of data to the cloud as well as data security once the data is in the cloud initially hampered adoption of cloud for big data, but now that much data originates in the cloud and with the advent of bare - metal servers, the cloud has become a solution for use cases including business analytics and geospatial analysis. Solutions range from Hadoop hosting in the cloud to end - to - end analytics. HPC cloud HPC cloud refers to the use of cloud computing services and infrastructure to execute high - performance computing ( HPC ) applications. These applications consume considerable amount of computing power and memory and are traditionally executed on clusters of computers. Various vendors offer servers that can support the execution of these applications. In HPC cloud, the deployment model allows all HPC resources to be inside the cloud provider infrastructure or different portions of HPC resources to be shared between cloud provider and client on - premise infrastructure. The adoption of cloud to run HPC applications started mostly for applications composed of independent tasks with no inter-process communication. As cloud providers began to offer high - speed network technologies such as InfiniBand, multiprocessing tightly coupled applications started to benefit from cloud as well. Architecture Cloud computing sample architecture Cloud architecture, the systems architecture of the software systems involved in the delivery of cloud computing, typically involves multiple cloud components communicating with each other over a loose coupling mechanism such as a messaging queue. Elastic provision implies intelligence in the use of tight or loose coupling as applied to mechanisms such as these and others. Cloud engineering Cloud engineering is the application of engineering disciplines to cloud computing. It brings a systematic approach to the high - level concerns of commercialization, standardization, and governance in conceiving, developing, operating and maintaining cloud computing systems. It is a multidisciplinary method encompassing contributions from diverse areas such as systems, software, web, performance, information, security, platform, risk, and quality engineering. Security and privacy Main article : Cloud computing issues Cloud computing poses privacy concerns because the service provider can access the data that is in the cloud at any time. It could accidentally or deliberately alter or even delete information. Many cloud providers can share information with third parties if necessary for purposes of law and order even without a warrant. That is permitted in their privacy policies, which users must agree to before they start using cloud services. Solutions to privacy include policy and legislation as well as end users ' choices for how data is stored. Users can encrypt data that is processed or stored within the cloud to prevent unauthorized access. According to the Cloud Security Alliance, the top three threats in the cloud are Insecure Interfaces and API 's, Data Loss & Leakage, and Hardware Failure -- which accounted for 29 %, 25 % and 10 % of all cloud security outages respectively. Together, these form shared technology vulnerabilities. In a cloud provider platform being shared by different users there may be a possibility that information belonging to different customers resides on same data server. Additionally, Eugene Schultz, chief technology officer at Emagined Security, said that hackers are spending substantial time and effort looking for ways to penetrate the cloud. `` There are some real Achilles ' heels in the cloud infrastructure that are making big holes for the bad guys to get into ''. Because data from hundreds or thousands of companies can be stored on large cloud servers, hackers can theoretically gain control of huge stores of information through a single attack -- a process he called `` hyperjacking ''. Some examples of this include the Dropbox security breach, and iCloud 2014 leak. Dropbox had been breached in October 2014, having over 7 million of its users passwords stolen by hackers in an effort to get monetary value from it by Bitcoins ( BTC ). By having these passwords, they are able to read private data as well as have this data be indexed by search engines ( making the information public ). There is the problem of legal ownership of the data ( If a user stores some data in the cloud, can the cloud provider profit from it ? ). Many Terms of Service agreements are silent on the question of ownership. Physical control of the computer equipment ( private cloud ) is more secure than having the equipment off site and under someone else 's control ( public cloud ). This delivers great incentive to public cloud computing service providers to prioritize building and maintaining strong management of secure services. Some small businesses that do n't have expertise in IT security could find that it 's more secure for them to use a public cloud. There is the risk that end users do not understand the issues involved when signing on to a cloud service ( persons sometimes do n't read the many pages of the terms of service agreement, and just click `` Accept '' without reading ). This is important now that cloud computing is becoming popular and required for some services to work, for example for an intelligent personal assistant ( Apple 's Siri or Google Now ). Fundamentally, private cloud is seen as more secure with higher levels of control for the owner, however public cloud is seen to be more flexible and requires less time and money investment from the user. Limitations and disadvantages According to Bruce Schneier, `` The downside is that you will have limited customization options. Cloud computing is cheaper because of economics of scale, and -- like any outsourced task -- you tend to get what you get. A restaurant with a limited menu is cheaper than a personal chef who can cook anything you want. Fewer options at a much cheaper price : it 's a feature, not a bug. '' He also suggests that `` the cloud provider might not meet your legal needs '' and that businesses need to weigh the benefits of cloud computing against the risks. In cloud computing, the control of the back end infrastructure is limited to the cloud vendor only. Cloud providers often decide on the management policies, which moderates what the cloud users are able to do with their deployment. Cloud users are also limited to the control and management of their applications, data and services. This includes data caps, which are placed on cloud users by the cloud vendor allocating certain amount of bandwidth for each customer and are often shared among other cloud users. Privacy and confidentiality are big concerns in some activities. For instance, sworn translators working under the stipulations of an NDA, might face problems regarding sensitive data that are not encrypted. Cloud computing is beneficial to many enterprises ; it lowers costs and allows them to focus on competence instead of on matters of IT and infrastructure. Nevertheless, cloud computing has proven to have some limitations and disadvantages, especially for smaller business operations, particularly regarding security and downtime. Technical outages are inevitable and occur sometimes when cloud service providers become overwhelmed in the process of serving their clients. This may result to temporary business suspension. Since this technology 's systems rely on the internet, an individual can not be able to access their applications, server or data from the cloud during an outage. Emerging trends Cloud computing is still a subject of research. A driving factor in the evolution of cloud computing has been chief technology officers seeking to minimize risk of internal outages and mitigate the complexity of housing network and computing hardware in - house. Major cloud technology companies invest billions of dollars per year in cloud Research and Development. For example, in 2011 Microsoft committed 90 percent of its $9.6 billion R&D budget to its cloud. Research by investment bank Centaur Partners in late 2015 forecasted that SaaS revenue would grow from $13.5 billion in 2011 to $32.8 billion in 2016. See also Computer networking portal Category : Cloud computing providers Category : Cloud platforms Cloud collaboration Cloud computing security Cloud computing comparison Cloud management Cloud research Cloud storage Edge computing eScience Microservices Mobile cloud computing Personal cloud Robot as a service Service - oriented architecture Time - sharing Ubiquitous computing Web computing References ^ Jump up to : `` What is Cloud Computing ? ''. Amazon Web Services. 2013 - 03 - 19. Retrieved 2013 - 03 - 20. Jump up ^ Baburajan, Rajani ( 2011 - 08 - 24 ). `` The Rising Cloud Storage Market Opportunity Strengthens Vendors ''. 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Retrieved 22 August 2010. ^ Jump up to : `` Cloud Computing Privacy Concerns on Our Doorstep ''. Jump up ^ M. Haghighat, S. Zonouz, & M. Abdel - Mottaleb ( 2015 ). CloudID : Trustworthy Cloud - based and Cross-Enterprise Biometric Identification. Expert Systems with Applications, 42 ( 21 ), 7905 -- 7916. ^ Jump up to : `` Google Drive, Dropbox, Box and iCloud Reach the Top 5 Cloud Storage Security Breaches List ''. psg.hitachi-solutions.com. Retrieved 2015 - 11 - 22. Jump up ^ Maltais, Michelle ( 26 April 2012 ). `` Who owns your stuff in the cloud ? ''. Los Angeles Times. Retrieved 2012 - 12 - 14. Jump up ^ `` Security of virtualization, cloud computing divides IT and security pros ''. Network World. 2010 - 02 - 22. Retrieved 2010 - 08 - 22. Jump up ^ `` The Bumpy Road to Private Clouds ''. Retrieved 8 October 2014. Jump up ^ `` Should Companies Do Most of Their Computing in the Cloud ? ( Part 1 ) - Schneier on Security ''. www.schneier.com. Retrieved 2016 - 02 - 28. Jump up ^ `` Disadvantages of Cloud Computing ( Part 1 ) - Limited control and flexibility ''. www.cloudacademy.com. Retrieved 2016 - 11 - 03. ^ Jump up to : `` The real limits of cloud computing ''. www.itworld.com. Retrieved 2016 - 11 - 03. Jump up ^ Karra, Maria. `` Cloud solutions for translation, yes or no ? ''. IAPTI.org. Retrieved 23 February 2017. Jump up ^ Smith, David Mitchell. `` Hype Cycle for Cloud Computing, 2013 ''. Gartner. Retrieved 3 July 2014. Jump up ^ `` The evolution of Cloud Computing ''. Retrieved 22 April 2015. Jump up ^ `` Microsoft Says to Spend 90 % of R&D on Cloud Strategy ''. Retrieved 22 April 2015. Jump up ^ `` Roundup of Cloud Computing Forecasts And Market Estimates, 2014 ''. Forbes. Retrieved 2015 - 11 - 22. Further reading Millard, Christopher ( 2013 ). Cloud Computing Law. Oxford University Press. ISBN 978 - 0 - 19 - 967168 - 7. Singh, Jatinder ; Powles, Julia ; Pasquier, Thomas ; Bacon, Jean ( July 2015 ). `` Data Flow Management and Compliance in Cloud Computing ''. IEEE Cloud Computing. 2 ( 4 ) : 24 -- 32. doi : 10.1109 / MCC. 2015.69. Armbrust, Michael ; Stoica, Ion ; Zaharia, Matei ; Fox, Armando ; Griffith, Rean ; Joseph, Anthony D. ; Katz, Randy ; Konwinski, Andy ; Lee, Gunho ; Patterson, David ; Rabkin, Ariel ( 1 April 2010 ). `` A view of cloud computing ''. Communications of the ACM. 53 ( 4 ) : 50. doi : 10.1145 / 1721654.1721672. Hu, Tung - Hui ( 2015 ). A Prehistory of the Cloud. MIT Press. ISBN 978 - 0 - 262 - 02951 - 3. Mell, P. ( 2011, September 31 ). The NIST Definition of Cloud Computing. Retrieved November 1, 2015, from National Institute of Standards and Technology website : http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf External links Wikimedia Commons has media related to Cloud computing. Cloud computing Applications Box Google G Suite Docs, Sheets and Slides HP Cloud ( closed ) Microsoft Online Rackspace Salesforce Zoho Platforms Alpha7 Amazon AppScale Box Bluemix Cloud Foundry Cocaine ( PaaS ) Engine Yard Helion GE Predix Google App Engine GreenQloud Heroku Inktank Jelastic Mendix Microsoft Azure MindSphere OutSystems openQRM OpenShift PythonAnywhere RightScale Force.com SAP Cloud Platform VMware vCloud Air WaveMaker Infrastructure Amazon Abiquo Enterprise Edition CloudStack CtrlS DigitalOcean EMC Atmos Eucalyptus Fujitsu GoGrid Google Cloud Platform GreenButton GreenQloud IBM cloud computing iland Joyent Lunacloud Mirantis Nimbula Nimbus OpenNebula OpenStack OrionVM Rackspace Cloud SoftLayer Zadara Storage libvirt libguestfs OVirt Virtual Machine Manager Wakame - vdc Virtual Private Cloud OnDemand Technologies Cloud database Data centers Distributed file system for cloud Hardware virtualization Internet Networking Security Structured storage Virtual appliance Web APIs Virtual private cloud Commons Parallel computing General Distributed computing Parallel computing Massively parallel Cloud computing High - performance computing Multiprocessing Manycore processor GPGPU Computer network Systolic array Levels Bit Instruction Thread Task Data Memory Loop Pipeline Multithreading Temporal Simultaneous ( SMT ) Speculative ( SpMT ) Preemptive Cooperative Clustered Multi-Thread ( CMT ) Hardware scout Theory PRAM model Analysis of parallel algorithms Amdahl 's law Gustafson 's law Cost efficiency Karp -- Flatt metric Slowdown Speedup Elements Process Thread Fiber Instruction window Coordination Multiprocessing Memory coherency Cache coherency Cache invalidation Barrier Synchronization Application checkpointing Programming Stream processing Dataflow programming Models Implicit parallelism Explicit parallelism Concurrency Non-blocking algorithm Hardware Flynn 's taxonomy SISD SIMD SIMT MISD MIMD Dataflow architecture Pipelined processor Superscalar processor Vector processor Multiprocessor symmetric asymmetric Memory shared distributed distributed shared UMA NUMA COMA Massively parallel computer Computer cluster Grid computer APIs Ateji PX Boost. Thread Chapel Charm++ Cilk Coarray Fortran CUDA Dryad C++ AMP Global Arrays MPI OpenMP OpenCL OpenHMPP OpenACC TPL PLINQ PVM POSIX Threads RaftLib UPC TBB ZPL Problems Deadlock Livelock Deterministic algorithm Embarrassingly parallel Parallel slowdown Race condition Software lockout Scalability Starvation Category : parallel computing Media related to Parallel computing at Wikimedia Commons GND : 7623494 - 0 NDL : 01155705 Retrieved from `` https://en.wikipedia.org/w/index.php?title=Cloud_computing&oldid=827977656 '' Categories : Cloud computing Cloud infrastructure Hidden categories : CS1 maint : Multiple names : authors list CS1 maint : Uses authors parameter All articles with dead external links Articles with dead external links from July 2017 Articles with permanently dead external links Wikipedia indefinitely move - protected pages Articles prone to spam from January 2017 All articles with unsourced statements Articles with unsourced statements from November 2017 Articles with unsourced statements from July 2015 Articles to be expanded from November 2017 All articles to be expanded Articles using small message boxes All articles lacking reliable references Articles lacking reliable references from February 2016 Wikipedia articles needing factual verification from July 2015 Wikipedia articles with GND identifiers Talk Contents About Wikipedia Afrikaans Azərbaycanca বাংলা Basa Banyumasan Беларуская Български Català Čeština Dansk Deutsch Eesti Español Esperanto Euskara فارسی Français Frysk 客家 語 / Hak - kâ - ngî 한국어 हिन्दी Bahasa Indonesia Íslenska Italiano עברית ಕನ್ನಡ ქართული Қазақша Latina Latviešu Lietuvių Magyar Македонски മലയാളം मराठी Bahasa Melayu Монгол Nederlands 日本 語 Norsk ଓଡ଼ିଆ Oʻzbekcha / ўзбекча Polski Português Română Русский Simple English Slovenčina Slovenščina Српски / srpski Suomi Svenska தமிழ் తెలుగు ไทย Тоҷикӣ Türkçe Українська اردو Tiếng Việt Winaray 粵語 中文 54 more Edit links This page was last edited on 27 February 2018, at 21 : 11. 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Question: when was the first idea of cloud presented
Answer: as early as 1996