Cloud-Based Emerging Services Systems: IoT, Big Data, and Analytics

This research paper explores cloud-based emerging services and their application within the Internet of Things (IoT) paradigm, emphasizing the effective utilization of data analytics. It delves into the amalgamation of modern technologies, including cloud computing, IoT, and Big Data, to support these emerging services. The study introduces a novel classification, CNNC (Clouda, NNClouda), for cloud data models and discusses relevant case studies to strengthen this classification. The paper examines the evolution of IoT, the growing need for robust data models, and the role of cloud computing in addressing Big Data challenges. It provides an overview of emerging services, analytics, and cloud computing, highlighting the benefits of cloud adoption, and the various as-a-Service models. The research also discusses the impacts and benefits of these advancements on businesses, offering a comprehensive understanding of the subject.

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Cloud-based Emerging Services Systems
Sugam Sharma1*, Victor Chang2, U Sunday Tim3, Johnny Wong4, Shashi Gadia4
1. Center for Survey Statistics and Methodology, Iowa State University, Ames, Iowa, USA
2. School of Computing, Creative Technologies and Engineering, Leeds Beckett University, UK
3. Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, Iowa, USA
4. Department of Computer Science, Iowa State University, Ames, Iowa, USA
Email: [e-mail]
Abstract. The emerging services and analytics advocate the service delivery in a polymorphic view that successfully
serves a variety of audience. The amalgamation of numerous modern technologies such as cloud computing, Internet
of Things (IoT) and Big Data is the potential support behind the emerging services Systems. Today, IoT, also
dubbed as ubiquitous sensing is taking the center stage over the traditional paradigm. The evolution of IoT
necessitates the expansion of cloud horizon to deal with emerging challenges. In this paper, we study the cloud-
based emerging services, useful in IoT paradigm, that support the effective data analytics. Also, we conceive a new
classification called CNNC {Clouda, NNClouda} for cloud data models; further, some important case studies are
also discussed to further strengthen the classification.
Keywords. Emerging services, as-a-Service, analytics, cloud computing, IoT, Big Data, CNNC
1. Introduction
Emerging services and analytics promisingly hide the complex details of the comprehensive data processing from
the end users and deliver a suave and plain results. The resulted outcomes are easily interpretable even by the
technically naïve stakeholders (Chang, 2015; Chang et al., 2015), managers, and even a common user. The success of
emerging services and analytics is largely depending upon the blending of various modern technologies and cloud
computing, and IoT (Internet of Things) are just to name a few and are the core ingredients. The information age is
shifting from traditional human-intervened Internet to Internet of Things (IoT) (Figure 1(a)), where the sensor
embedded commodity, also called things, communicate among themselves (Chen, 2012) on the existing network
resources and help facilitate the information collection and analytics with a very high degree of automation. The
pool of IoT-enabled devices is growing rapidly and IoT footprint in increasing almost all in domains that includes
trivial to complex applications such as smart grid (Monnier, 2013) to advanced smart cities (Zanella et al., 2014).
The IoT “Things” include a diverse range of embedded objects or devices such heart monitoring implants, sensor
equipped automobiles, thermostat systems and so on (Hwang et al., 2013). The increasing IoT maturity and the
advancing cloud-based services are revolutionizing the information generation, collection, management and
analytics. In IoT realm, the devices collect useful data and then share the data between other devices (Farooq et al.,
2015); there are 9 billion such devices that exist today and the number is inflating. Evans (2011) believes this
number will reach nearly 50 billion by year 2020 (Figure 1(b)), which substantiates the fact that the permeation of
IoT is expanding in human life. This indicates that the number of new applications under IoT umbrella is increasing
and consequently a massive amount of data is being generated at swift rate, called Big Data (Villars et al., 2011),
where the social media and sensor embedded systems play central role in data inundation. As, the IoT-enabled
devices access and process the data from several peer devices to constitute immediate decisions and swift actions,
there is a continuous need for equally strong data model, equipped with robust data engineering and comprehensive
analytical capabilities as the traditional data models are becoming inadequate in meeting the growing challenges that
information-rich IoT evolution possess.
To appropriately accommodate and adequately address the growing IoT data challenges, the concept of cloud
computing framework has emerged as a globally acceptable solution for secure and efficient data engineering and
functionally-rich analytics for technically naïve stakeholders, director, managers to tech-savvy users. The backbone
of the widely successful cloud adaptation is the secure and robust existing or evolving services that are delivered in
the form of *-as-a-Service and the state of the art as-a-Service cloud modality has gone beyond the core
Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), Software-as-a-Service (SaaS), and Database-as-a-

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Service (DBaaS) (Curino, et al., 2011; Seibold and Kemper, 2012) and this paper highlights some of them that are
highly relevant in IoT context.
Today, a trend of migration, from on-premises to cloud environment, of applications from almost all possible areas
is increasingly being observed to utilize the best offered services at substantially reduced cost of cloud computing;
the progressive maturity of IoT is accelerating this migration. The need for the efficient and successful management
and computation of the scalable Big Data motivates the scientific communities to devise and develop the new
scalable systems. Consequently, the appeal introduces and inducts a high abundance of cloud and non-cloud data
models; it becomes nearly impractical for a user to thoroughly access such a large set of data models to understand
individual’s technology in search for the most suitable cloud model. In this work, we harness the scientific name of
the data models and introduce a novel CNNC {Clouda, NNClouda | a- assisted, NN- No Name} classification. The
CNNC classification is based upon the intuitiveness of the scientific name of the cloud data model and considers
two types of cloud data models -1) the model contains the term “cloud” in their name (Clouda), and 2) the model
doesn’t have the word “cloud” in its name at all (NNClouda). In addition, some interesting case studies are also
explored to further strengthening the core of CNNC classification amid the broadening spectrum of emerging
services and analytics in IoT age.
Rest of the paper is structured as follows. Section 2 provides a brief overview of the emerging Services and
analytics. This section expands on the concept of cloud computing and IoT. Also, explored are some of the emerging
analytics techniques. Section 3 elaborates on the novel CNNC classification and investigates some key data models
of Clouda and NNClouda type to further understand and validate the proposed classification. Section 4 discusses
some interesting case studies, focusing on the undertaken cloud models. Section 5 highlights on some emerging
cloud services, which is followed by the extended discussions on emerging services and analytics in section 6.
Section 7 explains the impacts and benefits of this work to businesses. Finally, the paper is concluded in section 8.
2. Emerging Services and Analytics: Brief Overview
This section provides the brief overview of some core components of emerging services and analytics. The
discussion on the emerging cloud services is elaborated a bit comparatively.
2.1 IoT
IoT is a network constituted by uniquely identifiable commodity objects or devices equipped with some sensing
system (Ashton, 2009). IoT paradigm promotes a seamless amalgamation between the smart devices, scatter around
us, and the physical world to ensure full automation that eventually ameliorates human life. Some of the examples of
IoT-enabled commodity devices or things include heart monitoring implants, automobiles with embedded sensors,
firefighter’ devices, smart thermostat systems, and Wi-Fi enabled washer/dryers (Sundmaeker, 2010). As the arena
of IoT is expanding, the number of IoT-enabled applications is also rapidly growing, which results in massive
growth of smart devices in multiple order comparatively (Figure 1(b)). This swift increase in the number of sensing
things is responsible for generating and storage of a plethora amount of Big Data at much faster rate. The data needs
to be engineered and analyzed, which require robust services and analytical systems as the traditional techniques
unable to successfully and efficiently accost such data stress.
2.2 Big Data
IoT paradigm is increasingly encouraging the ubiquitous connectivity of the intelligent objects within internal or
external world. The continuous rapid growth of large number of IoT-enabled objects and storage technology have
resulted into the massive amount of heterogeneous digital footprints and sizeable traces. A vast amount of data
(IBM, 2012) is being generated by various sensing sources every day. The actual pattern and nature of such data is
indistinct, but is certainly large, complex, heterogonous, structure and unstructured (O’Leary, 2013). Palermo (2014)
demonstrates some important attributes of Big Data such as volume, variety, and velocity and some core
constituents of IoT like sensor-embedded devices, intelligence for quick decision making, and connectivity for data
sharing. Also, the rapid growth of sensing devices under IoT purview is generating such a large scale complex and
heterogeneous data that the available computing capacity of the existing systems (Figure 2) unable to successfully
match up the data challenges and today, this has emerged as one of the core issues for the data science community

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Figure 2. Storage vs. Computation capacity (Source: Hilbert et al., 2011)
(Manyika et al., 2011; Hilbert and López, 2011). The storage capacity and also the processing power of the existing
data computing systems are failed in handling the stress of Big Data.
As IoT and its applications are majorly impacting the human life, the scientific communities contemplate a broader
outreach from the processing and sharing of Big Data across the variety of the several commodity devices around us.
Consequently, the development of new capable services and analytics is encouraged to cater the current data
processing and presentation need. The exploratory analysis of the various aspects of Big Data in cloud can help in
understaing its important characterstics (Nugent et al., 2013) that may be very valueable to stakeholders and
managers. Sharma et al. (2014, 2015a, 2015b) provide the elaborated discusisons on the characteristics and
complexities of Big Data.
2.3 Cloud computing
Cloud computing has emerged as a ubiquitous paradigm for the service-oriented computing; it is believed to
overcome the bottlenecks, experienced by the traditional computing systems when deal with the dynamics,
complexities and uncertainties of the modern IoT-inclined data-oriented enterprise applications. Over the years, the
basic cloud paradigms -- Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service
(SaaS) that have attained significant popularity and in the IoT age, a new paradigm- Database-as-a-Service (DBaaS),
Figure 1(b). IoT growth over years
(Source: www.ncta.com/broadband-by-
the-numbers/)
Figure 1(a). Traditional vs. IoT paradigm
(Source: db.in.tum.de/teaching/ws1314/industrialIoT/)

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also getting popular. Elasticity in service usage and consequently price (pay-per-use), low financial inputs upfront,
reasonably reduced time to market (TTM), responsibility shift in risk management, etc. are some of the potential
convincing attributes of cloud computing. Cloud computing is a monetarily affordable, and technologically powerful
innovation that is developed for overly complex and excessively large computations; that renounces any need for a
dedicated space, and on-premises management of expensive hardware infrastructure and delicate software systems
and figure 3 depicts the popular base cloud services.
• Software-as-a-Service (SaaS): SaaS is one of the most popular, repository-rich and widely used cloud
model that is offering the services for more than a decade now. The service repository is much diversified
and cover a very wide range of simple to complex services such as Google Email, Google Doc, etc. Under
the contractual agreement of SaaS model, the vendor also called the service provider is fully responsible to
provide all the essential infrastructure consisting of the robust hardware resources and expansive software
systems. Also provided is a graphical user interface (GUI) that facilitates the user interaction with the
service.
• Infrastructure-as-a-Service (IaaS): As, the name suggests, IaaS offers the infrastructure as service. The
infrastructure consists of various building blocks, which can be combined or layered to derive a customized
environment most appropriate to execute the designated applications. Some of the most popular examples
of IaaS cloud model include Amazon Web Services (AWS), Rackspace, etc.
• Platform-as-a-Service (PaaS): In the PaaS cloud model, the service provider is responsible to provide the
risk free and robust environment for software product development. The environment consists of required
software tools and is hosted on the hardware infrastructure of the service provider. A user can avail the
benefits of the PaaS for the software development, either by using the APIs provided or thorough a GUI.
Google App Engine, Force.com, etc. are some of the popular example of PaaS.
• Database-as-a-Service (DBaaS): The DBaaS cloud model (Curino, et al., 2011) is gaining popularity in
this data science age (Wolfe, 2013). The service agreement under DBaaS assures the shift of data
management related responsibilities, especially administration from end user to a third party vendor, called
service provider, who can be public or private. Some of the data related operational burdens include
upgrade, provisioning, failover management, configuration, seamless scaling, performance tuning, backup,
privacy, access control (Seibold and
Kemper, 2012), etc. DBaaS not only
migrates the responsibilities, but it
guarantees overall a very lower costs to the
end users. Also, on the client site, the
DBaaS model avoids any needs of a
professionally trained database
administrator (DBA) who is primarily
responsible of data management and
maintenance otherwise. In DBaaS, the
engineers, engaged in application
development are neither expected nor
required to have expert understanding of
database management. The data related
operations such as failover, scaling, etc. in
DBaaS cloud is expected not to impact any
live user.
2.4 Integrated cloud computing and IoT ecosystem
The growing smart communication among the things, especially the sensor equipped, under the purview of IoT is
resulting into production of incredibly large amount of Big Data. Supported by the context-aware computing, Big
Data is sufficient enough to address the nontrivial and comprehensive tasks with great degree of automation
regardless the disciplines - financial, health, automobile, etc. Soon, the concept of IoT is going to deeply pervade
Figure 3. Base cloud services

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through the human life intending to automate the routine chores. The ubiquitous sensing of all the devices around us
emits enormous quantity of Big Data that needs to be stored, computed and visualized and analyzed in efficient
manner. Today, cloud computing is the most recommended solution for above mentioned issues of Big Data that
promises to deliver the efficient services as traditional commodities. The emerging cloud services are being
appreciated for supporting the analyses of Big Data for naïve to elite experts such as stakeholder, mangers and data
scientists for their routine tasks. The needs for the highly robust computing and analytical services to address and
analyze the data-related challenges with the exceedingly reduced cost have further brought the service-rich cloud
computing to forefront. The rising interest in IoT and cloud provisioning system has triggered the surge in cloud-
housed comprehensive analytics. In this paper also, we have illustrated some important related cloud-based analytic
services that are highly efficient for the dedicated tasks and data intensive applications, they have been developed
for; gene structure prediction, image processing, predicting the relationship of customers and their purchasing
patterns and web searches are some good examples of such data-rich applications and for the required appropriate
analytical services to analyze their highly complex and heterogeneous data cloud is the pertinent choice from the
security, efficiency and affordability aspects. Academia, industry and research communities are rapidly developing
robust analytic frameworks and housing them in cloud environment, and are delivered and utilized as analytics-as-a-
Service. In 2008, to deal with the collaborative complex computations, instead of data in the cloud, Cerri et al.
(2008) devised an analytical framework in cloud, called knowledge in the cloud that delivered multifarious
knowledge. Figure 4 shows the cloud computing and IoT ecosystem. It can be noticed that a sensing system, may
differ in attributes, is embedded into the objects /devices around us. The device communication with cloud is
facilitated through the robust cloud services. However, in IoT infrastructure a smart connectivity gateway, may be
with the existing networks, is required to further strengthen the device communication.
The devices proactively generate the heterogeneous Big Data, which is housed in the cloud in various formats and is
available for analysis through robust cloud services. Therefore, the communication between the cloud and the
external word is feasible only through robust and reliable cloud services.
2.5. Machine learning and predictive analytics
Machine learning and its supported predictive analytics provide the intelligent and efficient processing of data and,
the wide usage of these analytical technologies has brought them to fore in data science, especially for the new
incarnation of data into Big Data. The machine learning algorithms help recognize the subtle association of the
variables in the dataset, and satisfying all the input criterions, the analytical systems quickly deliver the output,
presented in the desired form that is easily interpretable by the end users or stakeholders (Chen and Zhang, 2014).
The predictive analytics are highly sought in business domain in the view to elevate the product sale. These
techniques are useful in predicting the items types, a customer may be interested to buy and the associated
approximate spending (Kumar, 2008). As the customer behaviors is closely predictable using the predictive
Figure 4. Cloud computing and IoT ecosystem

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modeling and this enhances the expansion of the business opportunities and also the customer may be recommended
and served with special offers and discounts.
2.6. Agile methodology
The stakeholders always desire to have the even the comprehensive software products ready in a shorter time. Also,
the functional or visual requirements related to that specific product from the stakeholders change several times over
the span of that product development. To accommodate all such limitations, restrictions, and complexity and to
promisingly deliver the robust software systems in smaller time span, standard agile development is highly efficient
(Zhang, Cheng, and Boutaba, 2010).
2.7. Statistical analytics
The evolution of data science in the form of Big Data has brought the statistical analytics to forefront regardless the
domain. The stakeholders are applying the statistical approaches on their customer databases to derive useful and
accurate recommendations to improve their business. Also, some statistical frameworks such as R programing have
gained unexpected popularity due to its rich set of statistical libraries; which are capable to produce the quick results
and their ability for swift, effective and on-the-spot variation in presentation as desired by the end users or
stakeholders. Also, regression testing is one of the highly sought mechanism in statistical analysis along with the
easily interpretable presentation of the crucial outcomes (Antcheva et al., 2009; Miner et al., 2009).
2.8. Visual analytics
With the recent advancements of data science, where data has taken a more complex shape in the form of Big Data,
visual analytics have become extremely relevant to deliver the elegant and informative presentations of the highly
complex data processed for the end users. Visual analytics also come equipped with some sophisticated statistical
models to be used by the end users or stakeholders (Antcheva et al., 2009; LaValleet al., 2013) to obtain the desired
interpretation. In business domain, the visual analytics are extremely used to envisage the market and business
trends and customer shopping behavior in order to improve the future decision making to elevate the business.
3 CNNC Classification for Emerging Services Models
With the growing projection of IoT smart, embedded, and sensing devices (Evans, 2011), the number of available
cloud-powered models for Big Data management is increasing; every month, the inclusion of several new models is
being obviously observed and claim to deliver the effective solutions when deal with Big Data. As the number of the
cloud providers is rising, it becomes nearly impossible for a cloud-interested user to surf through that giant pool of
models to comprehend their technology and suitability for his/her purpose, especially from cloud aspect of IoT. In
this case, it is very likely that a user fails to hit the most pertinent model due to several reasons such as gradually
eroded user patience, restricted time availability, etc.
In such scenario, to assists the user, we focus on the name of the cloud models and propose a novel first-level
classification, called CNNC. The objective of this classification is to gauge the intuitiveness of the name of the
cloud models.
The CNNC classification is believed to reduce the search time and consequent pain of the user to a greater degree.
Only the cloud-powered data models are eligible for this classification. The scientific names of the cloud-assisted
models are considered as the basis for classification, and are divided into two categories -- Clouda and NNClouda.
CNNC = {Clouda, NNClouda}, where NN- No Name, a- assisted
We further mature and test the proposed CNNC classification, by applying and explaining it with some prominent
cloud-powered IoT- Big Data models. We believe, the discussion of this classification will invigorate the
communities for more intuitive naming convention, where only the scientific name of a model itself is merely
sufficient to gesticulate about underlying cloud technologies.

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3.1 Clouda (Cloud-assisted) models
Definition 1. Clouda represents that class of
data models that have the term “cloud” in their
name.
Hypothesis 1. All cloud-powered Big Data
models have the word “cloud” in their name.
To validate this hypothesis, we review some
cloud-supported Big Data models of Clouda
nature.
3.1.1 CloudKit (Cloud): The CloudKit
framework (Hanson, 2015) is a new
technology that is meant to be used as service
for data transportation with (to/from) cloud.
CloudKit is not a local storage rather is a
transport mechanism that is used as the Rack
middleware. It provides an auto-versioned
RESTful JSON storage that has no schema
restriction. Although, it is a middleware
component for Rack-assisted applications but,
can be used on its own.
3.1.2 Cloud Datastore (Cloud): Cloud
Datastore (Google Inc., 2010) is a NoSQL datastore to store the schema-free, non-relational data in cloud. Each
instance of the Cloud Datastore is entirely managed by Google Inc. Cloud Datastore experiences no downtime and
the data is replicated across multiple datacenters, hence high data availability. Also, the Cloud Datastore scales up
automatically for growing data traffic, endorses ACID transactions, and offers eventual consistency for queries.
3.1.3 LightCloud (Non-cloud): LightCloud (Plurk.com, 2009), built on Tokyo Tyrant (FAL Lab, 2010), is an open
source, distributed key-value database that is always compared with memcached from performance point of view. It
is tested for storing millions of keys on a very small group of few servers. However, in contrast to memcachedb
(Chu, 2008), the LightCloud is horizontally scalable with master-master replication architecture and commodity
hardware nodes are easily added to achieve it. Although, Salihefendic (2009) in his blog quotes about the
LightCloud as “Plurk's open-source cloud database LightCloud…,” but, the available documentation on LightCloud
does not confirm the LightCloud reliance on cloud.
3.1.4 Cloudera (Cloud): Cloudera lately has drawn widespread attention for its completely open source, Apache
Hadoop distribution, popularly known as CDH (Cloudera’s Distribution for Hadoop) (Cloudera, 2012b). CDH is a
regressively tested distribution for Apache Hadoop, which has been widely deployed with extensive outreach. CDH
is one of the most complete solution for Hadoop, which provides to the end users the batch processing of the
massive data, support for MapReduce inclusion, interactive SQL and search, etc.
3.1.5 PiCloud (Cloud): PiCloud (Elkabany and Staley, 2014) provides a cloud computing environment that helps
the users to avail the massive computational power (thousands of cores) from Amazon Web Services (Cloud, 2011),
by completely avoiding the management, maintenance, or even configuration of the virtual servers. The access to the
PiCloud interface is feasible using 1) a command line, 2) a Python cloud library, or 3) web base GUI and can be
customized to accommodate other programming languages, though, Python is the default.
Figure 5.CNNC classification for emerging service models

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3.2 NNClouda (No Name Cloud-assisted) models
Definition 2. NNClouda is the representation for that class of Big Data models, which are cloud-powered, but do not
have the word “cloud” in their name.
Hypothesis 2. The cloud-powered models not necessary to have the word “cloud” in their name.
In order to investigate the hypothesis 2, we analyze some of the data models of NNClouda class; the data models
should not contain the word cloud in their name but must be cloud-enabled.
3.2.1 1010data System (Cloud): The 1010data (Bloor, 2011) is a columnar database system that has advanced
dynamic in-memory capability. It is a cloud base solution that best avails the massively parallel processing for data
management. It is a suite of advanced analytics that is devised and designed to perform the analytics exactly on the
designated data elements, which are intended to be analyzed. The data is comprehended in orderly fashion and in the
inherent structure, producing the efficient results.
3.2.2 Algebraix System (Cloud): ALGEBRAIX (Silver, 2005) is a cloud technology that harnesses the power
modern computer architectures and offers such a robust data analytics operations for Big Data, which are not
possible in the confines of conventional database systems, which are inflexible and inefficient to address the Big
Data challenges. The ALGEBRAIX uses the full potential of mathematical models and maintains a universe of
algebraic expressions relating data. The data is loaded in its native format and operations are performed on the same.
3.2.3 Azure DocumentDB (Cloud): Azure DocumentDB (Microsoft Azure, 2015) as a whole provides an
enterprise-proven hybrid IaaS and PaaS cloud infrastructure where DocumentDB serves as a NoSQL document
database, which inside the database engine natively supports JSON and JavaScript. DocumentDB is born in the
cloud and backed by blazingly fast, low-latency, write-optimized, solid-state drive storage. Being a cloud platform,
Azure DocumentDB can be scaled up or down quickly according to the user’s requests and demands.
3.2.4 Datameer (Cloud): Datameer, Inc. (Groschupf, 2009) provides cloud based (PaaS) effective, self-serviced,
and schema-free analytics and visualization support for Big Data application for Hadoop. It also offers the
deployment- specific SaaS Big Data analytics platform. Datameer is highly scalable up to thousands of nodes and all
major distributions of Hadoop can best use it. Datameer provides a cost-effective Hadoop-as-a-Service (Schneider,
2012) platform and Datameer Analytics Solution (DAS) is one of the popular services that Datameer offers. This
avoids the on-premises overly complex configuration and deployment of Hadoop and therefore, helps the business
executives and managers to stay dedicated to analytics only and utilize their time much effectively.
3.2.5 Dydra (Cloud): Dydra (Anderson, et al., 2011) is a fully managed cloud based graph database, which, natively
stores the data as a property graph. The property graph clearly shows the relationships, if any, in the underlying data.
Dydra is a very useful solution for a vast variety of IoT-enabled Big Data applications as most of the problems are
reducible to graphs with little or more efforts. Dydra is a multi-tenant data store and query engine. Dydra allows the
data operations through the query language, called SPARQL. It is an industry-standard, which specifically devised
to query the graph databases such as Resource Description Framework (RDF) (Pan, 2009).
Validation of hypotheses. With the help of the exploration of cloud-powered models, we examine the correctness of
both the hypotheses. Figure 5 provides a glimpse of CNNC classification, where the element Clouda is useful in
validating the 1st hypothesis, and other constituent NNClouda will be helpful in testing the validity of hypothesis 2.
In figure 5, it can be observed, out of all the children of Clouda, at least one child, LightCloud is dissimilar from
rest of its siblings. The LightCloud model has on-premises data storage and operations, in contrast to its other
siblings, which believe in cloud-enabled storage. According to the hypothesis 1, the cloud-powered data models
have the word “cloud” in their name, however, the investigation on Clouda class discovers that explanation of
LightCloud is unable to validate this hypothesis; the LightCloud (Light + “Cloud”) model has the word “Cloud” in
its name, but is not cloud-powered at least yet, rather perform on-premises data storage and related computations
and therefore, fails the hypothesis 1. The investigation on NNClouda category discloses that all its children,

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examined in this paper are cloud-powered and not any of them has the word “cloud” in their scientific name; this
fact correctly validates the hypothesis 2.
4 Related Case Studies
In this section, we briefly survey some of the modern and important case studies that discuss and further substantiate
the widening bandwidth of cloud computing into IoT and Big Data, especially Big Data. The case studies are
selected from numerous public and private areas such as healthcare, IT software and hardware, media, eservice,
ecom, gaming, space, etc., in order to procure the better investigating outcomes with diversified projection.
4.1 CDC (Public health): The Centers for Disease Control and Prevention (CDC) always strive to improve public
health concerns (Kass-Hout, 2013). Lately, CDC started a health awareness program, called BioSense 2.0. The goal
is to provide the national, state or local level awareness about all the health-related threats and their appropriate
responses. To bypass the financial burden of purchasing costly hardware infrastructure and software systems, and to
avoid the involved complex configuration, CDC adopted Amazon Web Service (AWS) cloud. Which provides the
low cost operations with pay-per-use scheme. Also, the hosted systems are high available. The cloud has full support
for the security and practice compliance.
4.2 DellSecureWorks (IT services): Dell SecureWorks, with global headquarters in GA, USA, is an information
security services industry that protects thousands of customers against cyber threats and attacks all around the globe
(Cloudera, 2012a). Dell SecureWorks collects and analyzes the information on millions of simultaneous IT events
occurred globally in a few milliseconds. Dell SecureWorks operates round the clock and provides real-time security
for the IT related assets of its clients. In order to provide and maintain such promising services, Dell SecureWorks
has to process enormous data on high-end technologies, demanding high cost. Cloudera offers Dell SecureWorks a
better solution that address the data processing challenges. The solution is highly cost-effective and speedy for
storing, scaling, and analyzing the massive amounts of information in real-time.
4.3 Expedia (Eservice): Expedia, Inc. (Kobashigawa-Bates, 2010) is one of the world’s largest online travel agency,
consisting of numerous popular brands such as Hotels.com, Hotwire.com, etc. in more than 60 countries. Along with
the other operations, the direct bookings to travel suppliers, and advertising opportunity are highly popular and the
company experiences heavy traffic every day. Expedia also powers its partners the same services. Observing a
regular increase in through traffic, Expedia moved to use Amazon Web Services (AWS) in 2010 to avail the global
infrastructure to support Asia Pacific customers and to solve key issues such as automation, and customer proximity,
etc. By 2011, Expedia shipped its several high-volumes critical applications to AWS and Global Deals Engine
(GDE) is one such example; GDE administers the deals with its online partners and allows them to use Expedia
APIs and other tool tools for various purposes.
4.4 NASA (Space): National Aeronautics and Space Administration (NASA) has numerous centers around the
nation. Jet Propulsion Laboratory (JPL) is one of the premier centers that is constantly engaged in the robotic
exploration of space (Cureton, 2012). It has launched the robots to every planet in the solar system. It gathers huge
amount of imagery data, being pumped by the robotic sensors. For the Mars Exploration Rover and the Mars
Science Laboratory, the successful missions, NASA/JPL started to the Amazon Web Services (AWS) cloud is an
essential part of the tactical operations to capture and store the images and metadata collected. Also, NASA planned
to share the thrilled success and experience of Mars mission with the interested population around the globe. Hence,
it decided to make the most current up-to-the-minute details of the mission, especially during the final 7 minutes,
rover took to descend through the Martian atmosphere and land on Mars. So, JPL used AWS again to stream the
landing images and videos. Considering the fact the public users, spread all over the globe, would be visiting its
websites, NASA decided to served its contents from AWS clusters, situated around the world. This helped to meet
the global demand of heavy traffic experienced by websites and also offered enhanced the viewer’s experience.
4.5 Netflix (Media): Netflix, founded in 1999 and based in California, supplies the Internet streaming media on
demand to the users (TSE, 2015). The viewers almost all around the globe enjoy the subscription-based services of
Netflix. The major market includes the countries such as North America, South America, Australia, New Zealand,
Denmark, United Kingdom, France, etc. The content can be streamed online anywhere internationally. However, in

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United States, the flat rate DVD-by-mail system is also available, where the mailed DVDs are dispatched via the
Permit Reply Mail. Today, Netflix offer a collection of way more than 100,000 titles on DVD and has greatly
surpassed 10 million subscribers. The scalability of the subscription and the competitive pressure for supporting
other media devices such as tablets, iPhones, etc. are some of the leading factors that compel Netflix to explore the
advance solutions that promise the seamless global service. Netflix finally believes in Amazon Web Services (AWS)
for services and content delivery. AWS cloud has powered Netflix to deploy thousands of servers and terabytes of
storage in almost no time.
5 Emerging as-a-Service and Analytics Models
IoT can be understood as a pervasive system that may have more than one mesh of embedded sensing devices. In
coming years, the IoT paradigm is expected to encompass the scalable networks of billions of uniquely identified
devices, also called things. The devices are highly capable for sensing, computing, communicating, uniquely
identifying the other peer devices for communication and effuse humongous data streams; therefore, the IoT
paradigm triggers the emission of amassed data, where swift inclusion of cloud-based services and analytics
becomes indispensable in order to harness the full potential of the data and discover valuable knowledge, insight,
and complex relationships between variables of the data. The functionally-rich cloud-based services and analytics
help shifting the data engineering and computation burden from the end entity to the cloud environment and hide
that complex details from the end users. This greatly eases the analysis process, especially for technically naive
stakeholder, director, managers, or even for common users. Therefore, in recent years, the communities have
focused on the development of the cloud-supported services and analytics to deliver the customizable outputs that
suffice the needs of various types of users. In this section, we briefly illustrate some of the cloud-based emerging
services and analytics, which may be highly useful in growing IoT paradigm.
1) Business Integration-as-a-Service (BIaaS/BIaS) enables the connections between numerous cloud-
powered services and combines various other services and business activities to achieve a streamline
process (Chang et al., 2012; Chang, 2013).
2) Business Intelligence-as-a-Service (SaaS BI/BIaaS) is an emerging cloud-supported service and analytics
that efficiently facilitates the business intelligence exercise and provides the data retrieval through a web
resource. SaaS BI/BIaaS is highly useful to stakeholders, managers, and tech-naïve common user and it
completely hides the implementation details and presents the simple abstraction, thus elevate performance,
to them (Sano, 2014; Chang, 2014).
3) Business Framework-as-a-Service (BFaaS) is a cloud assisted solution for business organizations to
embrace the cloud-based services and practices. BFaaS is capable for providing the right strategies and
business cases, accurately reviewing business performance in cloud, resolving the desktop to cloud
migration issues and among heterogeneous clouds from several distinct providers, and smoothly
synchronizing between IaaS, PaaS, SaaS and business entities and between cloud-related disparate research
methodologies (Chang et al., 2013).
4) Cloud-Based Analytics-as-a-Service (CLAaaS) delivers a data analytics, housed in the cloud as service to
users. CLAaaS also facilitates on demand data storage and multifarious analytics using the customizable
user interfaces. Also, for different user group, the interfaces can be customized differently or uniquely for
query, decision management, and workflow design and service execution (Zulkernine et al., 2013).
5) Data-as-a-Service (DaaS) is a cloud-based relatively new service that on demand delivers the data to
consumers using the existing or new APIs. The use of DaaS helps avoid the typical need of - 1) storage and
retrieval of behemoth data assets, and 2) exhaustive searching in that colossus data assets in order to derive
the useful knowledge and statistics (Vu et al., 2012).
6) Data Integrity-as-a-Service (DIaaS) gauges the criticality of some data-oriented well-known issues; and
therefore, to address them appropriately, it combines all the expertise essential for data integrity. Public
verifiability and dynamic content are few such examples where DIaaS is highly useful. DIaaS shreds off the

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burdens of managing data integrity from a storage service as it manages the data integrity using a third
party independent data integrity management service (IMS). Also, the inclusion of IMS helps reduce the
security risk of the data stored in the storage services as now IMS checks the data integrity (Nepal et al.,
2011).
7) Data Mining-as-a-Service (DMAS/DMaaS) offers the data owners to utilize the hardware and software
solutions dispensed by DMAS/DMaaS providers and discourages them for the development of their own.
DMAS/DMaaS is specially effective and useful for those consumers who govern mountainous amount of
the data, but short in budget, allocated for data analysis. For such consumers, to outsource their data and
data mining operations to a third-party service provider that always raises some security and monetarily
concerns, but DMAS/DMaaS avoids the needs of any third-party service provider (Liu et al., 2012).
8) Database-as-a-Service (DBaaS/DaaS) is one of the most sought cloud-based emerging services in recent
times to address the swiftly grown market needs due to the data inundation from IoT and other outlets.
DBaaS/DaaS is highly effective in shifting much of the operational efforts such as provisioning, scaling,
performance tuning, backup, and privacy to service providers in cloud from the localized database owners,
administrations or users. Thereby, it guarantees lowering the overall expenditure as compared to the on-
premises operational cost. Amazon RDS and Microsoft SQL Azure are the early providers of DBaaS
(Curino, 2011).
9) Ethernet-as-a-Service (EaaS) is a cloud service that has high-bandwidth and fiber optic. It uses a two-way
broadband shared infrastructure to successfully provide ubiquitous connectivity to remotely located devices
(Zaslavsky et al., 2013).
10) Failure-as-a-Service (FaaS) is a cloud-based service that is useful in performing the routine large-scale
failure drills in real deployments. FaaS is capable to regularly conduct the large-scale failures online.
Thereby, it may further strengthen the ability to anticipate, mitigate, respond, or recover from failures at
individual or organizational level (Gunawi et al., 2011).
11) Forensics-as-a-Service (FRaaS) provides a comprehensive cloud-powered forensics solution to develop a
repeatable system. The system could be developed as a standard forensics operational model for the
deployment in cloud irrespective of the client service lines or environments (Shende et al., 2012).
12) Identity and Policy Management-as-a-Service (IPMaaS) a cloud-supported service, dedicated for the
policy management. It is conceived to deliver a unified control point the users, where they are capable to
manage and access the policies and to control the access to their resources regardless the stored location of
these assets (Takabi et al., 2012).
13) Mobility-as-a-Service (MobiaaS) is a cloud-enabled service that is highly useful in providing the required
connectivity service continuity to the consumers. It also capable to deliver seamless handover for flows like
voice as the consumers use a multitude of devices to communicate (Baliga et al., 2011).
14) Object-as-a-Service (ObaaS) is based upon the idea to dynamically build the service on each object as
demanded and subsequently integrate that into the whole composition. ObaaS executes on the objects and
exploits its sensing, actuating, and computing capabilities (Cherrier et al., 2014).
15) Security-as-a-Service (SecuaaS/SaaS) provides the cloud-based solution for data, host and application
protection. SecuaaS/SaaS is capable to validate and ensure the security aspect over a geographically sparse
scalable, multicloud and cloud federation infrastructure (Pawar et al., 2015).
16) Sensing-as-a-Service (S2aaS) is a cloud-powered sensing service that is offered through mobile phones.
S2aaS has enormous scope to serve under IoT paradigm and also highly useful in environmental
monitoring, social networking, healthcare, transportation and several other domains, where sensing is an
important part. S2aaS is highly energy-efficient service and supports several sensing applications through
various smartphone platforms (Sheng et al., 2013).

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17) Sensor-as-a-Service (SenaaS) is a cloud-powered service that is delivered for ubiquitous management of
the remote sensors. However, SenaaS has no role in any type of data collection or data dissemination
mechanism for the sensor data (Zaslavsky et al., 2013). SenaaS is another qualified candidate that also has
huge opportunities to serve in IoT paradigm.
18) Sensing and Actuation-as-a-Service (SAaaS) is to develop a cloud of sensors and actuators and deliver it
as a service for sensing and actuation process. The inclusion of sensors and actuators initiates the creation
of new, value added, quality services that eventually leads to pervasive cloud computing (Distefano et al.,
August 2012). Similar to SenaaS, SAaaS also has wide utility in IoT paradigm.
19) Sensor Event-as-a-Service (SEaaS) is a cloud-supported service for message and alert notification. The
service is enabled and triggered at the sensor events. It also grants access privileges to the users on sensor
data and measurements. SEaaS imparts capabilities for generation, subscription, and retrieval of the
message and alert notifications. SEaaS allows users the dynamic registration of the sensors, which is
followed by subsequent service notifications (Dash et al., 2010; Rao et al., 2012). Also, the role of SEaaS
in IoT paradigm is expectedly highly promising.
20) Storage-as-a-Service (StaaS/SaaS) is a cloud-based service to store the data in online storage space in
cloud. StaaS/SaaS has robust cryptographic algorithms to address the user concerns about the data security
and privacy. StaaS/SaaS also claims the lower computation cost, higher security and sensitivity based on
data significance (Patel et al., 2012).
21) Things-as-a-Service (ThiaaS) allows the implementation of innovative and the value-added services in a
cloud environment that is integrated with IoT paradigm. ThiaaS provides the mechanism to construct a
cloud of things, where the multifarious resources are either aggregated or abstracted (Distefano et al., July
2012). Therefore, ThiaaS obviously is one of the most qualified and useful services in IoT evolution.
22) Video Surveillance-as-a-Service (VSaaS) is a cloud-enabled service for video surveillance and facilitates
video recording, storage, remote viewing, and cyber threat and security management. For the on-premises
installed surveillance devices, the VSaaS shifts the video processing, management and analysis in cloud
environment unlike the conventional systems, where the computations are locally performed by the
computing unit attached to the device itself (Prati et al., 2013), which add significant overheads that may
dampen the performance.
6 Discussions on Emerging Services and Analytics
In order to obtain the valuable information from mammoth Big Data, it requires to be subjected through the robust
and advanced analysis processes. The emerging services and analytics help unwrapping the important hidden
patterns, uncertain correlations. Due to the semi-structured and unstructured complex nature, Big Data often does
not fit well in traditional analytic techniques and thus requires comprehensive software frameworks that are fully
equipped with advanced analytical technologies such as data mining, predictive and statistical analyses, etc.
Additionally, the mainstream data analytics tools also have tightly coupled vigorous data visualization capabilities,
which provide exceptional support even to the real-time data analytics. Over the years, the data analytics has been
expanded to seamlessly integrate the visualization aspect and in data science, it is popularly known as visual
analytics. The visual analytics offers the complete view of data in order to obtain value insights. The state-of-the-
art data analytics tools are potentially innovative and powerful and are highly capable for multidimensional data
visualization, which imparts a stronger perception of data landscape and extra intuitive understanding. Today,
there are several software tools for visual analytics that are either open source or commercially available such as
IBM’s InfoSphere and BigInsights (Keahey, 2013). These tools are developed with the emerging technologies
and help advancing the human capabilities related to intrinsic pattern recognition.
As, the new data-related challenges are surfacing due to the evolution of IoT, the scientific communities are
developing new services and analytics tools at the same pace to efficiently address them. Pachube (Haque, 2004)
is believed to be one of the few providers, which begin the online data-related services, especially facilitates the
attachment of sensor data to Web. Today, it delivers an IoT-enabled platform for cloud-based real-time data