A Behavioral Change Support System (BCSS) is any information and communications technology (ICT) tool, web platform, or gamified environment which targets behavioral changes in its end-users. BCSS are built upon persuasive systems design techniques.
Underlying theories and models
The design of these systems and their contents are based on behavioral change theories and models for behavioral change over time.
The theory of planned behavior describes the relationship between
attitudes, intentions, and the desired behavior. It is considered to be
one of the most influential determinant models.
A supporting model is the Fogg Behaviour Model (FBM),
which states that a user must be motivated first before having the
ability to perform the change in their behavior, which is triggered by
either intrinsic or extrinsic factors (The term "trigger" was changed by
the author in late 2017 and the term "prompt" is now being used). BCSS makes use of extrinsic (perceptual) prompts like alarms, messages with offers or calls to action, ads, requests, and more.
Other theories that aid in the design and mechanisms behind a BCSS include the social learning theory (SLT), which studies the interactions between a user and the environment, and the theory of planned behavior (initiated as the theory of reasoned action).
Techniques and elements
Applications
of BCSS may include game and training elements in several market
domains which can range from Health and Education and Quality of Life
(QoL), to professional development and workability. Virtually any
concept designed to cause a shift in a person's behavior can be
considered a BCSS, even if this change is not directly observed by the
users. When users are aware of this intention and choose to work within
the system, the chances of favorable results from this system increase.
This effect is attributed to metacognition, as most BCSS systems implement metacognitive strategies for goal attainment. These strategies help users understand the cause of their resistance to
adopting the desired behavior. It requires that they monitor themselves
whenever the targeted behavior can be observed to understand their
progress towards the desired behavior, and record evidence (usually
objective but also subjective measurements) of their behavioral changes.
There can be a positive impact on people who have difficulties in
changing their behavior by considering behaviors and the distance to
the desired behavior. This can be achieved by helping them develop a
personalized plan for reaching the targeted behavior and learning the
ways to achieve their personal goals. In most cases, the general
objective can be split into more than one objective or step, before the
desired behavior is adopted by the users and becomes a routine. The
positive feedback introduces self-management in BCSS applications since
it is particularly helpful for people to take responsibility for their
own actions and do things to the best of their ability. BCSS is very
often equipped with additional features like game elements to foster
user engagement leading to serious game applications. Moreover, they implement machine learning
techniques to predict the future behavior of users based on their past
performance. The evidence of the achieved change in behavior, as well as
important notifications during self-evaluation, are communicated with visual analytics
tools such as performance graphs. Additional tools frequently found in
BCSS include checklists and questionnaires to collect users' feedback,
hardware sensing components like the Internet of things
(IoT) devices (e.g., cameras), and social collaboration to help the
members of a user community to support each other. Occasionally, some
BCSS allow professionals (trainers, educators, medical personnel and
social professionals) to participate in the BCSS activities. This can be
done by giving advice and support and also by making decisions and
alterations to the treatment plan according to the observed performance
and the personal needs of the targeted users.
Taxonomies
Most
BCSSes work on a single profile (targeted user), while some can monitor
and report progress made by a group of people. There are BCSS
applications purely made using software, while others include hardware
components like sensors and IoT devices to introduce physical computing
in a hybrid physical-digital approach.
The devices used to access a BCSS are usually internet-connected mobile
devices like smartphones, tablets, or smartwatches. The success in this
category of BCSS applications lies in monitoring and notifying the
users constantly in regards to daily activities. On the other hand,
there are BCSSes which are less intrusive and rely on less frequent
access to the system. Another way to distinguish BCSSes is by the
knowledge domain they refer to. Theoretically, a BCSS can be built in
any knowledge domain.
Knowledge domains
eHealth/mHealth
Examples of BCSS applied in eHealth domains include CAREGIVERSPRO-MMD,
which is a community-based intervention to support people living with
dementia and their caregivers using game elements to engage users in
non-pharmacological interventions; iLift, which trains nurses in lifting and transfer techniques to prevent lower-back injuries, and We4Fit
which is more like a game environment. A more extensive review of
health BCSS can be found on the work of Alahäivälä & Oinas-Kukkonen
(2016) and Bridle et al. (2005).
Education
As Arlinghaus and Johnston implied, “Although not sufficient, education is a necessary component for behaviour change” (2018).
BCSSes are used in education less for imparting knowledge and testing
knowledge gained, and more for teaching a difficult subject like
"responsible sexual behaviour" in middle-school students, or for changing attitudes and beliefs about a topic of interest.
Adopting new behavioral patterns is difficult and people are not
motivated to change their behavior if they do not recognize the blocking
issue. Gamification is used to help recognition by providing rewards,
competition, and motivational cues of a BCSS. Prochaska et al. (2007)
proposed a six-stage behavioral change model (pre-contemplation,
contemplation, preparation, action, maintenance, and termination) which
can be applied in educational uses of a BCSS, as it appears in an ideal
environment for making the first step (contemplation) after a long
period of resistance (pre-contemplation). BCSSes affect the physical
world and help people experiment with an alternative behavioral pattern
without thinking of possible coincidences (such as social exposure). The
virtual activities performed in a BCSS help in the next step
(preparation) where the user makes a transition from a passive to an
active state in a safe environment. The user-monitoring and reward
system of a BCSS helps users complete the rest of the stages of the
behavior change (action, maintenance, and termination) and avoid
regression to the previous unwanted behavior. Schmied (2017) proposes a
similar seven-step process: the Designing for Behaviour Change (DBC)
framework.
Overall, a positive behavioral change in education settings is
facilitated by technology through digital intervention strategies, where
a teacher or educator makes adjustments to personalize the
interventions to the student's profiles and performance. Although ICT tools may not be necessary to change behavior in schools,
when used in the form of serious game-assisted learning, they can
provide a more in-depth perception of important concepts in a field of
study despite some disadvantages.
Other Domains
BCSS has been applied in other knowledge and study areas, including workers' behaviour, consumers' brand-loyalty, and CO2 footprints and energy consumption. Examples include applications designed to raise water-saving awareness, apps used by drivers to reduce fuel consumption by adopting an eco-friendly driving style, and educational games for simulating energy consumption in domestic environments like in Casals et al. (2017).
A systematic review of the application of game elements to behavioural
change in domestic energy consumption can be found in Johnson et al.
(2017) An example from the Industry 4.0 domain is SATISFACTORY,
which proposes a gamified social collaboration platform that is
integrated into the shop-floor of industries to improve productivity,
safety and workers' engagement. In the marketing context, behavioural
change techniques do not aim to change the way people think, but how
they consume products and services.
In politics, behavioural change interventions are delivered in the form
of mass-media campaigns on existing social media platforms rather than
standalone applications.
Overall, there is a continually growing number of domains in
which ICT tools are introduced as tools to implement and deliver
behavioral change campaigns in a systematic way. Some researchers refer
to persuasive technology
to identify the computer-mediated communication between humans or
human-computer interaction technologies used to deliver persuasive
evidence. A BCSS should be treated as a more complex ICT-based construct
which may use persuasive technologies, but also supports the full
life-cycle of behavioral change interventions (from authoring to
publishing), implements various campaigns to achieve its goals, and is
adaptive to specific user profiles.
Criticism
Behavior
Change Support Systems have been criticized for a lack of grounding in
independent behavioral theory, as well as the lack of industry standards
to measure performance or effect. Another source of criticism refers to the dominant behavioral change models as products of the theory of planned behavior. According to some researchers (Kollmus & Agyeman, 2002),
there is a gap between attitude and intention, and target behavior.
Thus, it is difficult to find a widely accepted model that can take all
relevant behavioral parameters into account. Additionally, even if
BCSSes help to effect a change in a targeted user's behavior, the user
usually fails to maintain the target behavior. This could be the result
of underestimating the long-term influence that environmental factors
have on behavior.
There is currently an open discussion on how intrusive a BCSS
should be, but this appears to be dependent upon the physical and social
context of the environment in which the BCSS is being used. As BCSS
makes use of personal data coming from users' profiles and the
user-monitoring system, the use of BCSSes in everyday life may be
legally restricted.
Traditionally, the World Wide Web has been accessed via fixed-line
services on laptops and desktop computers. However, the web is now more
accessible by portable and wireless devices. Early 2010 ITU
(International Telecommunication Union) report said that with current
growth rates, web access by people on the go – via laptops and smart
mobile devices – was likely to exceed web access from desktop computers
within the following five years. In January 2014, mobile internet use exceeded desktop use in the United States. The shift to mobile Web access has accelerated since 2007 with the rise of larger multitouch smartphones, and since 2010 with the rise of multitouch tablet computers. Both platforms provide better Internet access, screens, and mobile browsers, or application-based user Web experiences than previous generations of mobile devices. Web designers may work separately on such pages, or pages may be automatically converted, as in Mobile Wikipedia.
Faster speeds, smaller, feature-rich devices, and a multitude of
applications continue to drive explosive growth for mobile internet
traffic. The 2017 Virtual Network Index (VNI) report produced by Cisco
Systems forecasts that by 2021, there will be 5.5 billion global mobile
users (up from 4.9 billion in 2016).
Additionally, the same 2017 VNI report forecasts that average access
speeds will increase by roughly three times from 6.8 Mbit/s to 20 Mbit/s
in that same period with video comprising the bulk of the traffic
(78%).
According to BuzzCity, the mobile internet increased by 30% from Q1 to Q2 2011. In July 2012, approximately 10.5% of all web traffic occurred through mobile devices (up from 4% in December 2010).
The mobile web was first popularized by the Silicon Valley company, Unwired Planet. In 1997, Unwired Planet, Nokia, Ericsson, and Motorola started the WAP Forum
to create and harmonize the standards to ease the transition to
bandwidth networks and small display devices. The WAP standard was built
on a three-layer, middleware architecture that fueled the early growth
of the mobile web. It was made virtually irrelevant after the
development and adoption of faster networks, larger displays, and
advanced smartphones based on Apple's iOS and Google's Android software.
Mobile Internet refers to Internet access and mainly usage of Internet
using a cellular telephone service provider or mobile wireless network.
This wireless access can easily change to use a different wireless
Internet (radio) tower as a mobile device user moves across the service
area. Cellular base stations that connect through the telephone system
are more expensive to provide compared to a wireless base station that
connects directly to the network of an internet service provider. A
mobile broadband modem may "tethers" the smartphone to one or more devices to provide access to the Internet via the protocols that cellular telephone service providers offer.
Mobile standards
The Mobile Web Initiative (MWI) was set up by the W3C
to develop the best practices and technologies relevant to the mobile
web. The goal of the initiative is to make browsing the web from mobile
devices more reliable and accessible. The main aim is to evolve
standards of data formats from Internet providers that are tailored to
the specifications of particular mobile devices. The W3C has published
guidelines for mobile content,
and aimed to address the problem of device diversity by establishing a
technology to support a repository of device descriptions.
W3C developed a validating scheme to assess the readiness of content for the mobile web, through its mobileOK Scheme, which aims to help content developers to determine if their content is web-ready. The W3C guidelines and mobileOK approach have faced criticism. mTLD, the registry for .mobi, released a free testing tool called the MobiReady Report (see mobiForge) to analyze the mobile readiness of website.
Development
Access to the mobile web was first commercially offered in 1996, in Finland, on the Nokia 9000 Communicator phone via the Sonera and Radiolinja networks. The first commercial launch of a mobile-specific browser-based web service was in 1999 in Japan when i-mode was launched by NTT DoCoMo.
Evolution of mobile web standards
The mobile web primarily utilizes lightweight pages like this one written in Extensible Hypertext Markup Language (XHTML) or Wireless Markup Language (WML) to deliver content to mobile devices. Many new mobile browsers
are moving beyond these limits by supporting a wider range of Web
formats, including variants of HTML commonly found on the desktop web.
Growth
Total data consumed by Opera Mini users worldwide from 2006 to mid-2008 in TB
At one time, half the world had mobile phones.The articles in 2007-2008 were slightly misleading because the real
story at the time was that the number of mobile phone subscriptions had
reached half the population of the world. In reality, many people have
more than one subscription. For example, in Hong Kong, Italy and Ukraine,
the mobile phone penetration rate had passed 140% by 2009 . In 2009,
the number of unique users of mobile phones had reached half the
population of the planet when the ITU reported that the
subscriber number was to reach 4.6 billion users which means 3.8
billion activated mobile phones in use, and 3.4 billion unique users of
mobile phones.
Mobile Internet data connections are following the growth of mobile
phone connections, albeit at a lower rate. In 2009 Yankee Group
reported that 29% of all mobile phone users globally were accessing
browser-based internet content on their phones. According to the BBC, in
2020 there were over 5 billion mobile phone users in the world. According to Statista there were 1.57 billion smartphone owners in 2014 and 2.32 billion in 2017.
Many users in Europe and the United States are already users of the fixed internet when they first try the same experience on a mobile phone. Meanwhile, in other parts of the world, such as India, their first usage of the internet is on a mobile phone. Growth is fastest in parts of the world where the personal computer (PC) is not the first user experience of the internet. India, South Africa, Indonesia, and Saudi Arabia are seeing the fastest growth in mobile internet usage.
To a great extent, this is due to the rapid adoption of mobile phones
themselves. For example, Morgan Stanley reports that the highest mobile
phone adoption growth in 2006 was in Pakistan and India. Mobile internet has also been adopted in West Africa, and China had 155 million mobile internet users as of June 2009.
Top-level domain
The .mobisponsored top-level domain
was launched specifically for the mobile Internet by a consortium of
companies including Google, Microsoft, Nokia, Samsung, and Vodafone. By
forcing sites to comply with mobile web standards, .mobi tries to
ensure visitors a consistent and optimized experience on their mobile
device. However, this domain has been criticized by several big names,
including Tim Berners-Lee of the W3C, who said that providing different content to different devices "breaks the Web in a fundamental way".
In the fall of 2015, Google announced it would be rolling out an open source initiative called "Accelerated Mobile Pages" or AMP. The goal of this project is to improve the speed and performance of content-rich pages which include video, animations, and graphics. Since the majority of the population now consumes the web through tablets and smartphones, having web pages that are optimized for these products is the primary need to AMP.
The three main types of AMP are AMP HTML, AMP JS, and Google AMP Cache.
As of February 2018, Google requires the canonical page content to match the content on accelerated mobile pages.
Mobile web access may suffer from interoperability and usability problems. Interoperability issues stem from the platform fragmentation of mobile devices, mobile operating systems, and browsers. Usability problems are centered on the small physical size of the mobile phone form factors, which limit display resolution and user input). Limitations vary, depending on the device, and newer smartphones overcome some of these restrictions, but problems which may be encountered include:
Small screen size – This makes it difficult or impossible
to see text and graphics dependent on the standard size of a desktop
computer screen. To display more information, smartphone screen sizes
have been getting bigger.
Lack of windows – On a desktop computer, the ability to open
more than one window at a time allows for multi-tasking and easy revert
to a previous page. Historically on mobile web, only one page could be
displayed at a time, and pages could only be viewed in the sequence they
were originally accessed. Opera Mini was among the first allowing multiple windows,and browser tabs have become commonplace but few mobile browsers allow overlapping windows on the screen.
Navigation – Navigation is a problem for websites not
optimized for mobile devices as the content area is large, the screen
size is small, and there is no scroll wheel or hover box feature.
Lack of JavaScript and cookies – Most devices do not support client-side scripting and storage of cookies (smartphones
excluded), which are now widely used in most web sites to enhance the
user experience, facilitating the validation of data entered by the page
visitor, etc. This also results in web analytics tools being unable to
uniquely identify visitors using mobile devices.
Types of pages accessible – Many sites that can be accessed
on a desktop cannot on a mobile device. Many devices cannot access pages
with a secured connection, Flash, or other similar software, PDFs, or video sites, although as of 2011, this has been changing.
Speed – On most mobile devices, the speed of service is slow, sometimes slower than dial-up Internet access.
Broken pages – On many devices, a single page as viewed on a
desktop is broken into segments, each treated as a separate page. This
further slows navigation.
Compressed pages – Many pages, in their conversion to mobile
format, are squeezed into an order different from how they would
customarily be viewed on a desktop computer.
Size of messages – Many devices have limits on the number of characters that can be sent in an email message.
Cost – The access and bandwidth charges levied by cellphone networks can be high if there is no flat fee per month.
Location of mobile user – If the user is abroad the flat fee per month usually does not apply
Access to device capabilities – The inability of mobile web
applications to access the local capabilities on the mobile device can
limit their ability to provide the same features as native applications.
Cloud
computing metaphor: the group of networked elements providing services
does not need to be addressed or managed individually by users; instead,
the entire provider-managed suite of hardware and software can be
thought of as an amorphous cloud.
Cloud computing is the on-demand availability of computersystem resources, especially data storage (cloud storage) and computing power, without direct active management by the user. Large clouds often have functions distributed over multiple locations, each of which is a data center.
Cloud computing relies on sharing of resources to achieve coherence and
typically uses a pay-as-you-go model, which can help in reducing capital expenses but may also lead to unexpected operating expenses for users.
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.
Cloud computing has a rich history that extends back to the 1960s,
with the initial concepts of time-sharing becoming popularized via remote job entry
(RJE). The "data center" model, where users submitted jobs to operators
to run on mainframes, was predominantly used during this era. This was a
time of exploration and experimentation with ways to make large-scale
computing power available to more users through time-sharing, optimizing the infrastructure, platform, and applications, and increasing efficiency for end users. The use of the "cloud" metaphor to denote virtualized services traces back to 1994, when it was used by General Magic to describe the universe of "places" that mobile agents in the Telescript
environment could go. This metaphor is credited to David Hoffman, a
General Magic communications employee, based on its long-standing use in
networking and telecom. The expression cloud computing became more widely known in 1996 when the Compaq Computer Corporation drew up a business plan for future computing and the Internet. The company's ambition was to supercharge sales
with "cloud computing-enabled applications". The business plan foresaw
that online consumer file storage would most likely be commercially
successful. As a result, Compaq decided to sell server hardware to internet service providers.
In the 2000s, the application of cloud computing began to take shape with the establishment of Amazon Web Services (AWS) in 2002, which allowed developers to build applications independently. In 2006 the beta version of Google Docs was released, Amazon Simple Storage Service, known as Amazon S3, and the Amazon Elastic Compute Cloud (EC2), in 2008 NASA's development of the first open-source software for deploying private and hybrid clouds.
The following decade saw the launch of various cloud services. In 2010, Microsoft launched Microsoft Azure, and Rackspace Hosting and NASA initiated an open-source cloud-software project, OpenStack. IBM introduced the IBM SmartCloud framework in 2011, and Oracle announced the Oracle Cloud in 2012. In December 2019, Amazon launched AWS Outposts, a service that extends AWS infrastructure, services, APIs, and tools to customer data centers, co-location spaces, or on-premises facilities.
Since the global pandemic
of 2020, cloud technology has surged in popularity due to the level of
data security it offers and the flexibility of working options it
provides for all employees, notably remote workers.
Value proposition
Advocates of public and hybrid clouds claim 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, providing burst computing capability: high computing power at certain periods of peak demand.
Additional value propositions of cloud computing include:
Topic
Description
Cost reductions
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 independence
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
Maintenance of cloud environment is easier because the data is
hosted on an outside server maintained by a provider without the need to
invest in data center hardware. IT maintenance of cloud computing is
managed and updated by the cloud provider's IT maintenance team which
reduces cloud computing costs compared with on-premises data centers.
Multitenancy
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)
utilization and efficiency improvements for systems that are often only 10–20% utilized.
Performance
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.
Productivity
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.
Availability
Availability improves with the use of multiple redundant sites, which makes well-designed cloud computing suitable for business continuity and disaster recovery.
Scalability and Elasticity
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.
The time-efficient benefit of cloud scalability also means faster time
to market, more business flexibility, and adaptability, as adding new
resources does not take as much time as it used to.
Emerging approaches for managing elasticity include the use of machine
learning techniques to propose efficient elasticity models.
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 cannot 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.
Challenges and limitations
One of the main challenges of cloud computing, in comparison to more
traditional on-premises computing, is data security and privacy. Cloud
users entrust their sensitive data to third-party providers, who may not
have adequate measures to protect it from unauthorized access,
breaches, or leaks. Cloud users also face compliance risks if they have
to adhere to certain regulations or standards regarding data protection,
such as GDPR or HIPAA.
Another challenge of cloud computing is reduced visibility and
control. Cloud users may not have full insight into how their cloud
resources are managed, configured, or optimized by their providers. They
may also have limited ability to customize or modify their cloud
services according to their specific needs or preferences.
Complete understanding of all technology may be impossible, especially
given the scale, complexity, and deliberate opacity of contemporary
systems; however, there is a need for understanding complex technologies
and their interconnections to have power and agency within them. The metaphor of the cloud can be seen as problematic as cloud computing retains the aura of something noumenal and numinous; it is something experienced without precisely understanding what it is or how it works.
In addition, cloud migration is a significant issue. Cloud
migration is the process of moving data, applications, or workloads from
one cloud environment to another or from on-premises to the cloud.
Cloud migration can be complex, time-consuming, and costly, especially
if there are incompatibility issues between different cloud platforms or
architectures. Cloud migration can also cause downtime, performance
degradation, or data loss if not planned and executed properly.
Service models
Cloud computing service models arranged as layers in a stack
The service-oriented architecture (SOA) promotes the idea of "Everything as a Service" (EaaS or XaaS, or simply aAsS).
This concept is operationalized in cloud computing through several
service models as defined by the National Institute of Standards and
Technology (NIST). The three standard service models are Infrastructure
as a Service (IaaS), Platform as a Service (PaaS), and Software as a
Service (SaaS).
They are commonly depicted as layers in a stack, providing different
levels of abstraction. However, these layers are not necessarily
interdependent. For instance, SaaS can be delivered on bare metal,
bypassing PaaS and IaaS, and a program can run directly on IaaS without
being packaged as SaaS.
"Infrastructure as a service" (IaaS) refers to online services that provide high-level APIs used to abstract
various low-level details of underlying network infrastructure like
physical computing resources, location, data partitioning, scaling,
security, backup, etc. A hypervisor
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. The use of containers offers
higher performance than virtualization because there is no hypervisor
overhead. 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 number of resources allocated and consumed.
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 an operating system, programming-language execution
environment, database, and the web server. Application developers
develop and run their software on a cloud platform instead of directly
buying and managing the underlying hardware and software layers. With
some PaaS, the underlying computer and storage resources scale
automatically to match application demand so that the cloud user does
not have to allocate resources manually.
Some integration and data management providers also use
specialized applications of PaaS as delivery models for data. 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 programs by building data applications for
the customer. dPaaS users access data through data-visualization tools.
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. It may also be free. 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. Examples of applications offered as SaaS are games and productivity software like Google Docs and Office Online. SaaS applications may be integrated with cloud storage or File hosting services, which is the case with Google Docs being integrated with Google Drive, and Office Online being integrated with OneDrive.
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 or Function-as-a-Service (FaaS)
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. 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, serverless computing does not actually involve running code without servers. The business or person using the system does not have to purchase, rent or provide servers or virtual machines for the back-end code to run on.
Function as a service (FaaS) is a service-hosted remote procedure
call that utilizes serverless computing to enable deploying individual
functions in the cloud to run in response to events. Some consider FaaS to fall under the umbrella of serverless computing, while others use the terms interchangeably.
Deployment models
Cloud computing types
Private
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".
Cloud services are considered "public" when they are delivered over
the public Internet, and they may be offered as a paid subscription, or
free of charge.
Architecturally, there are few differences between public- and
private-cloud services, but security concerns increase substantially
when services (applications, storage, and other resources) are shared by
multiple customers. Most public-cloud providers offer direct-connection
services that allow customers to securely link their legacy data
centers to their cloud-resident applications.
Several factors like the functionality of the solutions, cost, integrational and organizational aspects as well as safety & security are influencing the decision of enterprises and organizations to choose a public cloud or on-premises solution.
Hybrid cloud is a composition of a public cloud and a private environment, such as a private cloud or on-premises resources,
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 cannot 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.
Others
Community
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
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.
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 build 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.
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).
Poly
Poly cloud refers to the use of multiple public clouds for the
purpose of leveraging specific services that each provider offers. It
differs from Multi cloud in that it is not designed to increase
flexibility or mitigate against failures but is rather used to allow an
organization to achieve more that could be done with a single provider.
Big data
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.
HPC
HPC cloud refers to the use of cloud computing services and infrastructure to execute high-performance computing (HPC) applications. These applications consume a considerable amount of computing power and memory and are traditionally executed on clusters of computers. In 2016 a handful of companies, including R-HPC, Amazon Web Services, Univa, Silicon Graphics International,
Sabalcore, Gomput, and Penguin Computing offered a high-performance
computing cloud. The Penguin On Demand (POD) cloud was one of the first
non-virtualized remote HPC services offered on a pay-as-you-go basis.
Penguin Computing launched its HPC cloud in 2016 as an alternative to
Amazon's EC2 Elastic Compute Cloud, which uses virtualized computing
nodes.
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 of 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 technology engineering, security, platform, risk, and quality engineering.
Security and privacy
Cloud suppliers security and privacy agreements must be aligned to the demand(s) requirements and requlations.
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 delete information.
Many cloud providers can share information with third parties if
necessary for purposes of law and order 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. Identity management systems
can also provide practical solutions to privacy concerns in cloud
computing. These systems distinguish between authorized and unauthorized
users and determine the amount of data that is accessible to each
entity. The systems work by creating and describing identities, recording activities, and getting rid of unused identities.
According to the Cloud Security Alliance, the top three threats in the cloud are Insecure Interfaces and APIs, 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 the 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 seven 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 not have expertise in IT
security could find that it is 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 not
read the many pages of the terms of service agreement, and just click
"Accept" without reading). This is important now that cloud computing is
common and required for some services to work, for example for an intelligent personal assistant (Apple's Siri or Google Assistant).
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.
The attacks that can be made on cloud computing systems include man-in-the middle attacks, phishing attacks, authentication attacks, and malware attacks. One of the largest threats is considered to be malware attacks, such as Trojan horses.
Recent research conducted in 2022 has revealed that the Trojan horse
injection method is a serious problem with harmful impacts on cloud
computing systems.
Market
According to International Data Corporation (IDC), global spending on cloud computing services has reached $706 billion and expected to reach $1.3 trillion by 2025. While Gartner estimated that global public cloud services end-user spending would reach $600 billion by 2023. As per a McKinsey & Company
report, cloud cost-optimization levers and value-oriented business use
cases foresee more than $1 trillion in run-rate EBITDA across Fortune 500 companies as up for grabs in 2030.
In 2022, more than $1.3 trillion in enterprise IT spending was at stake
from the shift to the cloud, growing to almost $1.8 trillion in 2025,
according to Gartner.
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.
Cloud computing uses concepts from utility computing to provide metrics for the services used. Cloud computing attempts to address QoS (quality of service) and reliability problems of other grid computing models.
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).
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 the 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.
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).
Cloud sandbox
– A live, isolated computer environment in which a program, code or
file can run without affecting the application in which it runs.
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