Towards a Fuzzy-oriented Framework for Service Selection in Cloud
Azubuike Ezenwoke
, Olawande Daramola
and Matthew Adigun
Covenant University, Ota, Nigeria
Center for Excellence for mobile e-services, University of Zululand, South Africa
Keywords: Cloud Service Selection, e-Marketplace, Constraint Satisfaction, Fuzzy Decision Making, Fuzzy AHP,
Abstract: The growing popularity of cloud services requires service selection platforms that offer enhanced user
experience in terms of handling complex user requirements, elicitation of quality of service (QoS)
requirements, and presentation of search results to aid decision making. So far, none of the existing cloud
service selection approaches has provided a framework that wholly possesses these attributes. In this paper,
we proposed a fuzzy-oriented framework that could facilitate enhanced user experience in cloud e-
marketplaces through formal composition of atomic services to satisfy complex user requirements,
elicitation and processing of subjective user QoS requirements, and presentation of search results in a
visually intuitive way that aids users’ decision making. To do this, an integration of key concepts such as
constrained-based reasoning on feature models, fuzzy pairwise comparison of QoS attributes, fuzzy decision
making, and information visualization have been used. The applicability of the framework is illustrated with
an example of Customer Relationship Management as a Service.
The growing popularity of cloud services requires
service selection platforms that enable the
composition of atomic services to satisfy complex
user requirements, and highlight the quality of
services (QoS) attributes of these value-added
services under one e-marketplace structure (Akolkar
et al., 2012; Gatzioura et al., 2012). Despite their
successes, commercial cloud e-marketplaces (e.g.
AppExchange and SaaSMax) do not yet enable
dynamic composition, and employ keyword-based
search mechanisms that do not consider user’s QoS
requirements, nor support the elicitation of these
requirements in ways akin to subjective human
expressions. In addition, search results on these
platforms are presented as unordered lists of icons,
with little or no comparison apparatus that simplifies
decision making. Existing cloud selection
approaches (see Figure 1) do not currently provide
the sophistication to optimize user experience in the
e-marketplace (Akolkar et al., 2012).
For example, some approaches only allow users
to make selection from a predefined list of atomic
services, which does not address complex situations
where a user’s requirements extend beyond what
atomic services can provide (e.g. Esposito et al.,
2015). Additionally, some other methods lack the
flexibility to accommodate subjective QoS inputs,
and demand that a user specifies requirements in
exact terms, e.g. (Wittern et al., 2012; Rehman et al.,
Qu and Buyya (2014) observed that user’s QoS
requirements can indeed be specified in terms of
preferences (user’s priority for each QoS dimension)
and aspiration (user’s values for QoS dimension) as
two important considerations for determining which
cloud services to select. However, some existing
approaches that have considered subjectivity in user
requirements elicit either QoS preferences or QoS
aspirations alone from the user but rarely both (e.g.
Esposito et al., 2015; Yu and Zhang, 2014). Still,
some others (e.g. Esposito et al., 2015; Mirmotalebi
et al., 2012; Qu and Buyya, 2014; Rehman et al.,
2011) require users to assign priority weights to QoS
attributes, with the downside of being less accurate
compared to pairwise comparison of the relative
importance of QoS attributes (Millet, 1997).
Again, many approaches (e.g. Esposito et al.,
2015; Yu and Zhang, 2014; Qu and Buyya, 2014;
Ezenwoke, A., Daramola, O. and Adigun, M.
Towards a Fuzzy-oriented Framework for Service Selection in Cloud e-Marketplaces.
DOI: 10.5220/0006365306320637
In Proceedings of the 7th International Conference on Cloud Computing and Services Science (CLOSER 2017), pages 604-609
ISBN: 978-989-758-243-1
Copyright © 2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Wittern et al., 2012) present search results in a
textual list or table format, that do not make obvious
the trade-off among search results, which makes it
more cognitively demanding for users to make
decisions based on search results (Beets and
Wesson, 2011). Ultimately, these identified
limitations will hamper user experience in the cloud
service e-marketplace.
Figure 1: Summary of Related works.
Hence, in this paper, we present an integrated
framework for cloud service selection that supports
fuzzy-oriented decision making, and formal
composition of atomic services in response to
complex user requirements. This is proposed as an
approach to cloud service selection that caters for
observed limitations in existing cloud service
selection approaches. To do this, we employed an
integration of relevant concepts such as: 1) feature
modelling - to organize atomic services within the
cloud ecosystem; 2) constraint-based reasoning - to
guide formal service composition on the fly; 3)
Fuzzy-based prioritization and analysis methods – to
handle subjective user QoS preferences and
aspiration; and 4) information visualization – to
enable easy comparison of query results along
multiple QoS dimensions. Our framework is
proposed as an improvement to existing cloud
service selection approaches.
The rest of this paper is structured as follows:
Section 2 provides some background and Section 3
contains related works. Our framework is presented
in Section 4, while its applicability is presented in
Section 5. Section 6 discusses the implication of our
framework and Section 7 contains conclusion and
future work.
Cloud Services Ecosystem: A cloud services
ecosystem is an environment that host
heterogeneous cloud service offerings from different
providers, and affords the opportunity of
collaborations. A cloud service ecosystem is
analogous to a software product line (SPL).
Automated Reasoning on Feature Models:
The cornerstones of SPL endeavours are: a
knowledge model (e.g. feature model) that captures
the relationships among the components based on
variabilities, and computer-aided reasoning to derive
useful information from the model.
A feature model (FM) is a hierarchically
arranged collection of features and consists of the
inter-relationships between a parent feature and its
child features, and a set of cross–tree constraints that
define the criteria for feature inclusion or exclusion
(Berger et al., 2014). There are 3 types of FM: basic,
cardinality-based and extended feature models
(EFM) (Benavides et al., 2010). We adopted EFM
because it allows the modelling of cloud services,
their QoS attributes and relationship constraints
more naturally.
Automated reasoning is performed by mapping
the FM into logic-based encodings (e.g. description
logic, propositional logic, and constraint
programming), and encodings are inputted into
solvers to find valid compositions of atomic services
(Benavides et al., 2010). The overall QoS attributes
of the valid combinations is determined by the QoS
factors of constituent services, and are computed
using QoS aggregation functions. Types and
application of QoS aggregation functions can be
found in (Mohabbati et al., 2011).
Fuzzy Set Theory: Fuzzy set theory is effective
to capture vagueness that characterizes user QoS
requirements (Qu and Buyya, 2014; Zadeh, 1974).
Each QoS attribute can be represented as a linguistic
variable and users can express QoS preferences and
aspiration using linguistic terms. In this paper,
preference weights were derived using fuzzy
pairwise comparison in Fuzzy-AHP; while QoS
aspirations were elicited and analysed as a system of
fuzzy goals and constraints (Bellman and Zadeh,
A number of approaches for selecting cloud services
exist in the literature (see Figure 1). The approach in
Towards a Fuzzy-oriented Framework for Service Selection in Cloud e-Marketplaces
(Esposito et al., 2015) uses fuzzy sets theory to
handle uncertainty in users’ preferences and a
TOPSIS-based method to rank services. Yu and
Zhang (2014) proposed a SaaS selection model for
group users, by eliciting vague QoS preferences
using interval numbers. The approach of Qu and
Buyya (2014) employs a hierarchical fuzzy
inference system for cloud service selection, while
Kwon and Seo (2013) described an IaaS selection
model based on Fuzzy-AHP. Wittern et al. (2012)
presented an approach to harness cloud service
capabilities using variability model and likely
alternatives are subjected to a preference-based
ranking process. Mirmotalebi et al. (2012) proposed
an approach for ranking cloud services based on
both explicit and implicit user preferences. Rehman
et al. (2011) proposed two methods for service
selection based on similarity of users’ requirements
and service’s properties.
From the summary of relevant previous efforts
shown in Figure 1, all approaches, except Wittern et
al. (2012), cannot compose atomic services to meet
complex user requirements. Furthermore, only Qu
and Buyya (2014) elicits both subjective QoS
preferences and aspirations, while only Kwon and
Seo (2012) incorporates some type of visualization
to aid service selection. In contrast, our proposed
framework will enable composition of atomic
services, elicitation of subjective QoS requirements
and result visualization to aid selection in order to
foster improved user experience during service
selection in cloud e-marketplaces.
This paper proposes a fuzzy-oriented framework
(see Figure 2) for selecting cloud services in cloud e-
Figure 2: Proposed framework.
The framework comprises four modules namely:
Cloud ecosystem and service directory, GUI &
visualization, QoS requirement processing, and
Service evaluation & QoS ranking.
In step 0, the atomic services are combined to
realize the set of composite services offered in the e-
marketplace. Subjective QoS requirements are then
provided (step1), processed (step 2), optimized (step
3), and used to rank services in the directory (step
4). The ranked results are shown to the users via
bubble graph visualization (step 5). We shall discuss
each module in details subsequently.
4.1 Cloud Ecosystem and Service
The framework uses the extended feature model
notations (Benavides et al., 2010), to model the
cloud ecosystem feature model (CEFM). The CEFM
is mapped as a constraint satisfaction problem, and
the Choco-based reasoning engine (www.choco- reasons with a Depth-First search
algorithm to derive all valid mappings. Possible
combinations of atomic services that can be
generated from the pool of atomic services are made
available in the e-marketplace based on former
composition approaches (Akolkar et al., 2012).
4.2 GUI and Visualization
The framework integrates fuzzy-enabled web-based
widgets comprising sliders, drop-down menus and
textboxes for eliciting vague preferences and
aspirations, while bubble graph visualization is
employed to improve understanding of the
relationship among the ranked services.
Users can indicate preferences by pairwise
comparison for each QoS attribute by adjusting the
slider handle (see figure 3). The slider bar has two
colour codes that corresponds to the QoS attributes,
and indicates the level of preference for a QoS
attribute; the lengthier colour means user prefers a
QoS attributes more than the other. The positions of
the slider handle are underlined by fuzzy numbers,
from the fuzzified Saaty scale.
Since humans derive better insight from a picture
faster than mere text, the use of information
visualization to aid service selection improves user
experience (Spence, 2014). The use of bubble graph
visualization improves the understanding of how
each service in the ranking relates with others (see
Figure 4). Each service is represented as a bubble
(shape), using colours, sizes and x-y coordinates to
show services in the QoS information space. These
CLOSER 2017 - 7th International Conference on Cloud Computing and Services Science
dimensions (colours, size and x- and y coordinates)
represents four QoS attributes (e.g. availability, cost,
response time, security reliability etc.)
simultaneously and shows the relationship among
the top ranked cloud services; thereby, enabling
effective comparison.
4.3 QoS Requirements Processing
QoS Preference Prioritizer (QPP): The QPP
module ensures consistency in the pairwise
judgment and uses the geometric mean method to
derive priority weights. Defining comparison ratios
as fuzzy numbers are a better way to capture user’s
claim about the relative importance of criteria. To
prioritize user’s QoS preferences, the QPP employs
Fuzzy AHP-based approach. In FAHP, exact
comparison ratio a
is represented as a fuzzy
number, , based on 9 fuzzy linguistic terms
described in the fuzzified Saaty’s scale (Buckley,
1985). The user performs pairwise comparison for
all QoS criteria, which fills the fuzzy comparison
matrix. For example, a user’s degree of importance
of security criterion over availability can be
expressed by the fuzzy number “about strongly
important” . The corresponding
reciprocal from on the fuzzy comparison matrix
becomes . The fuzzy priority vector,
, is obtained by applying the geometric mean
prioritization method (Buckley, 1985).
QoS Aspiration Analyser (QAA): The QAA
module synthetizes user’s QoS values based on
fuzzy decision making, comprising membership
functions framed as fuzzy goal and constraints
(Bellman and Zadeh, 1970). Since the linguistic
terminologies describing the QoS aspiration reflect
the semantic approximations of user’s intent,
resolving the fuzzy decision results in optimal set of
QoS values that approximate user’s QoS intent.
Table 1 shows sample linguistic goals and
constraints for availability QoS attribute.
Table 1: QoS goals and constraints for Availability QoS.
QoS Goals
Linguistic QoS
Substantial greater than x
In the vicinity of x
About x
Very Close to
Examples of a fuzzy goal and constraints are “The
Availability of the service should be High”, and
Availability should be About x”; where x is a
specific value as indicated by the user.
4.4 Service Evaluation and QoS
QoS Requirements Optimizer (QRP): The QRP
component computes the optimal QoS values that
describe user’s requirements based on the QoS
information of all the services in the service
directory. The inputs into this component are the
priority weights and the value for QoS attributes.
The framework defines two utility functions: a
Simple Addictive Weighting (SAW) function (1)
and exponential Euclidean distance metric (eEUD)
(2), to evaluate the performance of each service
alternative w.r.t user requirements. SAW is used to
determine the QoS properties of the alternative with
the highest utility, while eEUD computes the QoS
properties closest to users’ requirements.
= j
QoS value of i
service; w
= j
QoS weight.
and y
are the values of the j
QoS properties of i
cloud service and user requirements respectively.
The results from the two functions are used to
construct the optimal QoS requirements.
QoS Ranking Engine: The output from the QRP
forms the basis for ranking the services in the
directory. The main technique used in this module is
a nearest neighbour algorithm, based on (2). The
output is the top-k services fed into the bubble graph
A scenario of a customer relationship management
as a service (CRMaaS) ecosystem and e-marketplace
was reported in (Ezenwoke, 2016). The CRMaaS is
made up of 5 modules (contact manager, database,
marketing, social media analytics and cloud
platform) and 14 atomic services to fulfil the
modules. The modules and atomic services were
modelled using extended feature modelling notations
and a constraint-based reasoning engine is used to
derive a set of 38 valid compositions based on the
constraints guiding the relationship of the modules
and atomic services.
Based on the 38 valid composite services
Towards a Fuzzy-oriented Framework for Service Selection in Cloud e-Marketplaces
reported in (Ezenwoke, 2017), Table 2 shows four
QoS attributes whose ranges are represented by
linguistic variable and underlying membership
functions. Table 3 and Table 4 show a sample user’s
preferences and aspiration over the 4 QoS attributes.
Figure 3 depicts how the user’s available QoS
requirements are captured using the GUI module.
Table 5 contains the sample optimal values
representing user’s QoS requirements. Table 6
shows the ranking of service with respect to user’s
requirements and the bubble graph visualization of
the ranking is shown in Figure 4.
Table 2: QoS Attributes and linguistic variables.
QoS Fuzzy sets
Very High, High,
Medium, Low
Low, Acceptable,
Below Average
Very High, high,
Average, Low
Premium, Standard,
Moderate, Cheap
Table 3: User’s QoS Preferences.
QoS Fuzzy Judgement QoS
Availability Extremely more important than Resp.Time
Availability Extremely less important than Reliability
Availability Somewhat Less important than Cost
Table 4: User’s QoS Aspiration.
QoS Goal Constraints
Availability Very High In the Vicinity of 98%
Resp. Time Low Very close to 400ms
Reliability Very High In the Vicinity of 75%
Cost Premium In the Vicinity of 400$
Table 5: Sample of complete user QoS requirements.
S Preference Aspiration
Availability 0.1242 98.49
Resp. Time 0.1237 489.46
Reliability 0.5798 75.43
Cost 0.1724 390.64
Table 6: Top ten services that match user requirements.
Rank ID Avail.
R. Time
1 S3 98.67 546.24 75.43 390.64
2 S17 99.03 546.24 75.43 386.15
3 S10 98.49 546.24 74.72 385.64
4 S35 98.62 489.46 75.72 360.98
5 S19 99.51 559.35 76 390.48
6 S4 97.16 546.24 72.48 381.15
7 S18 97.53 546.24 72.48 376.66
8 S20 98.01 559.35 73.04 380.99
9 S7 98.29 526.12 74.19 354.14
10 S32 98.02 551.35 75.62 360.46
Figure 3: Availability QoS Requirements for User’s
Figure 4: Bubble Graph for Ranked Services for User
Requirements: showing details for Service_ID 35 on
mouse over.
The proposed framework has the potential to address
some of the limitations that have been observed in
existing cloud selection techniques. These are
elaborated as follows:
Handling of Complex user Requirements that are
Beyond Capability of Individual Atomic Services:
The framework automates the composition of atomic
services to satisfy complex user requirements, and
updates the service directory by capturing scenarios
of new entrants and exists of atomic services. With
our framework, the number of potential composite
offerings can be planned a priori by e-marketplace
provider; similarly, atomic service providers can
drive the competitiveness of their offerings from
CLOSER 2017 - 7th International Conference on Cloud Computing and Services Science
knowing the number of composite offerings features
their services.
Enabling Flexibility to Accommodate Subjective
QoS Inputs: In addition, the framework promotes
the interface design of the e-marketplace with user
experience intended. This way, users can easily
express QoS requirements and find optimal
service(s) within the shortest time. The fuzzy-
enabled widgets integrated in our framework allow
users the flexibility of expressing subjective QoS
Improved Presentation Format for Search
Results to Aid Decision Making: Having obtained
a ranking of cloud services with respect to user QoS
requirements, the bubble graph used in the
framework enables comparison of the top ranked
services in one single view, thus simplifying the
selection decision compared to tabular listing.
In cloud service e-marketplace, service providers
should be able to join the ecosystem easily, and user
should conveniently express subjective requirements
(Akolkar et al., 2012), and to particularly explore
services without being overwhelmed by so many
choices. The main contribution of this paper is a
cloud service selection framework that incorporates
mechanisms to: 1) compose atomic services on the
fly to satisfy complex users’ requirements; 2) allow
users the flexibility of expressing QoS requirements;
both preferences and aspirations, and do so using
subjective descriptors that is more akin to human
judgment; 3) reduce choice overload by showing
only the top best services in a manner that facilitates
easy comparison for effective decision making. In
the nearest future, we plan to fully operationalize the
framework and evaluate its effectiveness in the
context of a real cloud service e-marketplace.
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