SERVICE ORIENTED QUALITY REQUIREMENT FRAMEWORK FOR CLOUD COMPUTING

International Journal of Computer Science & Information Technology (IJCSIT) Vol 7, No 6, December 2015
DOI:10.5121/ijcsit.2015.7613 155
SERVICE ORIENTED QUALITY REQUIREMENT
FRAMEWORK FOR CLOUD COMPUTING
Madhushi Rathnaayke1
and Janaka I Wijayanayake2
1
Department of Industrial Management, University of Kelaniya, Sri Lanka
ABSTRACT
This research paper introduces a framework to identify the quality requirements of cloud computing
services. It considered two dominant sub-layers; functional layer and runtime layer against cloud
characteristics. SERVQUAL model attributes and the opinions of the industry experts were used to derive
the quality constructs in cloud computing environment. The framework gives proper identification of cloud
computing service quality expectations of users. The validity of the framework was evaluated by using
questionnaire based survey. Partial least squares-structural equation modelling (PLS-SEM) technique was
used to evaluate the outcome. The research findings shows that the significance of functional layer is
higher than runtime layer and prioritized quality factors of two layers are Service time, Information and
data security, Recoverability, Service Transparency, and Accessibility.
KEYWORDS
Quality of Service, Service Oriented Cloud_ Computing, Functional layer and Runtime layer.
1. INTRODUCTION
Today business context shows that the usability of cloud computing is beyond just an IT function
where it directs strategic business objectives of organizations to achieve competitive advantage.
Since it plays a generic role in business competitiveness, many enterprises keen on using
virtualized IT resources (software, platform, data center as services) [1]. The use of service
oriented cloud technology is beneficial financially as it improves virtualized business
relationships. At the same time the mismatch of quality with dynamic business environment could
possibly discourage the cloud users [2]. To analyse the problem time to time evaluation of
Quality of Services (QoSs) has been worthwhile by appending the concept of Requirement based
Services (RbS) [3].
Enterprise has to identify the need of proper quality requirement of cloud computing to make sure
that the cloud strategy benefits the organizations than conventional way of doing business.
Hoever, there has bee on research done to identify the service quality requirement of coud
computing. This research paper proposed a framework to identify the quality expectations of
endusers towards the dimensions (capabilities/ attributes) of service oriented cloud services, and
proposed prioritized quality requirements in a cloud computing environment.
2. RELAETED WORK
The quality of a Web-based customer support system consist of service it provides, information it
supplies, its effectiveness which reflects the satisfaction of users and characteristics of the system
itself [9]. The framework SERVQUAL is best fitted instrument for measuring Quality of Service

156
(QoS) of uses. Five key indicators that indicate web based QoS factors are namely user
friendliness, reliability, responsiveness, assurance, and user orientation. Further, the quality of
cloud based services can be described in term of specific governance requirements, applicability
of standard, business or application-driven requirements [3].
Reference [5] proposed a framework for Infrastructure level service requirements of cloud users
and has focused on a set of infrastructure level quality attributes such as Service Level Agreement
support, Reusability, Customization, Multi-tenancy, Adaptation, Flexibility, Business alignment
and Technological transparency. Some researchers use service measurement indicators at
different levels of web based services. The best example is the study conducted by [6] where the
quality attributes of virtual web based services are measured based on two levels; namely
Functional and Runtime. Further this model presents taxonomy of Quality of Web (QoW)
parameters to identify the different quality aspects of Web services where these parameters are
measured quantitatively. Further, reference [6] shows the relationship between each quality
parameter in the model and also identified the key features of a Web Service Management System
(WSMS).
Frameworks focused on data management level and interoperability over web services that are
managed by an integrated WSMS are proposed by [7, 8]. In there the term interoperability was
focusing on ontology management for web services, functions like metadata schema with rich
descriptive capacity which facilitate the Information exchange between computer and people.
Further, the framework elaborate the security, privacy, response time, transaction reliability,
accessibility, cost, reputation, regulatory, monitoring, value addition, service level as parameters
to identify the QoWs. The stated quality variables at runtime layer are resource utilization;
reliability, response time, and ease of terminal use, and data accuracy. Ease of use, completeness,
reliability and system flexibility are considered as the measurements standard of system quality
component [4].
Though the current business environment sees the cloud computing resources as a strategic
option, the frustration towards the reliability, QoS may results in reluctance of using cloud
services. Reference [9,10] have clearly stated the issue of loosely interconnected Service Level
Agreements (SLAs) with consumers and the unavailability of data & data processing and loss of
integrity are another risk often faced by cloud computing providers.
The key characteristics of cloud computing based on subjective models and frameworks have
been defined as critical characteristics of service oriented cloud computing. The studies
conducted by [1, 9, 12] have presented cloud attributes, Interoperable service architecture, Cloud
service management, Multi tenancy and Service Level Agreements as critical attributes of cloud
domain. Further, reference [17] stated that the cloud computing model has positive impact on
performance of server infrastructure in increasing the utilization of computing resources.
3. RESEARCH METHOD
Quality of service can be evaluated under different perspectives of cloud stakeholders. This
research study focuses on the perspective of cloud users. Thus the model validity is performed
with the user response. The target respondents are cloud users such as software engineers, IT
professionals, business analysts, ERP consultants etc. The major focus of this research is to
identify the service quality factors that are embracing within the domain of cloud technology and
the SOCC dimension (the attributes of existing driving factors of service based cloud
technology). However, there has been no research done to analyze as to how the existing driving
factors (SOCC dimensions) of cloud services have influenced towards the quality of service.

157
Hence the focus of the research is to find whether there is a relationship between SOCC
dimensions and the quality of cloud service. Therefore, the main hypothesis is constructed as:
• H1: Service Quality requirements and SOCC dimensions has positive relationship
The term “quality of service” can be defined as the difference between expected and perceived
service [13]. They state “the key to ensuring good service quality is meeting or exceeding what
(customers) expect from the service”. Based on the above definition, it is implied that the
requirement of quality is depend on the user expectation and if user meets or exceeds the service
we can use the term “User satisfaction” and also it is derived along the experience that user can
archive with services. The same term is applicable in cloud computing where we can observe how
the existing service factors could pursue quality of service in general usage.
The conceptual model shown in figure 1 depicts the current position of service oriented cloud
computing as a set of variables where it is measured by the service oriented cloud computing
dimensions (SOCC dimensions) as given in Table 1 and measure against the Quality of Services
(QoSs) that pursue the cloud based service quality requirements. Further, the quality of service
factors are measured over two layers: Functional layer and Runtime layer as depicted in figure 1,
where the main construct of quality of service is assumed to be derived from the quality factors at
Functional and Runtime layer.
• Functional layer- refers the functional requirement of the service should be met within the
context of the business unit and the enterprise [1]. The attribute of functional objectives are
elaborated as customer relationship management (CRM), service information and
entitlements, service management, financing and Intra-enterprise services. In this layer
thirteen variables are measured to satisfy the QoS parameters as describe in table 2.
• Runtime layer- refers the functional operations run on cloud computing models (Software as
a Service (SaaS), Platform as a Service (PaaS), Infrastructure as Services (IaaS), and Data
storages). This phase where cloud service providers and users interact by service
provisioning. The measurement of properties that are identified in runtime layer is described
in table 3.
Figure 1. Overview of the Proposed Service oriented quality requirement framework

158
The hypothesis development for the relationship between quality of service construct and the
quality derived factors at Functional and Runtime layer can be described as:
• H2: Functional Layer quality requirements are positively related with service quality
requirements of users
• H3: Runtime Layer quality requirements are positively related with service quality
requirements of users
• SOCC dimensions –The characteristics of service oriented cloud computing that defined by
the US National Institute of Standards and Technology (NIST) implicate the current
technological capabilities of cloud computing. This study uses these attributes as the
dimensions of SOCC namely, On demand self-service, Broad network access, Service
measurement, Shared resource pooling and Rapid elasticity[12]. Cloud service management
is abstracted from the IT Service Management Framework on Cloud Computing Environment
that is proposed by [11]. In this framework it has been identified that service management is
an important factor on cloud environment. The another important factor in web based services
is interoperability which is proposed as a main attribute in framework of Multi- tenancy
architecture proposed in [6] for deploying & managing web service. These two attributes;
service management and interoperability are added to the conceptual model that is not
appeared in the NIST model.
Further, the indicators of each dimension are defined in table 1 and the definitions of seven SOCC
dimensions are described as follow:
Table 1. SOCC dimensions
SOCC Dimensions
Dimension Indicators
Variable
name
1.
Interoperable service
architecture(INT)
Systematic interoperability INT1
Semantic interoperability INT2
2.
Cloud service
management(CSM)
Service provisioning management CPM3
Business and operational support
management
CSM4
3. Service measurement(SM)
Service billing BIL5
Service monitoring SMN6
4. On demand self-service(OND) Service provisioning capability SPR7
5. Shared Resource pooling(SRP)
Multi-tenant model MLT8
Capability of assigning Different
physical and virtual resources
dynamically
RES9
6. Broad network access(BNA)
Access over the networks NET10
Access over client platforms CLP11
7. Rapid elasticity(RE)
Number of versions released V12
Availability at any given time AVI13
• Interoperable service architecture (INT) - The term interoperability explains several
attributes of cloud computing which are systematic interoperability, semantic interoperability
and interoperability over device, platform and legacy systems.

159
• Cloud service management (CSM) – This refers service provisioning management, business
and operational support management, service monitoring, service level agreement support,
evaluating and upgrading.
• Service measurement(SM) – This refers the pay per use or billing procedure based on
service utility.
Table 2. Service quality requirement dimensions
at Functional Layer
QoS (Functional Layer)
Variable
Name
User friendliness
(F_UF) The
physical features of
the system,
appealing and
looks good.
Attractivenes
s of the
application
F_UF1
Consistency F_UF2
Understanda
bility
F_UF3
Reliability(F_REL)
- focusing on
whether the system
is right, useful, and
dependable
Relevance F_REL1
Dependabilit
y
F_REL2
Cost benefit F_REL3
Accuracy F_REL4
Responsiveness(F_
RES) The
readiness of the
service to provide
service
Service time F_RES1
Assurance(F_AS)
- The knowledge
and courtesy
expressed in the
system and its
ability to inspire
trust and
confidence in its
safety
Service
Transparenc
y(SLAs)
F_AS1
Reputation F_AS2
Information
security
F_AS3
Completenes
s
F_AS4
Sufficiency F_AS5
User
orientation(F_UO)
individualized
attention
Customizatio
n of
application
F_UO1
Table 3. Service quality requirement dimensions
at Runtime Layer
QoS (Runtime Layer)
Variable
Name
User
friendlines
s (R_UF)
Throughput R_UF1
Number of
active sessions
(concurrency
level)
R_UF2
Resource
allocation
R_UF3
Redundancy R_UF4
Reliability
(R_REL)
Dependability R_REL1
Recoverability R_REL2
Responsiv
eness(R_R
ES)
Response time R_RES1
Assurance
(R_ASS)
Availability R_ASS1
Accessibility R_ASS2
Data security R_ASS3
Technical
support service
R_ASS4
User
orientation
(R_UO)
Integrity R_UO1

160
• On demand self-service (OND) – This refers the unilaterally provision computing
capabilities, such as server time and network storage, as needed automatically without
requiring human interaction with each service provider.
• Shared Resource pooling (SRP) – This refers 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.
• Broad network access (BNA) – This describes the capabilities that 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).
• Rapid elasticity (RE) – This refers the capabilities available for provisioning unlimited
services in any quantity at any time.
Indicators of each dimension are defined in table 2 and table 3 respectively for Functional layer
and Runtime Layer.
• Qualities of Services (QoSs) are the dependent measurement that encompasses different
quality parameters that characterize the behavior of cloud service in delivering its
functionalities. These parameters are categorized into two major quality layers: Functional
layer and Runtime layer. To derive indicators of each layer, this study uses SERVQUAL
model with five key performance indicators (KPIs) for measuring service quality as
explained in [11]. They are User friendliness, Reliability, Responsiveness, Assurance, and
User orientation.
4. RESULTS AND ANALYSIS
The research uses questionnaire based data collection approach to validate the above identified
indicators. After conducting a pilot survey, the finalized online questionnaire was distributed by
targeting industry wide cloud users and the main variables were measured using 5 degrees likert
scale. 53 responses were collected. The selected sample assures higher level of reliability since
almost all the respondents have used at least one cloud service including Microsoft Azure, IBM,
and Google App Engine etc. While 85% of them are experienced in cloud related services for
more than 2 years, 43 of given 53 respondents have got expertise in software engineering with
81.13% of the sample.
Out of the structural equation modelling (SEM) techniques Partial Least Squares (PLS) is the
well-established technique for estimating path coefficients in structural models and has been
widely used in various research studies [14]. The PLS data analysis was done using the software
package SmartPLS (Version: 2.0.M3).
According to the guidelines, Cronbach Alpha should be above 0.60 for exploratory research and
above 0.70 for confirmatory research [15]. When the composite reliability is a measure of
internal consistency, Cronbach Alpha must not be lower than 0.6. However, according to [16] if
an indicator’s reliability is low, 0.40 for Cronbach Alpha can be considered as the minimum
threshold value and ignoring indicators less than 0.4 is acceptable.

161
Figure 2. PLS values for whole sample
Figure 2 gives an overview of all components for constructs as a second order and first order
hierarchical. In the first step, estimation of the first order constructs for Functional layer quality
requirement and Run time layer quality requirement were conducted against SOCC dimension
layer and latent variable scores were saved.
Latent variables are the measurement of each construct that indicates in square boxes as depicted
in figure 2. Then the obtained latent variable scores were used as reflective indicators. When
come to the second assessment, all variables with least reflects in the constructs should be
removed from the conceptual model. After reconstruction, the validated model is depicted as in
figure 3. In our model the assessment was terminated in two tries as the model met the least
conditions after two tries. Table 4 displays the composite Reliability, Cronbach Alpaha
coefficient and R square value for the validated model.
Table 4. Composite Reliability and Cronbach α coefficient
Composite
Reliability
R Square Cronbach’s Alpha
(CA)
SOCC dimensions 0.7673 0 0.6362
Service Quality requirement 0.8634 0.0538 0.8321
FLQRs 0.8282 0.9146 0.7717
RLQRs 0.7407 0.7877 0.6106

162
Figure 3. PLS values for the validated model
As depicted in Figure 4 the path coefficient of the validated model is proven between 90%
confidence levels. Hence we accepted the hypothesis that has been derived as shown below in
fifure 4: (significance P value <0.01). Since all T values are greater than 1.674, null hypothesis
were rejected and H1, H2, H3 were accepted.
Figure 4. T values and path coefficient of the validated model
The valid indictors of QoS for overall model were prioritized under the functional and Runtime
levels as given in Figure 5 and 6 based on t values. When separately analyzed, each quality
requirements of two layers along with the SOCC dimensions, the given t values for functional and
runtime are 2.819, 1.801 respectively. Thereby the outcome is cleared that the functional quality
requirements are considered to be more important than runtime quality requirements.

163
Figure 5. FLQRs Prioritization
5. CONCLUSION
The objective of the research is to measure the relationship between Service Oriented Cloud
Computing dimensions and Quality of Services (QoSs) that are perceived by users. A conceptual
model was developed and hierarchical PLS path modeling technique was used to validate the
model. In the validated model QoS attributes are prioritized based on t values. Model shows that
the highest priority attributes are service time and information security for functional layer and
data security and recoverability are best prioritized attributes in runtime layer. Further analysis
shows that relationships between attributes of functional and runtime layer have higher
correlation against SOCC dimensions and among them QoS attributes at functional layer shows
the highest correlation. Thereby it can be stated that cloud users are more concern of service
quality at functional layer than runtime layer.
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Figure 6. RLQRs Prioritization

164
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AUTHORS
Ms. Madhushi Rathnayake completed her BSc. in Management and Information
Technology (Special) degree with a second class honours from University of Kelaniya.
Currently she is working as a assistant lecture at the department of Industial Management,
University of Kelaniya. Her research interest is in the area of management infoamtion
systems.
Janaka I Wijayanayake received a PhD in Management Information Systems from Tokyo
Institute of Technology Japan in 2001. He holds a Bachelor’s degree in Industrial
Management from the University of Kelaniya, Sri Lanka and Master’s degree in Industrial
Engineering and Management from Tokyo Institute of Technology, Japan. He is currently
a Senior Lecture in Information Technology at the department of Industrial Management,
University of Kelaniya Sri Lanka. His research findings are published in prestigious
journals such as Journal of Information & Management, Journal of Advances in Database, Journal of
Business Continuity & Emergency Planning and many other journals and international conferences.

SERVICE ORIENTED QUALITY REQUIREMENT FRAMEWORK FOR CLOUD COMPUTING

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