A Deployable Data as a Service Architecture for Enterprises
Adri
´
an T
´
oth
1
and Mouzhi Ge
2
1
Faculty of Informatics, Masaryk University, Brno, Czech Republic
2
Deggendorf Institute of Technology, Deggendorf, Germany
Keywords:
Service Computing, Cloud Computing, as a Service, Data as a Service.
Abstract:
Nowadays, data have been considered as one of the valuable assets in enterprises. Although the cloud com-
puting and service-oriented architecture are capable of accommodating the data asset, they are more focused
on software or platforms rather than the data per se. Thus, data management in cloud computing is usually not
prioritized and not well organized. In recent years, Data as a Service (DaaS) has been emerged as a critical
concept for enterprises. It benefits from a variety of aspects such as data agility and data quality manage-
ment. However, it is still unknown for enterprises why and how to develop and deploy a DaaS architecture.
This paper is therefore to design a deployable DaaS architecture that is based on the as-a-Service principles
and especially tackles data management as a service. To validate the architecture, we have implemented the
proposed DaaS with a real-world deployment.
1 INTRODUCTION
Big Data has received increasing attention in recent
years, as organizations and cities are dealing with
tremendous amounts of data with high complexity
and velocity (Ge et al., 2018). These data are fast
moving and can originate from various sources, such
as social networks, unstructured data from different
devices or raw feeds from sensors (Ge and Dohnal,
2018). Thus, cloud environment has been used to ac-
commodate the Big Data and significantly facilitate
the development of service-oriented computing for
data management (Park et al., 2021). While service-
oriented computing not only provides a better perfor-
mance of service offerings, perception, and quality of
service delivery (Mishra and Kumar, 2018), it also
offers a foundation for the development, execution,
composition, and integration of business processes
that are distributed across the cloud computing net-
work and accessible via standard interfaces and pro-
tocols (Serhani and Dssouli, 2010).
Cloud storage and service-oriented computing al-
lows enterprises to focus more on effective cross-
platform application data exchange (Khan et al.,
2019). However, when an enterprise intends to mi-
grate or build their data center with service-oriented
computing, there is lacking of guidelines on how to
design and build a Data as a Service (DaaS). Beyond
understanding the well-known cloud service models
such as Infrastructure as a Service (IaaS), Platform as
a Service (PaaS), and Software as a Service (SaaS),
it is unclear that how the as a Service features can be
applied to data, and compared to other cloud service
models, what the specific components for DaaS are.
Thus, DaaS can on the one hand benefit from the gen-
eral as a Service features, on the other hand, data can
be managed as a service to potentially enhance the
resilience and reusability for data-driven applications
(Rao and Nayak, 2019).
This paper therefore proposes a DaaS architecture
that can guide users to deploy the data centre on the
cloud. It can enhance the data accessibility through
different channels, and eliminate the geographical and
scalability limitations (Rajesh et al., 2012). We will
provide a blueprint of how as a Service features can
be used for DaaS. The architecture is mainly focused
on specific components to construct the DaaS. The
proposed architecture is further validated by the phys-
ical implementations with real-world deployment.
2 AS A SERVICE
The term as a Service has been widely associated with
different service models while it sometimes can be
misleading or confusing to understand what actually
the as a service is (Duan et al., 2015). With respect to
the specific functionality, the as a Service on the cloud
278
Tóth, A. and Ge, M.
A Deployable Data as a Service Architecture for Enterprises.
DOI: 10.5220/0010470702780285
In Proceedings of the 6th International Conference on Internet of Things, Big Data and Security (IoTBDS 2021), pages 278-285
ISBN: 978-989-758-504-3
Copyright
c
2021 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Table 1: Summary of features in as a service model.
IaaS PaaS SaaS DaaS
Monitoring computing resources software systems software applications data transactions
Metering load of resources system performance application usage transaction attributes
Security user’s responsibility combined responsibility
of user and provider
provider’s responsibility
to protect application
software
provider’s responsibility
to protect data assets
Tenancy Partitioning - system instances application instances data pools
Availability dependent on data cen-
ter(s)
dependent on infrastruc-
ture
dependent on infrastruc-
ture or platform
dependent on infrastruc-
ture or platform
Scalability on-deman resources allo-
cation/deallocation
on-deman resources allo-
cation/deallocation
on-deman resources allo-
cation/deallocation
on-deman resources allo-
cation/deallocation
Fault Tolerance task resubmission, stor-
age backup, etc.
system replicas, load
balancing, etc.
application replicas, or-
chestration, etc.
data protection, data reg-
ulation, etc.
Distribution - enabled enabled enabled
and normal services may have the same functionality
but can be organized in different forms. For example,
the difference between software and software as a ser-
vice does not depend on the functionality but on how
the functionality is provided. Implementing the as a
Service method requires to meet a set of requirements
that guarantee the functionality provision (Moorthy
and Pabitha, 2019). Given that as a Service is con-
sidered as a subset of cloud service model, there are
features that are inherited, meanwhile there are some
specific features for data management. By reviewing
the common features from IaaS, PaaS and Saas. We
have derived the following features for as a Service.
Monitoring - it covers the operations and actions
connected to the provided service that are later to
be observed, checked, and processed for meter-
ing. Monitoring results are assets, logs, and audit,
which grant repudiation and accountability char-
acteristics to the service itself (Bouasker et al.,
2020). These monitoring data are considered as
a trusted source of recorded events, operations,
and transactions that are substantially significant
for further calculations such as billing for the used
resources.
Metering - it regulates the resource usage analy-
sis. This analysis takes into account the resource
restrictions concerning the rules in the service
level agreement (Narayan et al., 2017). The ser-
vice provider may deliver different service qual-
ities to the customers e.g. lower price for less
resources, higher price for unlimited operations.
The customers should be familiar with the terms
of service and these limitations. Metering can be
observed as a special type of monitoring that is
required for business and marketing purposes.
Security - It is a fundamental feature in the cloud
computing. Each interaction from outside or
within the service represents a potential security
risk that might lead to exploitation of a security
vulnerability (Mthunzi et al., 2020). The data se-
curity is critical and it has been moved to the pri-
mary security stipulation. as a Service introduced
dozens of challenging security problems that re-
sulted in a continuous security incident monitor-
ing, reporting, and resolving.
Tenancy Partitioning (Multitenancy) - It en-
sures that every tenant (user) is isolated from each
other and prevents violations between them while
each user is using a single instance of the appli-
cation (Aljahdali et al., 2014). Each tenant has
its own operational environment and he is able to
work in the space excluding the ability to use oth-
ers operational environment. For instance, the ap-
plication database can be configured to serve just
for a single tenant, which would prevent storing
multiple users’ data in the same database (Liu,
2010). There may be an exception when the ten-
ants share spaces consciously with respect to their
needs such as collaboration. Tenancy partition-
ing requires to implement access control and user
isolation. From the aspect of SaaS application,
this feature has been considered as highly impor-
tant (Aleem et al., 2019).
Availability - It can be seen as a warranty from
a cloud perspective. It is usually expected that
the as a Service derivations meet criteria of high
availability and reliability (Li et al., 2020). As as
a Service is adopted on the basis of cloud comput-
ing, the availability is guaranteed partially by the
cloud, which means that the cloud hosted service
itself should be also resilient to errors and fault-
tolerant.
Scalability - This is the ability to increase or de-
crease IT resources regarding the actual demand
A Deployable Data as a Service Architecture for Enterprises
279
(Bellavista et al., 2017). Scalability can be dif-
ferentiated into two types - horizontal and vertical
scalability. In vertical scaling we change the com-
puting resources (such as CPU, storage, RAM,
etc.) on an existing machine while in horizon-
tal scaling we change the pool of resources by
adding a new or removing an existing computing
machine. The resource addition is called scaling
up and resource removal is called scaling down.
Horizontal scalability mostly includes clustering,
load balancing fencing and other tools as well as
techniques.
Fault Tolerance - It is the capability of a sys-
tem that can operate continuously despite errors.
Most of the applications hosted on a cloud require
a high level of fault tolerance that is solved by
transparent replication of applications (Moham-
madian et al., 2020). We distinguish two types of
fault tolerances - proactive and reactive fault toler-
ance. Proactive fault tolerance means that the ap-
plication is preemptive by nature by using preven-
tion techniques before the appearance of the error,
such as precautionary migrations. On the other
hand, reactive fault tolerance reduces the conse-
quences of the already occurred errors using prac-
tices for instance backups and rollbacks.
Distribution - A service is usually not a stan-
dalone service that provides all the functionalities
(Suresh and Varatharajan, 2019). The service may
rely on other integrated services used for specific
operations and additional resources. The specific
responsibilities can be shifted to other services in
order to improve efficiency of the business and in-
crease the service quality.
Based on the derived features above, we have
compared the IaaS, PaaS, SaaS and DaaS, where how
the as a Service features can be used on the data is
highlighted in Table 1.
The service computing and its as a Service model
have a beneficial impact on IT related business. The
increasing amount of divergent as a Service confirms
the usefulness and effectiveness (Prasad et al., 2014).
When the enterprises intend to use additional ser-
vice features, these services can be implemented on-
demand. This can influence the costs per resources or
usage. The underlying architecture of as as Service
model facilitates the extensibility and adaptive scal-
ing. It reduces processing time and costs for enter-
prises. Furthermore, the ubiquitous access makes the
service widely accessible to a wide range of users.
3 DATA AS A SERVICE
ARCHITECTURE
An architecture provides foundation components that
reveals the implementation requirements. Thus, the
DaaS architecture can be considered as a general tem-
plate, which includes fundamental concepts and pat-
terns from various disciplines to facilitate the adop-
tion and implementation of DaaS. The DaaS architec-
ture defines an architecture at a conceptual level while
the physical levels are designed by the enterprise it-
self based on their available technical resources. In
order to construct the DaaS, data architecture design
needs to conforms with the service design. First of
all, the architecture needs to ensure that heteroge-
neous distributed data are provided from its author-
itative source, which requires a data acquisition com-
ponent. Next component processes the acquired data,
while it applies the necessary data policies, rules, and
regulation. A separate component is allocated for se-
curity because there is nothing more vulnerable than
the data itself. Afterward, as the data are processed,
the data (including meta data) are delivered to the re-
quester in a specific format. There is also a particu-
lar need for service management, because the DaaS
should meet the service-level agreement criteria and
service quality expectations, which are committed be-
tween a provider and a client.
We propose the DaaS architecture that includes
the following components. All those components are
organized in Figure 1.
Data Acquisition - it formulates an interconnec-
tion layer between the system and each registered
data sources.
Data Management - it defines data policies that
guarantee consistent data manipulation manners.
A set of standardized techniques are required in
such an environment, which manage interoper-
ability between diverse and heterogeneous data
flows.
DaaS Engine - this is the core of the DaaS that
processes the data request accordingly to the data
policies, rules and further data regulations.
Data Regulations - applies the rules and laws from
the area of authority and legislation. The DaaS
needs to comply with the established legal entitle-
ments, which are formally defined.
Security - this is responsible for the continuous
protection of DaaS data assets. Cyberattacks are
still evolving by the time using more sophisticated
methods than before. It is necessary to protect and
ensure high security level for each component of
DaaS during the entire operation of the service.
IoTBDS 2021 - 6th International Conference on Internet of Things, Big Data and Security
280
Service Management - it ensures that DaaS will
satisfy all the customer expectations and re-
quirements and maximize the business value and
progress efficiency of the service. A service
needs to continuously improve its capabilities
to assure achieving a high customer satisfaction
even though the customer data requirements may
change from time to time.
3.1 Data Acquisition
The integration of heterogeneous data on distributed
data sources introduces two principal challenges -
integration of different data sources into one data
model, and performing manipulating operations on
the data (Khan et al., 2019). Data acquisition rep-
resents an interface layer between data sources and
the DaaS. This component of DaaS is the foundation
layer in DaaS, which ensures proper data acquired
from physical data sources.
A data source is an self-organized entity that sup-
plies the data. It can be a database, IoT devices, cloud
storage, data warehouse, distributed file system, etc;
which might support further operations beyond this
data (Weik, 2001). Each data source can have its own
interface that implements an access protocol. To in-
tegrate the data, the data sources need to support one
commonly shared data mapping protocol in order to
deal with the problem of data divergence with differ-
ent communication protocols. Nowadays, there are
some promising technologies such as message broker
that provides a translation from sender’s formal mes-
saging protocol into the receiver’s formal messaging
protocol, which mitigates the complexity of such a
problem.
Before data operations, data discovery is con-
ducted to select the corresponding data sources that
includes the necessary data. The data discovery is
responsible to select and prepare in advance the re-
quired data sources that includes pertinent data, which
will be required for the later search execution. The
output of the data discovery phase is a particular list
of data sources, which also includes supplementary
metainformation (Terzo et al., 2013).
Data discovery is followed by data mapping that
maps proprietary source data into a standardized data
structure. The data mapping transforms the data set
into an understandable format and ensures consis-
tency for later processes. The format represents a pre-
defined structure - a schema.
As certain data might be separated and stored in
different data sources, data need be aggregated cor-
respondingly. Data aggregation is a process of data
transformation with the intent to prepare combined
data for subsequent data processing. The data aggre-
gation can be described as a merge of multiple data
schema from the data mapping into one entity - more
complex schema that will be later processed.
As a fundamental component of DaaS, data acqui-
sition interconnects the whole system with each data
source that is managed by this system. Data acquisi-
tion solves the data access problems in DaaS such as
disparate data formats, access to the data by different
interfaces, and data source selection. This component
is closely connected to the DaaS engine, which inter-
acts with the data through this component.
3.2 Data Management
Data management includes all the data operations that
are more focused on the data values. As Gartner
defines the data management as “Data management
consists of the practices, architectural techniques, and
tools for achieving consistent access to and delivery of
data across the spectrum of data subject areas.”(gar, ),
DaaS requires to have a component that deals with the
data representation, data coordination, data manipula-
tion as well as data cleaning.
To understand data and provide more comprehen-
sive insights from the data, it is essential to conduct
data analysis. Data analysis in the context of DaaS
stands for knowledge and pattern discovering from
available data sets by using different approaches and
techniques such as data mining. The acquired knowl-
edge can lead to enrichment of the basic data set, for
instance, data augmentation can be implemented. The
results from data analysis can directly support the de-
cision making.
Data control is responsible for procedures related
to data permissions, which include access, privileges,
ownership, etc. Delivering data demands for data pro-
tection from source to destination as well as its value
protection from misuse. Data protection from the le-
gal point of view will be described in Section 3.4. On
the other hand, the data control in data managements
protects the data during its delivery that is followed by
its usage. Data control imposes the data source rules
and ingestion defined by its owner.
Especially in a Big Data context, the data can be
inconsistent or with represented in different scales.
Therefore, data standardization is to deal with data
inconsistency problem. Different data may repre-
sents different obstacles and difficulties associated to
tasks. Data standardization defines an uniform for-
mat, which facilitates the work associated with such
inconsistent data.
Data governance deals with the permissions and
specific criteria granted by the data owner to their data
A Deployable Data as a Service Architecture for Enterprises
281
DaaS
DaaS Engine
Data Acquisition
Data Regulations
Interface
Registry
Data Management
Data Mapping
Data Control
Data Analysis
Data Aggregation
Service Management
Service Quality SLA Compliance
Request Processing
Data Discovery
Privacy
Data Standardization
Data Governance
Data Quality
Security
Legal
3rd Party
Service Integration
Figure 1: Conceptual architecture of DaaS.
sources. Some of the sensitive data may be prohib-
ited, allowed, or in a specific case provided under de-
termined restrictions and limitations such as partial
censorship. Overall, data governance regulates the
valuable information of data.
Data quality can directly affect the efficiency and
effectiveness of organizations and businesses (Ge and
Lewoniewski, 2020). DaaS is based upon data deliv-
ery that is associated with data risks and threats. To
minimize and eliminate data quality problems, it is
essential to focus on data quality improvement. Data
quality in DaaS provides a qualitative and quantitative
cleaning process for the available data, which leads to
the improvement of the overall data analysis provided
by DaaS.
3.3 DaaS Engine
DaaS engine represents the core component of the
DaaS. It is a system that implements the DaaS poli-
cies and regulates each data flows. This component
in the DaaS is mainly focused on operating efficiently
data flows between the data source and data consumer
that are managed and controlled in relation to the de-
fined policies.
The DaaS engine consists of two fundamental
parts: the DaaS system and the DaaS registry. The
system is responsible for the interoperability - infor-
mation exchange between two end devices. The data
consumer can access the DaaS via an interface. The
interface requests are processed by the DaaS system.
The DaaS registry represents a catalog that includes
meta information about the data, which are used for
incoming requests in the DaaS. The registry contains
information that includes the data relation, location,
accessibility, etc., which facilitate to understand the
data. The DaaS system then provides authoritative
source data to the data consumer in a standardized
structured data that can be understood on a success-
fully processed request. The DaaS engine might be
integrated with other third party services, which en-
rich the feature set of the DaaS. The integration is
controlled and accompanied by the conditions of ser-
vice usage that entail particular responsibilities.
3.4 Data Regulations
Data regulation is responsible for definition of data
protecting policies in the DaaS. This component of
the DaaS is a connecting point between the cloud ser-
vice and the area of authority and legislation, which
IoTBDS 2021 - 6th International Conference on Internet of Things, Big Data and Security
282
influence the data and its processing. By implement-
ing a proper data regulation, the DaaS can minimize
the data privacy and data protection related issues in
the cloud environment such as illegitimate data dis-
semination.
With the rapid development of IT and globaliza-
tion, Big Data introduces new data challenges, and
results in newly defined policy to protect the data and
its further processing in IT. DaaS needs to act in ac-
cordance with the established legal and privacy rights.
The private data stands for every piece of information
that is considered as personal. Data protection rights
have a high effect on how the service is provided by
data providers and how the data consumers use the
subscribed service. Data regulation is responsible for
the formal definition of each use case necessary from
the legal point of view that has to be implemented into
DaaS.
Obligations derived from data protection and data
privacy can significantly influence the DaaS. For ex-
ample, in Europe there are several rule enforcement
that defines these obligations such as GDPR. These
rule enforcements mandate the enterprise to adhere to
these obligations, which process or collect any type of
information related to its subscribers. The obligations
are influencing the data provider as well as to the data
consumer.
3.5 Security
With the widespread presence of cyberattacks and se-
curity breaches, the data security plays a significant
role in IT management (Michener, 2020). Data se-
curity protects data assets from undesired disclosure,
modification, exploitation, and destruction, whether
accidental or intentional. DaaS is particularly vul-
nerable, and may involve sensitive and confidential
data. Securing DaaS system requires to protect the
data flow from source to destination.
DaaS is a cloud service model and most of its
components such as data sources are integrated via
network connection. Thus, it requires different secu-
rity operations, for instance, active data in the tran-
sitions, data in an established communication chan-
nel between endpoints, or persistent data stored in
the cloud data storage. Securing such a complex and
constantly changing environment needs to provision
a continuously innovated scheme in each application
area of such a DaaS system to ensure the security.
Moreover, data fraud is becoming more prevalent.
Enterprises may use different approaches such as wa-
termarking or censorship to protect the sensitive data
to not being exposed, copied, and stolen. However,
it will devalue the expected information. This raises
the issue of trust from both business and customer.
For example, when the data are delivered to users, the
consuming side also needs to have the protection re-
sponsibility.
3.6 Service Management
The increase of data services leads to a growing de-
mand for service management that is to control to
the overall service provision. Enterprise activities that
are performed to manage, control, deliver and operate
data services offered to customers can be managed ac-
cordingly. The service management has a crucial role
for the DaaS provider, as it ensures the customers’ ex-
pectations for the provided service.
as a Service instances are associated with service
delivery, as the name suggests, which delivers value
to the customers/subscribers. Service management
is fundamental because it drives the associated areas
such as service strategy, service goals (including ob-
jectives), service metrics (e.g., key performance indi-
cator), etc. The key role rests in a clear, well-defined
roadmap that will reduce costs and improve the effi-
ciency of the provided service.
The service management in DaaS also includes
service delivery and IT service management. Trans-
formation shift requires firstly to adopt a service de-
livery model, which defines roadmap and establish
service blueprint for data services. It is required to
have a clearly defined goals, strategy, mission and vi-
sion of the data service. Service delivery deals with
the service subscribers respectfully. Furthermore, ser-
vice management also tackles the DaaS operation
management and continuous improvement, which al-
low productive and efficient data delivery service.
4 IMPLEMENTATION
A physical architecture represents a deployable archi-
tecture instance that describes operational character-
istics of the designed concept. In the physical in-
stance, we show a physical architecture in Figure 2.
It is composed of real-world tools and services. This
deployment consists of existing technologies and ser-
vices that are optimized for the desired purpose in this
instance.
The enterprise needs to obtain the requirements
for DaaS provisioning such as cloud platform, cloud
storage, etc. The platform ensures essential features
in the cloud. In our deployment, we have chosen the
Google Cloud Platform as a PaaS provider from the
available alternatives such as Amazon Web Service,
Microsoft Azure, Oracle Cloud, IBM Cloud etc.
A Deployable Data as a Service Architecture for Enterprises
283
Figure 2: Deployable physical architecture of DaaS.
The usage of enterprise service bus (ESB) pro-
vides a efficient method for multiple divergent sys-
tem integration and management. Nowadays man-
ufactures released and announced message-oriented
middleware appliances such as Apache ServiceMix
1
that can realize the ESB communication system. The
ESB is responsible for information exchange in real
time between subsystems of DaaS. The usage ESB
has the advantage to build a loosely coupled system.
Furthermore, DaaS Engine delivers the requested
data to consumers or applications. The engine can be
implemented in various ways, we have chosen Flask
web framework and Gunicorn as our WSGI
2
while
both of them are implemented in a Python language,
which is a general object oriented imperative pro-
gramming language. The Gunicorn implements two
roles - server side (requests handling) and application
side (request delegation). The Gunicorn invokes the
corresponding python callable on the incoming re-
quest. In our deployment, the Flask is the callables
and responsible for processing requests in the data
management. Also, using Flask, there is a possibil-
ity to implement the data-related validation rules and
policies in a custom way. Flask is a framework writ-
ten in Python and supports the object mapping (in our
case, object document mapping) of database items.
The specific rules can be isolated and separated from
the application logic and stored in a persistent data
storage, for instance, in a database - MongoDB.
One of frequently used approaches to integrate
data to other application is via application protocols
such as HTTP or HTTPS. The access to the DaaS is
enabled through a RESTful API, which is based on
OpenAPI standard and implemented via Connexion
that maps each endpoint to the Python callables.
Third-party service integration is to enrich the
1
A framework composed of Apache ActiveMQ,
ApacheCamel, and Apache CXF, and Apache Karaf.
2
Web Service Interface Gateway
provided basic feature set of the DaaS. In most cases,
the external service is added as an encapsulated ser-
vice entity that is managed with respect to the in-
ternal policies and terms of service. The integration
process will connect the specific service by mapping
the service interface to the message-oriented middle-
ware. The integration result is a connected service,
which can exchange data with other components of
the DaaS.
Current cloud storage providers offer a wide range
of storage services such as backup and versioning.
There is a large amount of options on how to im-
plement cloud storage within a DaaS. Regarding the
price pool and available resources, the cloud storage
may be an integrated service provided by a cloud stor-
age provider or a custom cloud storage. For large
scale data volume, using existing cloud storage may
be a cheaper option to the enterprise in contrast to
re-implement the cloud storage. In our example, we
use an already existing cloud storage - Google Cloud
Storage, that is used for storage purposes of the DaaS.
5 CONCLUSIONS
In this paper, we have proposed a Data as a Service ar-
chitecture to guide users to build, migrate and deploy
data management on the cloud. The features of the as
a Service are derived from the IaaS, PaaS and SaaS.
Those features are then applied as a foundational set-
ting for DaaS. The DaaS architecture further deal with
specific data functionalities along with data flow in
the cloud computing. In order to validate the pro-
posed DaaS architecture, we have demonstrated how
to instantiate the DaaS architecture to a deployable
physical architecture. During the implementation, we
have reported the lessons learned from the DaaS im-
plementation. It can be seen that the proposed DaaS
can be applied in real-world deployment and can sig-
nificantly help the enterprises to build their DaaS.
As future works, the proposed implementation can
be further developed and enriched by performance
benchmarks of the deployed instance including dif-
ferent tools and technologies.
REFERENCES
Gartner glossary. https://www.gartner.com/
en/information-technology/glossary/
dmi-data-management-and-integration.
Aleem, S., Ahmed, F., Batool, R., and Khattak, A. (2019).
Empirical investigation of key factors for saas archi-
tecture dimension. IEEE Transactions on Cloud Com-
puting, pages 1–1.
IoTBDS 2021 - 6th International Conference on Internet of Things, Big Data and Security
284
Aljahdali, H., Albatli, A., Garraghan, P., Townend, P., Lau,
L., and Xu, J. (2014). In 8th IEEE International
Symposium on Service Oriented System Engineering,
pages 344–351. IEEE Computer Society.
Bellavista, P., Corradi, A., and Zanni, A. (2017). Integrat-
ing mobile internet of things and cloud computing to-
wards scalability: lessons learned from existing fog
computing architectures and solutions. Int. J. Cloud
Comput., 6(4):393–406.
Bouasker, T., Langar, M., and Robbana, R. (2020). Qos
monitor as a service. Softw. Qual. J., 28(3):1279–
1301.
Duan, Y., Fu, G., Zhou, N., Sun, X., Narendra, N. C., and
Hu, B. (2015). Everything as a service (xaas) on the
cloud: Origins, current and future trends. In 2015
IEEE 8th International Conference on Cloud Comput-
ing, pages 621–628.
Ge, M., Bangui, H., and Buhnova, B. (2018). Big data for
internet of things: A survey. Future Gener. Comput.
Syst., 87:601–614.
Ge, M. and Dohnal, V. (2018). Quality management in big
data. Informatics, 5(2):19.
Ge, M. and Lewoniewski, W. (2020). Developing the qual-
ity model for collaborative open data. Procedia Com-
puter Science, 176:1883–1892.
Khan, F. A., ur Rehman, M., Khalid, A., Ali, M., Imran,
M., Nawaz, M., and Rahman, A. (2019). An intel-
ligent data service framework for heterogeneous data
sources. Journal of Grid Computing, 17(3):577–589.
Li, C., Song, M., Zhang, M., and Luo, Y. (2020). Effec-
tive replica management for improving reliability and
availability in edge-cloud computing environment. J.
Parallel Distributed Comput., 143:107–128.
Liu, G. (2010). Research on independent saas platform. In
2010 2nd IEEE International Conference on Informa-
tion Management and Engineering, pages 110–113.
Michener, J. R. (2020). Security issues with functions as a
service. IT Professional, 22(5):24–31.
Mishra, S. and Kumar, C. (2018). Effort estimation for
service-oriented computing environments. Comput.
Informatics, 37(3):553–580.
Mohammadian, V., Navimipour, N. J., Hosseinzadeh, M.,
and Darwesh, A. (2020). Comprehensive and sys-
tematic study on the fault tolerance architectures
in cloud computing. J. Circuits Syst. Comput.,
29(15):2050240:1–2050240:40.
Moorthy, R. S. and Pabitha, P. (2019). Optimal provisioning
and scheduling of analytics as a service in cloud com-
puting. Trans. Emerg. Telecommun. Technol., 30(9).
Mthunzi, S. N., Benkhelifa, E., Bosakowski, T., Guegan,
C. G., and Barhamgi, M. (2020). Cloud computing se-
curity taxonomy: From an atomistic to a holistic view.
Future Gener. Comput. Syst., 107:620–644.
Narayan, A., Pillai, P. S., Prasad, A. S., and Rao, S. (2017).
Resource procurement, allocation, metering, and pric-
ing in cloud computing. In Research Advances in
Cloud Computing, pages 141–186.
Park, J. H., Younas, M., Arabnia, H. R., and Chilamkurti,
N. K. (2021). Emerging ICT applications and services
- big data, iot, and cloud computing. Int. J. Commun.
Syst., 34(2).
Prasad, A., Green, P. F., and Heales, J. (2014). On gover-
nance structures for the cloud computing services and
assessing their effectiveness. Int. J. Account. Inf. Syst.,
15(4):335–356.
Rajesh, S., Swapna, S., and Reddy, P. (2012). Data as a
service (daas) in cloud computing. Global journal of
computer science and technology.
Rao, K. R. and Nayak, A. (2019). Data residency as a ser-
vice: a secure mechanism for storing data in the cloud.
Int. J. Embed. Syst., 11(4):397–418.
Serhani, M. A. and Dssouli, R. (2010). Case study: Master
of science in service computing (msc sc). In 2010 6th
World Congress on Services, pages 80–83.
Suresh, A. and Varatharajan, R. (2019). Competent re-
source provisioning and distribution techniques for
cloud computing environment. Clust. Comput.,
22(5):11039–11046.
Terzo, O., Ruiu, P., Bucci, E., and Xhafa, F. (2013). Data
as a service (daas) for sharing and processing of large
data collections in the cloud. In Seventh International
Conference on Complex, Intelligent, and Software In-
tensive Systems, pages 475–480.
Weik, M. H. (2001). data source, pages 358–358. Springer
US, Boston, MA.
A Deployable Data as a Service Architecture for Enterprises
285