A Multi-criteria Scoring Method based on Performance Indicators for
Cloud Computing Provider Selection
Lucas Borges de Moraes
, Adriano Fiorese
and Fernando Matos
Dept. of Computer Science (DCC), Santa Catarina State University (UDESC), Joinville, Brazil
Dept. of Computer Systems (DSC), Federal University of Para
ıba (UFPB), Jo
ao Pessoa, Brazil
Cloud Computing Providers, Performance Indicators, Scoring Method, Ranking, Selection.
Cloud computing is a service model that allows hosting and on demand distribution of computing resources
all around the world, via Internet. Thus, cloud computing has become a successful paradigm that has been
adopted and incorporated into virtually all major known IT companies (e.g., Google, Amazon, Microsoft).
Based on this success, a large number of new companies were competitively created as providers of cloud
computing services. This fact hindered the clients’ ability to choose among those several cloud computing
providers the most appropriate one to attend their requirements and computing needs. This work aims to
specify a logical/mathematical multi-criteria scoring method able to select the most appropriate(s) cloud com-
puting provider(s) to the user (customer), based on the analysis of performance indicator values desired by the
customer and associated with every cloud computing provider that supports the demanded requirements. The
method is a three stages algorithm that evaluates, scores, sorts and selects different cloud providers based on
the utility of their performance indicators for each specific user of the method. An example of the method’s
usage is given in order to illustrate its operation.
The evolution of information society brought the need
of efficient, affordable and on-demand computational
resources. The evolution of telecommunications
technology, especially computer networks, provided a
perfect environment for the rise of cloud computing.
Cloud computing has shown a new vision of service
delivery to its customers. It became a differentiated
paradigm of hosting and distribution of computer
services all over the world via Internet.
Cloud computing abstracts to the user the complex
infrastructure and internal architecture of the service
provider. Thus, to use the service, the user don’t need
to perform installations, configurations, software
updates or purchase specialized hardware (Hogan
et al., 2013). On this way, the cloud computing model
has brought the benefit of better use of computing
resources (Zhang et al., 2010). In addition to being
a convenient service, it is easily accessible via the
network and it is only charged for the time that is used
(Armbrust et al., 2009; Zhang et al., 2010). In this
model all computing resources that the user needs,
can be managed by the cloud provider (Zhang et al.,
The success of cloud computing paradigm is
currently noticeable and it has been adopted in major
IT companies like Google, Amazon, Microsoft and
Salesforce.com and has become a good source of
development/investment both in the academy and
industry (Zhou et al., 2010; H
ofer and Karagiannis,
2011). This success led to the rising of a large
number of new businesses such as cloud computing
infrastructure providers. With the increasing amount
of new cloud providers the task of choosing and
selecting which cloud providers are the most suitable
for each user’s need has become a complex process.
The process of measuring the quality of each provider
and compare them is not trivial, as there are usually
many factors involved, many criteria to be studied and
checked out throughout the process.
Measuring the quality and performance of
a cloud provider (called Quality of Service or
simply QoS) can be made using various strategies.
One well-known strategy is numerically and
systematically to measure the quality of each
provider’s performance indicators (PIs), reaching
a certain value or score. Thus, providers can be
ranked and the provider that offer the higher score
is theoretically the most appropriate provider to that
Moraes, L., Fiorese, A. and Matos, F.
A Multi-criteria Scoring Method based on Performance Indicators for Cloud Computing Provider Selection.
DOI: 10.5220/0006289305880599
In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) - Volume 2, pages 588-599
ISBN: 978-989-758-248-6
Copyright © 2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
The research questions that this study aims to
investigate and answer are:
What are and what kind of PIs are used to describe
cloud computing providers?
How to utilize these PIs to systematically measure
the quality of each provider for each user?
The answer to the second question is obtained
through the method to be specified in this work, that
is, how to use the different data types (numbers,
classes, subclasses) collected from each cloud
provider and stored in different PIs to score a finite
list of different providers according to the needs and
requirements demanded by every possible consumer
of resources of these cloud service providers. Each
cloud computing service consumer is an user of
the proposed method. The consumer can have x
different requirements, wishing the w best ranked
cloud providers based on expected values for m PIs
of interest.
The developed method is a logical/mathematical
algorithm able to select the w more suited providers
for each specific user, scoring and ranking each
provider. This process is based on the utility of
each user’s interest PIs for each available provider.
The utility of each PI is calculated based on their
type (quantitative or qualitative), the nature of the
behaviour of its utility function (Higher is Better or
HB; Lower is Better or LB; Nominal is Best or NB
(Jain, 1991)), the desired/expected value by the user
(indicated through the input expression) and the value
of its competitors (other providers to be analyzed by
the method).
Therefore, this work aims to propose a simple,
intuitive (logical) and agnostic method with high
generality and high dimensionality, that is, flexible
and applicable to any PIs that may exist, regardless
of its type (quantitative or qualitative) for n generic
providers with m generic PIs, where n and m can grow
This paper is organized as follows: Section
2 presents and discusses different PIs found in
the studied literature to qualify cloud computing
providers. Section 3 presents related works to the
selection, scoring and ranking of cloud providers
based on indicators. Section 4 presents and discusses
the proposed method that scores and ranks the
different cloud providers based on user’s interest PIs.
Section 5 illustrates an example, with hypothetical
data, that represents an application of the proposed
method, in order to validate it and to demonstrate its
operation presenting the results. Finally, Section 6
presents the final considerations.
This section aims to expose and clarify some
performance indicators (PIs) used to evaluate and
qualify the different cloud computing providers.
Indicators are tools that allow a synthesized gathering
information for a particular aspect of the organization
using metrics that are responsible for quantifying
(assigning a value) the study of objects to be
measured. In general, the indicators can be classified
into two categories (Jain, 1991): Quantitative
(discrete or continuous) and qualitative (ordered or
Quantitative: They are those states, levels or
categories that can be expressed numerically, and
can be worked algebraically. The numerical
values assigned can be discrete or continuous.
Examples of discrete quantitative indicators are:
number of processors, amount of RAM (Random
Access Memory), disk block size, etc. Example of
continuous quantitative indicators are: response
time, weight, length of an object, area of a land,
Qualitative: Also called categorical indicators.
These indicators have distinct states, levels or
categories that are defined by an exhaustive
and mutually exclusive set of subclasses, which
may or may not be ordered. The ordered
subclasses have a perceptible logical graduation
among their subclasses, giving the idea of a
progression between them. Examples of ordered
qualitative PIs: security level (low, medium,
high), frequency of use of a service (never,
rarely, sometimes, often, always), etc. The
unordered subclasses do not have the idea of
progression, e.g.: type of computer service
(processing, storage, connectivity), research
purpose (scientific, engineering, education), etc.
We can also classify PIs according to the behavior
of its utility function (Jain, 1991). This means how
useful (effective benefit) the PI becomes when its
numerical value increases or decreases. There are
three possible classifications (Jain, 1991):
HB (Higher is Better): Users and/or system
managers prefer the highest possible values for
that indicator. For instance: System throughput,
amount of resources (money, memory, materials,
etc.), availability of a service, etc;
LB (Lower is Better): Users and/or system
managers prefer the lowest possible values for this
A Multi-criteria Scoring Method based on Performance Indicators for Cloud Computing Provider Selection
indicator. For instance: Response time, delay,
costs, etc.;
NB (Nominal is Best): Users and/or system
managers prefer specific values. Higher and
lower values are undesirable. A particular value
is considered the best. The system load is an
example of this feature. A very high system
utilization is considered bad to users because it
generates high response times. On the other hand,
a very low utilization is considered bad by system
managers since the resources are not being used
For the cloud computing paradigm we have a
especial set of PIs called key performance indicators
(KPIs) defined at Service Measurement Index (SMI).
The SMI was developed by the Cloud Service
Measurement Index Consortium (CSMIC) (CSMIC,
2014) and represents a set of KPIs that provide a
standardized method for measuring and comparing
cloud computing services. It also provides metrics
and guidelines to help organizations measuring
cloud-hosted business services and it works as a
framework that provides an holistic view of the
quality of service required by cloud computing
consumers. The SMI is a hierarchical structure whose
upper level divides the measurement space into seven
categories and each category is optimized by four
or more attributes (subcategories). The seven major
categories are (CSMIC, 2014): accountability, agility,
service assurance, financial, performance, security
and privacy, usability.
Figure 1 depicts a mental map that displays and
classifies several PIs that can be used for evaluation
and monitoring of cloud computing service providers
according other technical literature (Garg et al., 2011;
Garg et al., 2013; Sundareswaran et al., 2012; Shirur
and Swamy, 2015; Baranwal and Vidyarthi, 2014).
The PIs presented do not represent an exhaustive
list of all existing PIs. They form a portion of
the indicators most often found in scientific papers
studied. These PIs can be quantitative (integer or
real numbers), qualitative (represented by a category
or a set of them - simple categorical or compound
categorical) and/or may even fall into both types
(can appear as quantitative or qualitative). It is
important to note that PIs with boolean values were
classified as qualitative (at the proposed method they
will be treated as unordered qualitatives with only two
categories). The selection method proposed in this
work is this classification and it is agnostic, that is, its
user can use any PI that he wishes (since its present
for at least one provider registered in the database of
the method), not limited to those listed in this Section.
Figure 1: Classification of different PIs for cloud computing
This section presents related works already developed
by other authors to rank and select cloud computing
providers based on indicators.
Sundareswaran and others (Sundareswaran et al.,
2012) proposed a new brokerage architecture in the
cloud, where brokers are responsible for selecting
the appropriate service for each user/customer. The
broker has a contract with the providers, collecting
their properties (performance indicators), and with
consumers, collecting their service requirements. It
analyzes and indexes the service providers according
to the similarity of their properties. When the broker
receives a cloud service selection request the broker
ICEIS 2017 - 19th International Conference on Enterprise Information Systems
will search the index to identify an ordered list of
candidate providers based on how well they meet the
needs of users.
The authors (Shirur and Swamy, 2015) specify
a framework to quantify the efficiency of different
cloud computing providers through the Quality of
Service metrics (QoS). Based on that, the proposed
framework ranks cloud computing providers.
The framework divides the QoS metrics into two
categories: application dependent metrics (reliability,
availability, security, data center, cost, operating
systems support, platforms supported, service
response time, throughput and efficiency) and user
dependent metrics (reputation, client interface, free
trial, certification, sustainability, scalability, elasticity
and user experience).
A framework called “SmiCloud” is presented in
(Garg et al., 2013). It is responsible for measuring
the quality of service (QoS) of cloud providers and
ranking them based on that calculated quality. The
quality is directly related to the values of each metric
of the Service Measure Index (SMI) (CSMIC, 2014)
classified into functional and non-functional. The
work uses the Analytical Hierarchical Process (AHP)
(Saaty, 2004) in the calculation of the quality and
ranking of providers.
The framework developed in (Baranwal and
Vidyarthi, 2014) presents an expectation of QoS
metrics (also based on SMI) that the every cloud
provider should have. This expectations are then
used by a cloud broker that assists selecting the most
appropriate ones. That framework uses a voting
method that takes into account the user requirements
for ranking cloud providers.
The work developed in (Wagle et al., 2015)
proposes an evaluation model that verifies the quality
and the status of service provided by cloud providers.
The data is obtained by cloud auditors and is viewed
via a heat map ordered by the performance of
each provider, showing them in descending order
of overall quality of service provided. This map
represents a visual recommendation aid system for
cloud consumers and cloud brokers. The main metrics
are again based on the SMI: availability (divided in
uptime, downtime and interrupt frequency), reliability
(divided in load balancing, MTBF, recoverability),
performance (latency, response time and throughput),
cost (per storage unit and per VM instance) and
security (authentication, encryption, and auditing).
The work developed in (Achar and Thilagam,
2014) present a broker based architecture for selecting
the more suitable cloud provider based on the
measurement of the quality of service provided. This
approach prioritizes the selection of those providers
who fits better the request sent to the broker. Selection
involves three steps: To identify the proper and
necessary PIs to request, evaluating the weight of
each of these criteria using the AHP method, and
ranking of each provider using the Technique for
Order Preference by Similarity to Ideal Solution
(TOPSIS), used to select the alternative which is
closest to the ideal solution and farthest from negative
ideal solution.
This section aims to present and discuss the proposed
cloud service providers selection method. The
following subsections expose how the method works,
presenting a method overview, its inputs and outputs,
steps and mechanisms for calculating the score and
ranking of each cloud provider.
4.1 Method Overview
Figure 2 presents an overview of the selection method
to be described in this Section. The database
of cloud provider candidates and their performance
indicators can be fed indirectly through websites such
as “Cloud Harmony” (https://cloudharmony.com) or
through cloud providers by their own (e.g: Amazon)
or it can be consolidated by third parties.
Third Party
Cloud Providers Data Base
Provider 1
Provider 2
Provider n
PI 1 = X11
PI 2 = X21
PI 3 = X31
PI M = XM1
PI 1 = X12
PI M = XM2
PI 1 = X1n
PI 2 = X2n
PI 3 = X3n
PI 2 = X22
PI 3 = X32
PI M = XMn
Providers Vector “P” with “n” elements
Method Data Base
Input Expression
+ Number of Output
Providers: “w”
User Request
Cloud Provider
Selection Method
Based on PIs
Output Expression
1, name(Prov. 1), Score(Prov. 1), …
w, name(Prov. w), Score(Prov. w), ...
- PI 2 = X2 - Priority L1
- PI 3 = X3 - Priority L2
- PI M = XM - Priority Lm
Provider 1
Provider n
Figure 2: Multicriteria method for selecting cloud providers
based on performance indicators.
Data input (Inputs) corresponds to a list P with
n different candidates (cloud providers), each one
A Multi-criteria Scoring Method based on Performance Indicators for Cloud Computing Provider Selection
with M different PIs (whose values are known), and
an input expression (generated by the user of the
method) containing m PIs of interest (subset of the
known M PIs) and the priority level of each one. This
priority level is set by the method’s user according the
classification adopted in the proposed method. The
initial cloud providers list P will be filtered, at the
first step of the method, based on the input expression
and at the end it will have n
elements (with n n
If n
= 0, there is none available compatible provider
to the user, so the method interrupts the process with
an error message; if n
= 1, there is only a single
compatible provider, which will be returned to the
user; however, if n
> 1 the method proceeds along
to another stage to rank providers. The expected
output data (Outputs), except the special conditions
mentioned, is a list with the cloud providers better
scored by the method. The proposed method is
divided into three main stages:
1. Elimination of incompatible cloud providers to
the user;
2. Evaluation and scoring of interest PIs for each
priority level;
(a) Score quantitative PIs;
(b) Score qualitative PIs.
3. Calculation of the final score for each cloud
provider, ranking them and return results to the
4.2 Stage 1 Elimination of
Incompatible Cloud Providers
Figure 3 summarizes what occurs in the first stage
of the method. In fact, the initial list of candidate
providers (P) is cleaned from all incompatible cloud
providers (concept discussed further), generating a
new list P
with n
different providers.
Providers list “P” with their PIs
User’s Input
Elimination of Incompatible
Cloud Providers
Compatible providers “P’ ”
if n’ = 0 ERROR
if n’ = 1 Single Provider
Go to next Stage
Figure 3: Stage 1 – Elimination of incompatible providers.
For the proposed method, we have created the
classification of PIs’ priority levels presented in
Figure 4. Each PI can be classified as essential or
non-essential by method’s user. The non-essential PIs
have different priority levels, which can vary between
levels “High” and “Low”. In order to simplify this
work, it was adopted only one intermediate level of
priority, named “Medium”.
Highest priority/Importance
Priority/Importance given by levels
Priority Levels
Top level
Intermediate level
Lowest level
Figure 4: Classification of priorities of the PIs in the pro-
posed method.
An essential PI has the highest priority/importance
for the customer (user) and consequently to the
proposed method. It indicates that if the specific
value entered in the Input expression is not satisfied,
the user cannot achieve their goals. This makes it
a criterion of elimination. Thus, a provider that
does not attend all essential PIs will be automatically
deleted from the list P of valid candidates (compatible
providers) because it is incompatible to that user in
On the other hand, if a non-essential PI is not
attended, it does not incapacitate the user to achieve
their goals. But, it can jeopardize them. How much
it could be jeopardized (seriously or very little) is
directly related to the priority level of the PI, set by the
user. Do not attending a very high level PI means that
the user will be seriously impaired in achieving their
goals. On the other hand, do not attending a minimum
priority level PI implies that the negative impact will
be virtually imperceptible. The optional PIs can be
automatically classified with minimum priority level.
In this work, it will be considered incompatible,
the cloud provider that does not attend all the user’s
essential PIs. The concept of incompatibility and,
in general, the scores of cloud providers, are based
on the premise of attending/matching the desired PIs
values (made available in the input expression by the
ICEIS 2017 - 19th International Conference on Enterprise Information Systems
user). However, what means a particular PI j attends
a specific y value desired/required by the user? The
answer to this question depends on the type of the PI
and the behavior of the utility function of j. Thus, if j
is quantitative, there are three possible classifications
for the behavior of its utility function: HB (Higher is
Better), LB (Lower is Better) or NB (Nominal is the
best) (Jain, 1991).
Thus, given a quantitative PI j that stores the value
x (number) and j belongs to the cloud provider i, if j
attends the value y, specified by the user, then we can
conclude that:
x attends y then
x (y t
) if j HB
x (y +t
) if j LB
x = (y ±t
) if j NB
Where t
represents a certain tolerance regarding
x, that is, a deviation from the desired value y tolerated
by the user. Comprising the proposed method, the
default tolerance value is zero, but it can be adjusted
by the user via input expression.
Meanwhile, if the PI j is qualitative, it can be
ordered or unordered (Jain, 1991). If it is unordered,
the rule is simple: if x is the value that the user
specify (y), then the PI j is attended, otherwise it is
not. However, if PI j is ordered each value (category
or class) has a certain relationship with the others,
scaling from a lower level to a higher level. If the
user specifies a low level value, a higher level value
can also satisfy, it depends on the PI in question.
An example of this are the qualitative PIs security
and quality of service with values: “low”, “medium”
and “high” (Sundareswaran et al., 2012). If the PI
is quality of service and the user specify the value
“medium”, the value “low” would not be appropriate,
but the value “high” would be equally good, or even
better. For the PI security level this isn’t always true,
because a very high degree of security can be harder
to work and this may impair the user’s work. Thus,
higher and/or lower level values (categories) than the
desired category y can also satisfy the user. Therefore,
to solve this problem an ontology, similar to the one
in (Jain, 1991), was created in order to indicate if
an ordered qualitative PI has tolerances for categories
below and/or above the desired category.
Higher is Tolerable (HT): Categories above of
the desired one are tolerable;
Lower is Tolerable (LT): Categories below of the
desired one are tolerable;
Higher and Lower are Tolerable (HLT):
Categories above and below of the desired one are
both tolerable;
These tolerances can be set by the user via input
expression. If nothing is informed, the default used is
NB. Thus, given a qualitative PI j, which stores the
value x (category), and j belongs to the provider i, if j
attends the value y, specified by the user, then we can
conclude that:
x attends y then
x = y if j is unordered OR j NB
x y if j is ordered AND j HT
x y if j is ordered AND j LT
j is ordered AND j HLT
In any case of PI j, whether quantitative or
qualitative, whose value x does not satisfy Equation
1 or Equation 2, respectively, it is said that j doesn’t
attend the value of y, specified by the user. Therefore,
taking into account the PI attending premise and the
incompatibility concept, at the end of this initial stage
it will remain a list of candidate cloud providers
containing only those compatible ones with the
requirements of essential PIs to be selected.
4.3 Stage 2 Evaluation and Scoring
Interest PIs for Each Priority Level
This second stage aims to score each provider
individually, according to the utility (real benefit) of
each one of its PIs. The higher the utility value
associated with the PI, the higher the score. The
utility is influenced by the value specified by the user
(desired one) and also regarding the best value (bigger
utility) among all candidate providers for that specific
This stage will receive the list P
, with the n
filtered providers from the previous stage. Each
provider presents values for the m user’s interest PIs,
which can be quantitative or qualitative. Each one of
these PIs has a priority level associated with that is
set by the user in the input expression. Thus, if L is
the number of different available priority levels and
the amount of PIs with the lth level of priority,
the score (Pts
) for the ith provider is given by
Equation 3.
(i) =
That is, the score of the lth priority level is the
simple arithmetic average of the individual scores for
each PI
with the same priority level l, whether the
PI is quantitative or qualitative. This stage ends when
all the L levels are scored for each of the n
providers. For example, in this work we considered
four priority levels (L = 4): “Essential”(always
A Multi-criteria Scoring Method based on Performance Indicators for Cloud Computing Provider Selection
maximum level), “High”, “Medium” and “Low”.
Thus, each provider i will always have four scores
at the end of this stage; one for each priority level.
Regardless of the priority level, the individual score
)) for a quantitative and a qualitative PI is
calculated in different ways.
4.3.1 Scoring Quantitative PIs
The score of a PI j of a provider i, will be 0 if its
numerical value x doesn’t attend the numerical value
y, specified by the user. If the value is attended, it
will be scored in proportion to how useful (utility) this
value is compared to all other compatible providers
available in the candidates list (given by a constant).
The evaluation function of a quantitative PI is shown
in Equation 4. It always returns a normalized real
number between 0 and 1 ( value( j), y,X
and C
,t > 0).
Pts( j) =
0, if value( j) doesn’t attend y
value( j) y
, if j HB
y value( j)
y X
, if j LB
t |y value( j)|
, if j NB
Where the real constants (empirical parameters)
and C
belong to the normalized open interval
between ]0,1[, and C
+ C
= 1, mandatorily. The
number X
is the highest value among all other n
providers in the list P
for that PI j; as well as X
is the lowest value and t is the maximum tolerated
distance (number) from the optimum point (y) for a
NB PI (since that PI attends y, that is, belongs to the
interval [y t; y +t]). The value of t can be configured
by the user.
The same happens with the coefficients C
and C
. They can be tuned according the user
understanding of how to weight the PI attending
and how proportionally attended is the PI regarding
the same PI on other providers. The constant C
weighs the score given the desired minimum match
(including the tolerances associated with the PI, if
any) between the value in the provider (x) and the
value that the user wants (y), based on the PI type
under analysis (HB, LB or NB). The constant C
weighs the score given to how much this PI value
excels the desired minimum, that is, the value x
is, in practice, better than the desired value y. It
is noteworthy that the first coefficient (C
) must
be greater than the second one (C
), because it is
not interesting to weight more how better a PI is
comprising its value in other cloud providers, in
prejudice of the attending the user desired value.
Also, it is essential that C
= 1.
For this work, it was initially adopted C
= 0.7
and C
= 0.3. Thus, to the fact that a quantitative PI
attends the value y (desired by the user), it is given a
score of 0.7 (70% of the total score). The other 0.3
(30% of the total score) comes from how well ranked
this PI is among all other competitors in the list of
compatible candidate providers (P
). If the PI has the
best value among all, the evaluation returns 1. If it
has the lowest (but still attends the given value y) then
Pts( j) is 0.7. Summing up, if PI does not attend the
value y, then evaluation returns 0, otherwise it returns
a value between 0.7 and 1. Thus, when j HB, the
higher its value, the closer to 0.3 will be the second
term of the sum in Equation 4. When j LB, the
lower its value, the closer to 0.3 will be the second
term of the sum in Equation 4. Finally, when j NB,
the closer to the y value, the closer to 0.3 the second
term of the sum in in Equation 4.
4.3.2 Scoring Qualitative PIs
In spite of numerical values, qualitative PIs (or
categorical) have categorical (string) ones. Therefore,
qualitative PIs can be ordered or unordered.
Comprising the unordered qualitative PIs, or the
category is that one the user specified (receiving score
1) or not (receiving score 0). Regarding ordered
qualitative PIs, the score depends on the tolerance
supported and informed by the user (HT, LT, or HLT)
from the PI’s value (category) offered by the provider.
Categories of higher and/or lower levels to the desired
category y can be tolerable to the user and may
thus scoring. If nothing is mentioned about it in
the input expression it is concluded that there is no
tolerance and scoring proceeds in the same way as for
unordered qualitative PIs.
For this work it was defined that the score of
tolerable categories will be directly influenced by the
disparity between the category specified by the user
(y) and that offered by the provider (A). This means
the greater the distance of the category in question
(A) to the desired one (y) (both given by integers), the
lower the score for that PI. Figure 5 presents how the
score is influenced by the type of tolerance associated
with a qualitative ordered PI, how to specify the
categorical levels between the PI categories and how
calculate the distance between these levels. The
numbers identifying the desired category (y) and the
tolerated ones are underlined. The score of a tolerated
category is a constant multiplied by the normalized
distance between the categories A and y.
The score of an ordered qualitative PI will always
ICEIS 2017 - 19th International Conference on Enterprise Information Systems
Very Low
Regularly Low
Regularly High
Very High
A = Value offered = Low
y = Value desired = High
Score: HT: 0 LT, HLT: Const * norm(Dist(A,y)) NB: 0
Dist(A, y) = |A - y| = |3 - 7| = 4
K1 = Categories above High = 2
K2 = Categories below High = 6
Dist(A, y)
Figure 5: Relationship between the score, category-value
and the type of tolerance associated with a hypothetical or-
dered qualitative PI with 9 distinct categories.
be a real value between 0 and 1. Therefore, in case
of perfect match between the desired category (set
by the user in the input expression) and the cloud
provider offered category, then that category receives
the maximum score 1. When do not occurs a perfect
match, categories are scored according Equation 5. In
this case, the desired neighboring categories (above
and below) will score C
(with 0 < C
< 1) and
so on. In this sense, in Equation 5, A = value( j)
is the category under consideration (value of the
PI j that is offered by the cloud provider), y the
user’s desired category and K
, K
and K
= K
, the total number of tolerable categories, higher,
lower to y or both depending on the PI is HT, LT
or HLT, respectively. The distance between the
categories value( j) and y is the difference between
their levels in module: |level(value( j)) level(y)|.
To each category is assigned a level value associated
with a positive integer from 1 to the total of
categories available (for that ordered qualitative PI),
in increasing order of graduation (lower levels, lower
numbers, higher levels, higher numbers, according
to Figure 5). It is important to note that the bigger
the distance between the category value( j) and the
desired category y, the smaller the score.
Pts( j) =
dist(A,y) + 1
, if j HT
dist(A,y) + 1
, if j LT
dist(A,y) + 1
, if j HLT
The real constant C
represents the maximum
score that a tolerable category (another category
different of the desirable y, but within the tolerances
associated with that particular PI) can assume.
Therefore, the smaller the value of C
, more
aggressive is the penalty (loss of score) applied
to any and every PI j, whose category value( j)
diverges from the desired optimal value y. If C
0, then the method punctuates with zero any value
different than y, whether the PI is ordered or not.
This is an undesirable behavior, since it depreciates
sub-optimal, and can penalize excessively providers
that are also appropriate for the user. If C
= 1, the
method depreciates the importance of reaching the
optimal point for a qualitative PI, assigning too much
punctuation to sub-optimal ones, encouraging the
wrong choice of the best provider(s). For this work
it was initially adopted as a starting point C
= 0.7,
that is, a category next to y and within the tolerance
will receive a score of 0.7 (70 % of the total score).
4.4 Stage 3 Final Scoring for Each
Cloud Provider
Previous two stages results in four scores for each
cloud provider from the list P
: one for each priority
level: “Essential”, “High”, “Medium” and “Low”,
including quantitative and qualitative PIs. The
consolidation of these scores in a single value will
be the provider’s final score. Therefore, a weighted
arithmetic average will be used, where the coefficients
(weights) are directly proportional to the priority
levels. Equation 6 presents the score to a certain
provider i. It is worthwhile to note that the sum of
all weights must be 1 (α
+ ... + α
= 1).
f inal
(i) =
(i)) (6)
An efficient technique for calculating each
coefficient (α
) is to use a matrix of judgements.
A judgement matrix aims to model relationships
(e.g.: importance, necessity, discrepancy, value,
etc.) between the judged elements (Saaty, 2004).
In this case, the elements to be judged (regarding
the determination of weights) are the priority levels
of each PI. Therefore, the judgement matrix is a
matrix with dimension L, wherein each row and each
column represents a different priority level, arranged
in descending order of priority (from top to bottom
lines, from left to right columns). This technique
is used several times in the decision-making method
called Analytic Hierarchy Process (AHP) (Sari et al.,
2008; Ishizaka and Nemery, 2013; Fiorese et al.,
Comprising this work, which has only four levels
of priority, Table 1 presents a possible judgement
matrix. The assigned values are based on the scale
A Multi-criteria Scoring Method based on Performance Indicators for Cloud Computing Provider Selection
of Saaty (Saaty, 2004). In this case, the values in the
judgement matrix indicate how important is the line
element i with respect to the column element j. Thus,
following this methodology to build the judgement
matrix, we obtain all values in the diagonal equal to
1 and the observed inversions. On the last line, the
elements of each column are summed up in order to
advance the next step to find the weights, which is the
normalization of this judgement matrix.
Table 1: Matrix of judgments: Relations of importance be-
tween each different priority levels.
Levels Essential High Medium Low
Essential 1 2 4 9
High 1/2 1 2 6
Medium 1/4 1/2 1 3
Low 1/9 1/6 1/3 1
Col. sum. 1,8611 3,6667 7,3333 19,00
Following the judgement matrix technique, the
judgement matrix normalization takes place. This
process takes each column element divided by its Col.
sum. position, according Table 1. The results can be
seen in Table 2, which represents Table 1 normalized.
Table 2: Normalized judgement matrix for each priority
Levels Essential High Medium Low
Essential 0,5373 0,5455 0,5455 0,4737
High 0,2687 0,2727 0,2727 0,3158
Medium 0,1343 0,1364 0,1364 0,1579
Low 0,0597 0,0455 0,0455 0,0526
Finally, the weights for each one of the priority
levels are resolved summing the values on the priority
level line of the normalized matrix and dividing the
result by the number of priority levels (L), which is
4 in this case. Therefore, Table 3 shows the resolved
priority level weights.
Table 3: Performance indicators’ priority level weights.
Levels Weights
Essential 0,5255
High 0,2825
Medium 0,1413
Low 0,0508
Thus, after the consistency checks on the
judgement matrix (Saaty, 2004; Sari et al., 2008;
Ishizaka and Nemery, 2013), which allowed its
normalization and the weights get resolved, we have
Equation 7, where the unknowns α
of the Equation 6
are resolved. Therefore, Equation 7 represents the
cloud provider score.
f inal
(i) =0, 5255 Pts
(i) + 0,2825 Pts
0,1413 Pts
(i) + 0,0508 Pts
It is worth to note that the score of each provider
i is normalized between 0 and 1. After scoring all
providers, the list of compatible providers is ordered
by score in descending order. Then, the proposed
method returns the w first providers in that ordered
list (highest scores) to the user.
Once the cloud service provider selection method is
specified, it is necessary to expose an example of
its application on a set of real or hypothetical data
in order to show its operation helping to answer
questions about its procedure and results. Thus,
this Section aims to apply the specified method on
a possible data set. Table 4 shows an example of
data that can be used for that. It shows 5 fictitious
providers, each one with 7 interest PIs to the user.
This data set represents the information regarding the
candidate cloud providers that will be selected using
the proposed method.
Table 4: Data set example of PIs to cloud providers.
PI/Provider Prov
NI 5 3 9 12 10
NOS 1 2 4 8 6
Cost (U$/h) 0,30 0,50 1,00 1,50 1,20
RAM (Gb) 4 2 4 8 16
Storage (Gb) 10,0 50,0 80,0 15,0 5,0
Avail (%) 99,9 95,0 88,0 99,0 90,0
Sec M M H L M
NI: Total types of available Virtual Machines. Integer
NOS: Total available operating systems. Integer 1.
Cost: Average cost of the desired service. Given in U$/h.
RAM: Average amount of RAM available. Given in
Storage: Average amount of data storage. Given in
Avail: Availability of the service per year, in average.
Given in %.
Sec: Estimated level of information security and privacy.
It has 3 possible categories: High (H), Medium (M) and
Low (L).
Several steps compose the proposed method
execution. The first step copes with the identification
of the nature of each PI and with checking which
one attend and which one do not attend the desired
values specified by the user. To accomplish that,
ICEIS 2017 - 19th International Conference on Enterprise Information Systems
it is necessary that the proposed method recognizes
that the PI “NI” is quantitative discrete with utility
function HB; “NOS” is quantitative discrete NB;
“Cost” is quantitative continuous LB; “RAM” is
quantitative discrete HB; “Storage” is quantitative
continuous HB; Avail” is quantitative continuous
HB; and “Sec” is qualitative ordered NB. This
matching can be done since the cloud provider PIs
database is kept updated by experts or by the user
of the method, including this information about the
PI’s nature. Once the PI nature is acknowledged, the
user needs to provide an input expression comprising
which requirements (PIs), their values (including,
eventually, tolerances and their values) as well as their
priority levels. This input expression intends to be
used by the proposed method to rank the PI attending
providers, returning back to the user the w best ranked
(when there are w). Table 5 shows an user input data
used for this method working example. Values in
brackets represent the tolerance for that PI.
Thus, taking into account the PI values provided
by the user for this example, Table 6 shows the
desirable values (based on the utility functions
associated with them) and tolerable values (in
accordance with tolerance values associated with
utility functions provided by the user) for each PI.
Continuing the analysis of the PI values required
by the user and those provided by cloud providers,
Table 7 shows provider’s max/min PI values needed
for the final scoring of each provider.
Table 5: Example of data input entered by the user.
PIs and their values Tolerance Priority levels
RAM 4 Essential
Storage 5 0,5 GB Essential
Cost 1,00 0,10 U$/h High
Avail 90 0,5 % High
NI 8 1 Medium
Sec = Medium HT Medium
NOS = 3 1 Low
Table 6: Analysis of the PIs presented in example.
PI Desirable values Tolerable values
RAM (GB) [4,+)
Storage (GB) [5,0; +) [4,5; 5, 0)
Cost (U$/h) [0;1, 00] (1, 00; 1,10]
Avail (%) [90,0; 100, 0] [89,5;90, 0)
NI [8,+) 7
Sec Medium High
NOS 3 2 and 4
Thus comparing Tables 4 , 5 and 6 it is possible
to determine if there is incompatible providers to
eliminate from the list P (methods’s Stage 1). It is
Table 7: Max/Min PIs values from providers.
PI Max/Min values
= 16 GB
Storage (HB) X
= 80, 0 GB
Cost (LB) X
= 0, 30 U$/h
Avail (HB) X
= 99, 9 %
= 12
Sec (NB)
NOS (NB) t = 1
incompatible any provider that does not attend all
the essential PIs (i.e., “RAM” and “Storage”). A PI
attends certain desired value, if its value is in the range
of desirable values or at least in the range of tolerable
values, both identified in Table 6. On that basis, it was
built Table 8, where () informs that the PI attends the
user’s value and () that it does not attend.
Table 8: Identification of provider’s PIs that attend the
user’s desired values.
Storage (E)
Cost (H)
Avail (H)
NI (M)
Sec (M)
Observation of Table 8 allows us to conclude
that only cloud provider Prov
does not attend the
essential PI “RAM”. This observation is backed
up to Table 4 that shows Prov
has only 2 GB
of RAM, leaving user requirement of 4 GB or
more, unattended. Thus, regarding the ve PIs
used, the only incomparable provider is Prov
therefore it should be removed from the list of
suitable/compatible providers. Thus, the next stages
only will consider the new generated list P
all providers (and their PIs), except Prov
Next, on Stage 2, proposed method must score
the quantitative and qualitative (Subsection 4.3.2)
PIs, using Equation 4 and Equation 5, respectively,
regarding Prov
e Prov
. The score,
by priority levels, as requested by user, is calculated
according Equation 3. Table 9 presents the final
scores by priority level of the PIs comprising each
compatible provider. The constants used are: C
0.7, C
= 0.3 e C
= 0.7.
Next, on Stage 3, final score is calculated and
consequently the ranking for each one of the four
competing providers. This task is performed to each
provider, according to Equation 7 (Subsection 4.4).
A Multi-criteria Scoring Method based on Performance Indicators for Cloud Computing Provider Selection
Table 9: Providers’ scores by priority level.
Priority Levels
Essential High Medium Low
0,71 1 0,5 0
0,85 0,35 0,7375 0,7
0,77 0,485 0,5 0
0,85 0,35 0,925 0
Coefficient values used in the weighted average are
the values shown in Table 2. These weights are
applied to the scores of each priority level already
calculated. Table 10 presents the final score to the
cloud providers 1, 3, 4 and 5.
Thus, according to Table 10, it is possible to
rank the 4 competing providers in scoring descending
1. Prov
with 0,7263 points;
2. Prov
with 0,6853 points;
3. Prov
with 0,6763 points;
4. Prov
with 0,6123 points.
Therefore, Prov
is the most suitable to the
user in this example. The proposed method
can return to the user a list containing the w
better ranked/ordered providers. Thus, given
w = 3, the return would be: {1,Prov
This work specified a multi-criteria scoring method to
assist decision making that scores and ranks (orders)
cloud computing providers in order to select the
most suitable ones, based on the user’s requirements
(criteria) regarding their performance indicators. The
requirement must present the performance indicators
(PIs) of interest, the preference (desired) values for
each PI and the priority of one over the other, that is,
their importance to the fulfilment of user goals. The
specified selection method comprises an intuitive and
simple way to calculate whether the value of certain
PI fits (attends) the user desired value and how to
score it in a manner consistent with other competing
available providers.
The proposed method is agnostic regarding which
PIs to use in order to score, rank and select
cloud providers. This means user can request,
in his/her input expression, any PI and desired
value. Notwithstanding, this work presented a method
utilization example that has considered a set of
indicators present in ve works (Garg et al., 2011;
Garg et al., 2013; Sundareswaran et al., 2012; Shirur
and Swamy, 2015; Baranwal and Vidyarthi, 2014).
In addition, we also used the Service Measure Index
(SMI) framework (CSMIC, 2014), which provides
a good set of PIs to measure and compare cloud
computing services.
The proposed method is designed following
three stages: 1) removing incompatible providers;
2) scoring quantitative and qualitative PIs by priority
level and calculating final scores to providers;
3) ranking them and return results to user. The
proposed method separates PIs into two types:
essential and non-essential. The non-essentials have
different degrees of importance, giving rise to distinct
priority levels, thus, the higher the importance, the
higher the priority. The final cloud provider score
takes into account this priority. Thus, higher priority
levels have larger weights and consequently they have
higher influence on the final score. The final score
is a real number between 0 and 1. The closer to 1,
the most appropriate and preferable is that provider in
relation to its competitors for that user in question.
The main benefits of the method are its high
generality and high dimensionality, that is, the ability
to work with any and all available PIs regardless
of whether it is quantitative or qualitative, and its
database can be easily and indefinitely expanded
(number of total providers and the number of PIs of
each provider to be considered for selection). The
method is also simple and intuitive, since it does
not require sophisticated mathematical and modelling
skills to understand or use it.
The major limitation of the method is the
need to have as pre-requisite a large database of
cloud computing providers with the respective PIs
registered for each provider. It is necessary to
establish relationships of trust with the providers if
it is decided that they will provide such data or, if
it comes from third parties, they must somehow to
ensure the data veracity. In addition to obtaining
the data from the providers it is necessary to classify
them: quantitative (HB, LB or NB) and qualitative
(NB, HT, LT or HLT).
Other factor to consider is the need to adjust
parameters C
, C
and C
in the method. Although
these parameters give more flexibility to the method,
if they are poorly adjusted the method efficiency
will be seriously compromised. The parameters
are empirical constants that need several tests to
draw more precise conclusions, mainly regarding the
ratio of C
to C
. It is mandatory to respect the
domain presented (be a real between 0 and 1) and the
restrictions: C
= 1, C
> C
and C
< 1.
An example of the method was presented,
demonstrating its use and the convenience of its
ICEIS 2017 - 19th International Conference on Enterprise Information Systems
Table 10: Final score calculation.
General formula:
f inal
(i) = 0, 5255 Pts
(i) + 0,2825 Pts
(i) + 0,1413 Pts
(i) +
0,0508 Pts
Provider Final score
0,5255 (0,71) + 0,2825 (1,00) + 0,1413 (0, 50) + 0, 0508 (0) = 0, 7263
0,5255 (0,85) + 0,2825 (0,35) + 0,1413 (0, 7375) + 0, 0508 (0, 70) = 0, 6853
0,5255 (0,77) + 0,2825 (0,485) + 0,1413 (0, 50) + 0, 0508 (0) = 0, 6123
0,5255 (0,85) + 0,2825 (0,35) + 0,1413 (0, 925) + 0, 0508 (0) = 0, 6763
Future work includes testing the proposed method
in realistic settings, as well as the creation of a cloud
computing broker that incorporates the developed
The authors would like to thank UDESC PROBIC
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A Multi-criteria Scoring Method based on Performance Indicators for Cloud Computing Provider Selection