SERVICE LEVEL AGREEMENT PARAMETERS FOR
ECOMMERCE CLOUD
AbdelSalam H. Busalim1 Ab Razak Che Hussin 1
1
Universiti Teknologi Malaysia, Faculty of Computing, Skudai, Jorhor Bahru, Malaysia (www.utm.my)
ABSTRACT
Nowadays Services Level Agreement (SLA) became an important aspect between the cloud consumer and
cloud provider, the dynamic nature of cloud computing needs to continue monitoring of the services. The
restricted choice of appropriate parameters in SLA affects the interacting of end user with cloud services
and creates risks of user data. End users are concerned about their data and how it will be stored in cloud
and how the data is recovered in the case of failure of disaster. However, none of SLAs consider the end
user view while conducting the SLA document. This paper discusses the importance of the parameters
which need to be included in SLA when ecommerce companies move to cloud services, In order to verify
and validate these parameters in ecommerce cloud, data were collected from105 respondents in faculty of
computing in Universiti Teknologi Malaysia. The results indicate that most of the selected parameters are
significant from the end users point of view.
Keywords: SLA, Cloud Computing, ecommerce cloud
1. INTRODUCTION
Cloud technology is becoming an
increasingly popular trend. As a new concept, cloud
computing offer online software and hardware
capabilities. It offers a secure online environment
for data storage and various e-commerce services
that maintain the latest technologies and continuous
protections for clients. Many ecommerce companies
are turning to cloud computing and services due to
the cost-efficiency it offers. According to the
Gartner group report, by 2013, 40 % of e-commerce
companies will use a complete cloud services (SaaS)
solution. It appears that cloud computing is set to
change the ways in which businesses operate [1].
Form the business perspective, cloud computing
offers a great number of benefits including reduced
infrastructure and maintenance costs.
Many e-commerce companies and retailers have
moved to cloud services due to the many benefits
this shift offers in regards to cost effective and
easily accessible storage options, and elastic
computing and infrastructure. In 2012, the Gomez
application performance monitor report clearly
showed that among the 50 well known online
retailers,
40%
use
Amazon
cloud-based
applications. However, there a number of risks
associated with this reliance on outside services,
especially from the perspective of end users. For ecommerce cloud companies, the end user is an
important capital; therefore, satisfaction of the end
user is a major goal of e-commerce cloud service
providers. Among the risks the end user potentially
faces when engaging in cloud-based e-commerce, is
the issue of confidentiality of data, how data is
stored, and who has access to this data. Another
problem concerns privacy. The dynamic cloud
environment makes it difficult for cloud providers to
observe current data protection regulations and
privacy policies [2]. The majority of current cloud
contracts are little concerned with consumer privacy
and consumers are often poorly informed about
privacy issues [3].
One method of ensuring that the client receives
the correct services best suited to its needs is the
SLA, which has transformed from being a financial
contract into a managerial tool to ensure the
expectations of the customer are met [4]. For this
mechanism to be effective there is a need for clear
definitions of services, and suitable measuring
parameters to measure the level of services. In the
cloud computing environment, computer resources
and infrastructures that are offered are scalable, and
the platform, software and infrastructure provided in
the form of services can be accessed anytime and
anywhere. However, provisioning this paradigm of
cloud services requires specific SLAs for every type
of services. This can be rather challenging in view
of the diverse range of services offered by cloud
computing services, the various levels of each
service, and the diverse needs of clients. No two
clients are identical.
This paper organized as follow. Section 2
highlights the literature review, Section 3 explains
the methodology adopted by this paper, Section 4
will cover the results and discussion of the paper,
then conclusion of this paper will be in Section 5.
2.
LITERATURE REVIEW
A. Risks and Challenges in E-Commerce Cloud
In ecommerce cloud sellers ,a large number
of users information is stored in the cloud ,and
transmission and processing taking place in cloud,
therefore, the problems and risks more than
traditional e-commerce model [5]. Increasingly,
online retailers are relying on cloud services and
applications such as storage, computing, comparison
engines, product locators and dynamic imaging to
run their businesses. With this relying on outside
services high risks raised out . As a result, they are
losing control of the end user experience.
The confidentiality of the user’s data is one
of the main risks facing ecommerce cloud. In ecommerce cloud, most of the business information
is stored in the cloud therefore, e-commerce
companies are not able to supervise and monitor
user's sensitive information. As virtualization
technology been used in cloud computing, ecommerce companies using cloud services are not
clear about where the data been stored, and do not
even know the physical location of that data.[5]. The
European Network and Information Security
Agency (ENISA), conducted a survey for the main
cloud computing security issues. More than 70 % of
the SME (small and medium enterprises) in this
study are concerned by the first six criteria and more
specifically by confidentiality of data. Privacy are
another important issue in Cloud Computing. The
dynamic nature and structure of Cloud environment,
make it difficult for Cloud providers to follow the
current data privacy and protection rules. The main
reasons beyond this, the transnational nature of
Cloud Computing that has to face the national
nature regulation privacy[2].
Transparency towards cloud services users must be
the golden rule. Users do not know where the data
stored and by whom it will be finally processed. It
is compulsory for Cloud services providers to
inform the users about the way in which data are
processed . In the case of e-commerce Companies
concern about the end user personal data, these
important and private data may contain names,
addresses, e-mail addresses and credit card numbers
[2]. As known, that virtualization is one of the
backbone of cloud computing technology, therefore
there are some security risks in sharing machines for
instance, losing control over data location, and who
has the right to access to user data. [6]
B. Services Level Agreement (SLA)
To achieve high quality and performance
goals in services or products, it may need the
enterprise to establish and manage service level
agreement (SLA) between the enterprise which
provides the business service or product and the
consumers, companies are responsible for its
shareholders, expectations of the level of service to
be offered [7]. The main goal of establishing SLA
process is to improve the Quality of Experience of
the service or product to the enterprise customer and
reach the satisfaction level .
However Service level Agreement (SLA) is a
document describes the level of service expected by
a customer from a services provider, based on
metrics or policies by which that services are
measured, and penalties if any, should the agreedupon levels not be achieved. Usually, SLA is
between companies and external suppliers[8].A
service level agreement can be an extremely
effective communication tool for creating a common
understanding between two parties regarding
services , expectation , responsibilities and priorities
[9]. Cloud technology as new paradigm of
computing where available computing resources are
delivered as a service. These resources are generally
offered under the concept of pay-as-you-use,
therefore cloud services become attractive to cost
conscious customers. In order for Cloud providers to
provide customers with services that meet their
demands, both sides need to negotiate the client's
requirements
and
the provider's
services
capabilities, and then they
agree to certain
conditions and terms which is the services level
agreement [10]. The figure below shows the SLA
parties in cloud environment.
Figure 1: SLA parties in cloud environment
.
The main purpose of SLAs is to set a parameters and finally he proposed a monitoring
framework for the providing services and for the framework for compliance checking.[14] proposed
cooperation among service providers and service a framework to minimize the issues of
consumers[2]. However the concept of "everything- trustworthiness among the cloud service provider
as- services" offered by in cloud manner, this and cloud consumer, by using a quantitative model
paradigm has made the establishment of SLA more of trust. They identified and formalized several
challenging and makes the relation among the cloud parameters which has derived from SLA, these
providers and users getting more complex.
parameters to estimate trust. In this framework, the
identified set of parameters have categorized into
two sets. The first set of parameters obtained from
C. Services Level Agreement Parameters
the SLA description and named as Pre-SAL
The SLA in cloud computing It consists of a parameters. These parameters are obtained in trust
set of measurable attributes called SLA parameters estimation before signing the SLA contract, which
which are established by some objectively can to help to build the consumer’s initial trust on
measurable conditions, termed as Service Level the cloud provider. However, most of cloud service
Objectives (SLOs)[11]. The parameters used to providers focus only on small set of parameters,
measure and manage performance compliance to namely Availability, request completion rate and
SLA commitments are the key of successful response time.[15] conducted a study to break
agreements and are a critical long term success down the Cloud SLA into easy and understandable
factor[12]. There are examples include the components and compare the SLAs of the
parameters of throughput and timing, also the considered public cloud provider. By comparing the
percentage of availability of virtual machines and SLA of Amazon, Rackspace, Microsoft, Terremark
other resources, these objectives can be written in vCloud Express and Storm on demand, the study
the SLA in the form below: [13].
highlighted that none of those providers offer nay
performance
guarantee
for
the
services
 Availability of a service X is 99.5%
nevertheless, none of the providers automatically
credit the consumer for SLA violation, consumer
 Response time of a database query Q is
should detect the SLA violation. The problems and
between 3 to 5 seconds
unfulfilled expectations during accomplishing the
 Throughput of a server S at peak load time SLA are the result of , poor choice of
parameters[12].
is 0.875.
There are several many studies have been
conducted related to SLA in cloud computing
environment. [11] pointed out that the SLAs
provided by the presented cloud providers are
relatively biased towards cloud providers and do
not provide any formal method of verifying if the
guarantees are complying or not, therefore the
author attempted to identify the SLA parameters
for Storage-as-a-Service in cloud delivery model
and also the objectives for measuring these
As we mentioned in our previous paper [16]
that In ecommerce cloud, to alleviate the risks and
challenges facing the end user during using
ecommerce cloud websites, suitable parameters
need to be included in SLA to consider the end user
perspective. The table below describes the extracted
parameters for E-commerce cloud SLA, which can
be used for Managing and monitoring the Quality of
services delivered by cloud providers.
Table 1 : Deriving Cloud SLA Parameters
Parameters
Description
Citations
Availability
The uptime of the services for
the user in specific time
[15, 17] [18]
[19] [11] [20]
[21]
Scalability
Ability to increase and
decrease the storage space
[21] [19] [18]
Portability
The services working on
different devices or different
platforms
[21] [19] [18]
Performance
The duration of time to
respond on user's requests
[17] [20] [19]
[18] [15] [14]
Security
The security of user data and
the safety of the environment
in the cloud
[17] [19] [18]
Reliability
Services ability to operate
over the time without failure
[21] [19] [18]
Usability
The ability of the service to be
attractive ,understandable,
learnable, operable
[19] [21] [18]
Backup &
Recovery
How the Service store the
image of user data and the
ability to recover data in
disaster.
[17] [18] [15]
[14]
Availability zones in which the
data are stored
[18]
Data location
3. METHODOLOGY
This section basically illustrates the activities
for every phase in the research, which should be
carried out for this paper. There are four phase in
the research methodology which are illustrated in
the figure 2 below. From the literature review phase
we have found out some potential limitations in the
SLA parameters and frameworks provided by cloud
services providers, especially in the context of the
e-commerce cloud. The current SLAs mentioned
only the availability of the service and the
performance level of the services, in e-commerce
environment most of the companies have moved to
cloud service, however the ecommerce end user in
cloud computing environment facing risks during
communicating with ecommerce cloud websites
and, the SLA between the cloud provider and
ecommerce company does not consider these risks,
and the user should be aware for this SLA. After
identifying the main SLA parameters which
is based on the risk and challenging in ecommerce
cloud, a questionnaire is constructed to verify the
end user perceptions regarding the importance of
these parameters. This research is using quantitative
method for collecting data. The questionnaire
consist of two parts, the first part is the
demographic part and part B is questions which
related to parameters. Content validity process has
been
conducted
before
distributing
the
questionnaire, by asking two experts in faculty of
computing in Universiti Teknologi Malaysia
(UTM), to make sure that the questionnaire is well
organized and the questions is easy to understand.
The final modified questionnaire has been
distributed online using Survey Monkey tool. The
sample size of the respondents after distributing the
final questionnaire is 105 students, and the time for
collecting the data was around two weeks The
respondents are the postgraduate and undergraduate
students of computing faculty in UTM.
Figure2: Research framework
Table 2 shows the structure of part B in the final
questionnaire distributed. The structure of this part
of the questionnaire represent the parameters which
extracted from literature review. The first column
shows the parameters of SLA, the second column
shows the questions of each parameters based on
the objectives related to that parameter, and the last
column represent the ID of each questions to make
the process of analyzing easy and achievable. The
aim this part of the questionnaire is to know the end
user point view regarding the proposed parameters
and how these parameters can reduce the risks in
ecommerce cloud Therefore, to validate the
collected data form questionnaire, SPSS statistical
analysis software has been used in this research.
Table 2. Structure of the questionnaire
4. RESULTS AND DISCUSSION
A. Demographic Analysis
Most of the respondents are male, 80
students out of 105 which represent 76% of the
respondents where the rest of the respondent are
female with approximately 24% as it can been seen
in figure 3.
Figure 3. Information on respondent's gender
Figure 4 shows the level of education of the
respondents, the highest number of the respondents
are Masters students which represent 69% of the
total respondents , where 18% of them are bachelor
degree students and 10% are Phd students.
While the rest are 1.9% are new undergrad
students. As mentioned earlier the scope of the
respondents was limited in student of faculty of
computing, in order to make sure the answers are
accurate regarding the parameters which can be
used in ecommerce cloud environment.
Figure 4: Education level of the respondents
Figure 5 explains the information of
respondent about how frequently they buy online.
Based on the graph below, the major number of the
respondent is buying once a month which represent
33.3%. The second is 28% which buy online once
in every year and 22.8% represent the frequency of
buying once in sex month, while 11.4% of the
respondents buy once in every week. The less
number represent 3.8% who are buy every day.
Figure 5: Frequency of buying online
Another results shows the experience of the
respondents about cloud computing applications.
The major number of the respondents (54 students)
using cloud applications between 2 to 4 years
which represent 51.4% from the whole respondents.
While 33.3% of the respondents using cloud
from less than one year and the rest of the
respondents using cloud from 5 to 7 years which
represent 14.2%. From the figure above, it can be
pointed that majority of the respondents know very
well the concept of the cloud computing. Figure 6
illustrates these results.
Figure 6: Respondents experience in using cloud
B. Descriptive statistics
Cronbach's alpha method used to measure
the internal consistency, which means how closely
the item are related to each other as group . In
Cronbach's alpha the reliability should be more than
.70 in order to consider the items are. The result of
Cronbach's alpha reliability analyze is .961 as
shown the table below.
Table 3: Reliability Statistics for Questionnaire
Cronbach's
Cronbach's
Alpha
Alpha Based on
N of Items
Standardized
Items
.961
.965
31
The tables below shows the Descriptive
statistics Analysis for the parameters The tables
show the Descriptive statistics for each variables in
the questionnaire as listed in table 1. In part B of
the questionnaire the weight is (1.Strongly
Disagree, 2Disagree, 3 Neutral, 4 Agree, and
5Strongly Agree).
Table 5. Descriptive Statistics
Variables
N
Mean
Std.
Deviation
SEC1
SEC2
SEC3
SEC4
SEC5
SEC6
SEC7
PRF1
PRF2
PRF3
PRF4
PRF5
PRF6
USE1
USE2
USE3
88
84
87
86
87
85
87
88
87
85
87
85
88
88
86
88
4.34
4.58
4.54
4.36
4.52
4.49
4.45
4.02
4.30
4.18
4.07
4.25
4.20
4.38
4.33
4.33
.815
.779
.775
.825
.819
.854
.743
.934
.794
.833
.832
.844
.833
.778
.913
.827
Table 6. Descriptive Statistics
Variables
N
AVL1
AVL2
DLC1
DLC2
BKRC1
BKRC2
BKRC3
BKRC4
RBY1
RBY2
RBY3
SBLY1
SBLY2
PROT1
PROT2
88
87
85
85
85
85
85
84
85
85
85
85
84
85
83
Mean
Std.
Deviation
4.43
4.45
3.33
3.61
4.28
4.22
4.27
4.18
4.19
3.51
4.08
4.13
4.31
4.41
4.40
.841
.789
1.189
1.114
.781
.905
.892
.946
.764
.840
.848
.753
.744
.678
.748
According to Table 5 and 6 most of the items
have higher means which means all the items are
important and significant from the end user point of
view, we considered the items who have higher
means as most important for user perspective.
C. Factor Analysis
Factor analysis is data reduction technique,
which used to structure large number of items in
the questionnaire. Factor Analysis also used to
minimise the redundancy between items and to
group the items based on the inter correlation
between these items. Since this study to identify
the main important parameters for SLA cloud
commerce, factor analysis has been used to remove
the inappropriate items which are not suitable or
not belong to any parameters.
The first output of factor analysis showed in
table 7 is a Kaiser-Meyer-Olkin measure of
sampling adequacy and Bartlett‟s Test. In KMO and
Bartlett's Test the value should be greater than 0.5 in
order to consider the sample is adequate and suitable
for applying factor analysis. According to the table
above the value of KMO test for this study is .732
which greater than 0.5
meaning
that ,the
questionnaire sample is suitable to conducting
factor analysis.
Table 7 : KMO and Bartlett's Test Analysis
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Approx. Chi-Square
Bartlett's Test of Sphericity
.732
2149.043
df
465
Sig.
.000
The last output of the factor analysis is
Communalities Analysis. Table 8 and 9 below
explain the communalities before and after
extraction which highlight shows how much of the
variance for the variables accounted for by the
extracted factors. For example over 80% of variance
in Parameter NO.2 (security2) is accounted while
50% of the variance in parameters No.27 (reliability
3) is accounted. From the factor analysis results we
can highlight that all the parameters and their
objectives are significant and recognized are
important from the end users perspective, which
may improve the quality of the SLA between the
cloud providers and ecommerce seller by
considering the end user perspective during
conducting the negotiations process between the two
parties. Considering end use perspective toward
cloud services in the SLA can increase their trust
and also the reputation of the ecommerce seller well
be increased.
Table 8. Communalities Analysis
Item
Initial
Extraction
SEC1
1.000
.711
SEC2
1.000
.804
SEC3
1.000
.823
SEC4
1.000
.715
SEC5
1.000
.808
SEC6
1.000
.808
SEC7
1.000
.676
PRF1
1.000
.697
PRF2
1.000
.785
PRF3
1.000
.811
PRF4
1.000
.632
PRF5
1.000
.756
PRF6
1.000
.692
USE1
1.000
.750
USE2
1.000
.809
USE3
1.000
.725
Table 9. Communalities Analysis
Item
Initial
Extraction
AVL1
1.000
.694
AVL2
1.000
.704
DLC1
1.000
.782
DLC2
1.000
.770
BKRC1
1.000
.653
BKRC2
1.000
.797
BKRC3
1.000
.820
BKRC4
1.000
.787
RBY1
1.000
.607
RBY2
1.000
.492
RBY3
1.000
.567
SBLY1
1.000
.759
SBLY2
1.000
.719
PROT1
1.000
.782
PROT2
1.000
.671
5. CONCLUSION
Service Level Agreement has significant impact on
ecommerce websites which using cloud services,
SLA can guarantee the services provided to the
ecommerce websites and protecting ecommerce
user by applying appropriate parameters. This paper
introduced ecommerce cloud SLA parameters
which consider the ecommerce cloud end users,
these parameters has been selected based on the
risks facing the user during interacting with
ecommerce cloud, the parameters validated by
conducting a survey inside Faculty of computing in
UTM. The results indicates that all the parameters
are important from end user point of view.
However this research included only the student in
faculty of computing as respondents . Futures work
should cover wider variety of respondents
6.
7. Group, T.O., SLA Management Handbook. Vol.
4. 2004, UK: The Open Group. 137.
8.
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