A Trust Reputation Architecture for Virtual Organization Integration in
Cloud Computing Environment
Luis Felipe Bilecki
, Adriano Fiorese
and Fernando Matos
Department of Computer Science (DCC), Santa Catarina State University (UDESC), Joinville, Brazil
Department of Computer Systems (DSC), Federal University of Para
ıba (UFPB), Jo
ao Pessoa, Brazil
Trust, Virtual Organization, Reputation, Cloud Computing, Collaborative Networks.
Virtual Organization (VO) represents a prominent collaboration initiative, where a set of entities share compe-
tencies and risks attending a common goal. Moreover, their interactions can be supported in an Internet basis,
using Cloud Computing (CC) resources. The VO and CC integration brings several benefits, such as: reduc-
tion of costs and maintenance, interoperability, among others. However, there are issues related to privacy,
trust and security that need to be addressed. One of the issues observed is how much trust VO members put
in the cloud provider (CP), particularly, in a scenario where VO members use the resources provided by a CP
to made available their services and in order to interact with other members. Thus, the proposed reputation
architecture intends to assist the decision-making processes present in the VO’s life-cycle reputing CP trust.
The reputed trust is based on two sources: a) objective (Quality of Service (QoS) indicators) and b) subjective
(feedback from users regarding those QoS indicators). The evaluation results show that the architecture is
resilient to attacks on subjective trust during the reputation calculation. Also, it is possible to note that the
proposed architecture presents an acceptable average time for each one operation, and a significant role during
VO’s creation and operation.
The socioeconomic challenges faced by society (e.g.
globalization and competitiveness), have motivated
small and medium-sized enterprises in the adoption of
collaborative methodologies, reducing time and costs
of the production process (Esposito and Evangelista,
Virtual Organization (VO) is a collaborative net-
work where via a temporary alliance, a set of legally
independent entities (enterprises), heterogeneous and
geographically disperse, share resources, skills, capa-
bilities and risks in order to attend a collaboration op-
portunity (Camarinha-Matos et al., 2009). VOs may
use technologies such as Cloud Computing, to sup-
port the transactions between people and enterprises.
In the cloud based scenario presented in (Ruaro
and Rabelo, 2016), the exchange of information, host-
ing and execution of applications are performed in the
cloud. This means that VO members can use cloud
resources to execute and provide their services and
In this sense, Cloud Computing (CC) acts as a
technology that provides access to computing re-
sources (e.g. infrastructure and applications) on
a practical and on-demand way (Mell and Grance,
This integration between VO and CC, presents
some benefits, such as: cost reduction, competitive
advantage, resources provided according to demand,
among others (Ruaro and Rabelo, 2016). However,
some issues could arise, such as: security, search
and selection of cloud providers, and trust assess-
ment (Noor et al., 2013). A particular challenge con-
cerns the trust assessment from VO members to CC
providers. This is an important issue since trust plays
a significant role in the collaboration opportunity at-
tendance (Arenas et al., 2010), because VO members
use the provided resources as a support to execute
their activities.
Considering the trust issue in this environment,
this work presents a trust reputation architecture to
support the several decision-making processes exist-
ing in the VO’s life-cycle. The proposed reputation
architecture is responsible for generate the trust indi-
cators for reputation from two sources (objective and
subjective), disseminate reputation and attend other
requests through a centralized approach, monitor the
Bilecki, L., Fiorese, A. and Matos, F.
A Trust Reputation Architecture for Virtual Organization Integration in Cloud Computing Environment.
DOI: 10.5220/0006276506950702
In Proceedings of the 19th International Conference on Enterprise Information Systems (ICEIS 2017) - Volume 2, pages 695-702
ISBN: 978-989-758-248-6
Copyright © 2017 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
objective source during VO’s operation, and receive
feedbacks from VO’s member to CC providers, in or-
der to update the cloud provider’s reputation.
Thus, the proposed architecture intends to cover
all stages and decision-making processes existing in
VO’s life-cycle. During the VO’s creation stage, the
reputation can be used as an indicator in the process
of cloud providers search and selection. In Opera-
tion stage, the reputation should be verified and eval-
uated. In the Evolution stage, a potential change of
cloud providers may be necessary, and finally, in Dis-
solution the transactions feedbacks are collected, in
order to update the subjective trust and consequently
the reputation of any cloud provider.
The remainder of this work is organized as fol-
lows. Section 2 presents the related concepts of this
work. Section 3 presents the proposed trust repu-
tation architecture. Section 4 presents the scenario
where simulations are performed, the parameters used
in simulation and shows and discusses results from
the evaluation. Finally, Section 5 presents the conclu-
sions and future work.
2.1 Virtual Organizations and Cloud
Due to dynamic environment and market competitive-
ness, enterprises have been noticing need to work to-
gether in order to operate with greater agility and flex-
ibility, joining collaborative networks to achieve com-
mon goals (Alawamleh and Popplewell, 2010).
A Virtual Organization (VO), a form of collabo-
rative network, is understood as a temporary alliance,
where a set of legally independent and heterogeneous
entities (usually enterprises) share skills, resources,
competences and risks, in order to achieve specific
business goals (to attend a collaboration opportunity)
(Camarinha-Matos et al., 2009). A striking and de-
cisive VO feature is the use of resources provided by
a communication infrastructure to obtain competitive
advantage. Thus, the VO is configured as a single en-
tity through the union of the core competencies of its
entities (Arenas et al., 2010).
The VO’s life-cycle is composed of several stages
(Camarinha-Matos et al., 2009). In the Creation stage,
partners are discovered, selected and the network is
configured. In Operation stage, partners interact and
exchange information to achieve a common goal. The
Evolution stage is performed during VO’s operation,
when minor changes occur in membership, roles or
operation principles. Finally, in the Dissolution stage,
the VO’s finishes its operation and this stage can oc-
cur in two ways, where the business objectives are
achieved successfully or due to serious problems in
operation that invalidates the VO’s existence.
The emergence of new concepts of information
and communication technologies (ICT), such as grid
computing and cloud computing, brought a new ap-
proach to VO and its operation, which could improve
its service quality and enhance market survival.
Cloud Computing (CC) can be defined as a set
of computing resources (processing, storage, con-
nectivity, platforms, applications, and services) that
are available over the network (Internet) and can be
quickly provisioned without any human intervention
(Mell and Grance, 2011).
The main characteristics of cloud computing are:
on-demand self-service, resource pooling, broad net-
work access, rapid elasticity and measured services
(Mell and Grance, 2011). The CC provides services to
the users based on three different models, SaaS (Soft-
ware as a Service), PaaS (Platform as a Service), and
IaaS (Infrastructure as a Service) (Ruaro and Rabelo,
Through the mentioned models, small and
medium enterprises use these resources, for the de-
velopment of business systems and information shar-
ing in collaborative environment, that set up CC as a
infrastructure to support VO’s operation.
Nevertheless, the integration of CC and VO, of-
fer several benefits. The necessary perception of trust
arouses the need for trust assessment, specially dur-
ing the several stages of VO’s life-cycle. The trust in
the cloud providers appears as a key element that can
jeopardize the collaboration opportunity during VO’s
operation. Thus, Section 2.2 presents trust and repu-
tation concepts and their relationship with VO.
2.2 Trust and Reputation
Trust is a multidisciplinary concept and it has been
used in many areas. Besides multidisciplinary, trust
is a complex concept and have different meanings on
each context.
A formal definition about the concept of trust is
provided by Gambetta (Gambetta et al., 2000): “trust
(or, symmetrically, distrust) is a particular level of the
subjective probability with which an agent assesses
that another agent or group of agents will perform a
particular action, both before he can monitor such ac-
tion (or independently of his capacity ever to be able
to monitor it) and in a context in which it affects his
own action”.
Thus, in a computational implementation, trust
ICEIS 2017 - 19th International Conference on Enterprise Information Systems
can be defined as a numeric value that indicates how
trustworthy an agent is, allowing others to consider
this value to decide whether or not to interact with
(Noor et al., 2013).
Some characteristics according to (Firdhous et al.,
2012) are common to any trust definition, they are:
helps to manage uncertain and high risk environ-
ments; it is used as a basis for decision-making; based
in objective or subjective source; based in historical
performance and past experience; context sensitive
and dynamic over time.
In a VO environment, the trust assessment is
guided by some requirements, such as: direct re-
lationship (non symmetric), subjective (feedbacks)
and/or objective (past performance) basis, automated
management and trust is dynamic over time and new
observations (Winkler et al., 2007).
The reputation concept is closely related to trust,
and can be used in VO environments to assess confi-
dence from members to cloud providers (Bilecki and
Fiorese, 2016). Thus, reputation is defined as a collec-
tion of feedbacks about an object, character, or related
to attributes of an entity (reliability, capability and us-
ability) (Resnick and Zeckhauser, 2002). Therefore,
in a VO and CC integration environment, reputation
should be based on objective indicators (Bilecki and
Fiorese, 2016) and subjective indicators (feedbacks)
(Noor et al., 2013).
Nevertheless, some threats on reputation systems
are described in literature, and these systems should
be robust to cope with the attacks. There are many
attacks types (Jøsang and Golbeck, 2009), however in
the VO and CC integration, some are considered, such
as: (i) Feedback Collusion: Set of fake feedbacks that
aims to maximize or decrease the reputation of an
entity; (ii) Unfair Rating: A VO’s member provides
malicious ratings to the CC provider, in order to in-
crease or decrease reputation level. In this attack, the
attacker can have a disproportionately influence over
computed reputation.
2.3 Related Work
With the prominent integration of CC as a form of
VO’s infrastructure, some contributions related to
trust assessment and management, should be ana-
lyzed as related work.
PathTrust (Kerschbaum et al., 2006), is a central-
ized reputation system applied to the search and se-
lection step during the VO’s formation. To partic-
ipate in the VO formation process, a member must
register in an Enterprise Network (EN). At the VO’s
dissolution stage, each member sends his feedbacks
regarding the performed transaction with other mem-
bers. Thus, the utilization of a reputation system in
VO’s provides non-monetary benefits. Conversely, it
provides a means to ensure a better or more reliable
service (Kerschbaum et al., 2006).
In (Arenas et al., 2010), a centralized reputation
system has been applied to a VO, which uses the Grid
Computing infrastructure. The reputation of each
user is evaluated according to the use of provided re-
sources and jobs, and the user evaluates the quality of
service provided by the service provider. Otherwise,
a reputation algorithm is applied in VO’s creation us-
ing cloud resources, where the resources are selected
according to cloud provider’s reputation (Pan et al.,
2013). However, the algorithm does not cover all the
VO’s life cycle stages.
In (Mashayekhy and Grosu, 2012) a VO’s forma-
tion mechanism based on reputation of Grid Service
Provider’s (GSP) is presented. This mechanism acts
in the integration between VO and Grid Computing,
where a key element is the GSP’s reliability regarding
the delivery of promised resources. The GSP reputa-
tion’s is based on their past interactions, to evaluate
how likely is to provide the requested resources. In
some cases, a GSP agrees to participate in VO, but it
fails to deliver the promised resources, affecting the
operation step.
Stochastic Reputation Service for Virtual Organi-
zations (STORE) (Haller, 2008) is a reputation system
in order to assist the decision-making process existent
in the VO’s creation. The trust indicator, used as repu-
tation model, represents an aggregation of a hierarchi-
cal indicators composed by financial, operational, or-
ganizational, externals, and third-party indicators that
reflects the organizational’s performance.
Despite the small sample of related work, it is
possible to note that although one address the issue
of VO’s integration with CC, none of them provides
means to attend the several stages of the VO’s life-
cycle, and, few studies address the issue of trust and
reputation applied to VO’s integration with CC.
Thus, this work intends to cover the existent lack
of trust in the VO and CC integration, providing a
trust reputation architecture, where reputation is com-
posed by two trust sources (objective and subjective)
and this reputation will help in the several decision-
making process existent in VO’s life-cycle.
The emergence of new ICT concepts, such as cloud
computing, brought a new approach to VO and its op-
eration. The cloud computing provides computational
resources and the VO uses these resources to deliver
A Trust Reputation Architecture for Virtual Organization Integration in Cloud Computing Environment
its service in a distributed manner, geographically dis-
persed, thereby facilitating the collaboration process.
Hence, to build a trust indicator for reputation in a
VO and CC integration environment, the cloud com-
puting providers quality of service (QoS) properties
are exploited. In this integration scenario, the QoS
indicators represent an important role in trust assess-
ment, because the VO’s members are using the CC
resources to make available their systems and attend
the collaboration opportunity.
In order to assess and manage the trust in this sce-
nario, the reputation concept can be applied, aiming to
cover all stages in the VO’s life-cycle. Therefore, in
the proposed architecture, depicted in Figure 1, data
and reputation requests will be managed through a
centralized approach.
The reputation architecture comprises four mod-
ules, namely: monitoring module, data repository,
aggregation module and reputation broker service
(RBS). The Monitoring Module is responsible for
monitoring and updating QoS indicators of each CC
provider, during the VO’s operation stage. The Data
Repository stores historical and current values for
QoS indicators and VO’s members feedbacks. The
Aggregation Module is responsible for calculating the
reputation for CC providers based on QoS indicators
and users feedbacks. The RBS module is the inter-
face through which communication occurs with oth-
ers members. For example, the VO’s member can
send its feedback regarding a provider to RBS, or may
request a cloud provider’s reputation, at any time, to
3.1 Aggregation Module
Reputation and trust systems are used in different
scenarios to assist someone in the choice of some-
thing that is reliable. In most e-commerce reputation
and trust systems, the trust is based on the feedbacks
given by consumers. Thus, in the VO environment us-
ing cloud resources, other sources of trust should be
considered too, such as SLA, QoS indicators, among
others, along with consumer feedbacks (Noor et al.,
Taking this into account, the main objective of
aggregation module is calculate the cloud computing
providers’ reputation, based on two sources of trust:
objective and subjective.
The objective trust indicator is related to QoS indi-
cators that reflect the performance of cloud computing
provider. In this work, objective trust is composed of
availability (A), response time (RT), security (S), sta-
bility (E) and cost (C) indicator (Garg et al., 2013).
The subjective trust is composed of the feed-
backs provided by VO’s members to cloud computing
providers. During the VO’s dissolution or operation,
a VO’s member sends its feedback related to QoS pro-
vided by its cloud computing provider.
During the reputation calculation, current objec-
tive values monitored by the monitoring module can
be also used together with the historical ones to take
a current reputation snapshot.
Thus, the reputation value R
of a cloud comput-
ing provider s, according Equation 1, is defined as
a combination of objective and subjective trust indi-
cators by means of a weighted sum, where ω
ob j
are the weights defined by the VO’s manager,
ob j
(s) and T
(s) are the objective and subjective
trust indicators, respectively.
= ω
ob j
ob j
(s) + ω
(s) (1)
3.1.1 Objective Trust
This section proposes a objective trust assessment
methodology for cloud computing provider, using the
aforementioned QoS indicators, where these indica-
tors are independent of cloud service model.
The objective trust (T
ob j
(s)), represented by Equa-
tion 2, is calculated by two approaches: analyzing the
efficiency (E f f (s)) of cloud providers based on the
historical QoS data and scoring the QoS indicators by
means of a multi-criteria approach (MC (s)).
ob j
(s) = E f f (s) MC (s) (2)
The cloud providers’ (CP) efficiency is calculated
by the Data Envelopment Analysis (DEA) method.
The DEA is a nonparametric method that calculates
the relative efficiency of multiple decision-making
units (DMUs), where DMU refers an entity capable
to convert multiple inputs to outputs (Banker et al.,
Therefore, for DEA application, the input and out-
put should be modeled and are related to the QoS indi-
cators. Thus, the output (O
k j
) for each cloud provider
(k) as a DMU, represented by Equation 3, is under-
stood as the average of historical data (VO’s participa-
tions) for each QoS indicator ( j). Moreover, the stan-
dard deviation is considered due to a possible fluctua-
tion in CP’s past performances.
k j
= H
k j
+ σ(H
k j
) (3)
The input data set (I
) is composed of the aver-
age of estimated values, by a linear regression from
the QoS indicators (i) historical ones for each cloud
provider (k). To generate the estimated values, the
linear regression is used, taking the first and second
ICEIS 2017 - 19th International Conference on Enterprise Information Systems
Possible replacement of cloud providers
Figure 1: Conceptual Model Involving the Reputation Architecture.
historical values from QoS indicators and predicting
the third, and so on, until the n-nth historical partici-
pation. This approach is necessary to verify the CP
behavior at future VO’s participations. Equation 4
represents the input calculated for each indicator us-
ing the aforementioned method.
= X
) (4)
Thus, after the input and output data set definition,
the efficiency can be calculated. The efficiency is cal-
culated solving a DEA model through linear program-
ming. In this context, the DEA-BCC model output-
oriented (Banker et al., 1984) will be used, because
the output values are independent from the estimated
Also, the proposed objective trust also considers
a multi-criteria trust (MC (s)), understood as a score
which represents the importance assigned by the VO’s
manager to the QoS indicators. To calculate MC (s),
the comparison matrix of the Analytical Hierarchy
Process (AHP) multi-criteria method is used.
To determine the weights of indicators the Saaty’s
scale is used (Saaty, 1990). This scale is composed by
degrees of importance (from 1 to 9), where for exam-
ple, 9 represents the largest discrepancy between the
indicators. Through this scale, a judgment matrix is
created to perform a pairwise comparison of the QoS
indicators and by means of some normalization and
averaging processes result in the weight of each indi-
Therefore, multi-criteria trust (MC (s)), presented
in Equation 5, is calculated by multiplying the nor-
malized average historical QoS indicator values (A,
RT , S, E, and C) by their corresponding weights
MC (s) = (w1 A) + (w2 RT ) + (w3 S) +
(w4 E) + (w5 C)
3.1.2 Subjective Trust
The subjective trust assessment of a CP exploits
users’ ratings about the QoS provided. This subjec-
tive source was adapted from (Noor et al., 2013) and
these ratings are collected and stored by the proposed
Each VO’s member gives its feedback, during
VO’s dissolution or operation stage, about the transac-
tion performed with a CP. The feedback is composed
by a set of ratings ranging from 0 to 5, to each one
of the QoS indicators. The subjective trust assess-
ment of a CP s made by a VO’s member c is seen
as Q
(c, s) and it is understood as a weighted sum of
all QoS ratings, where the weights are calculated ac-
cording the multi-criteria trust approach (see more in
Subsection 3.1.1).
Then, the subjective trust of a CP s, is calculated
as a ratio between the subjective assessments Q
(c, s)
given by the VO’s members c to a CP multiplied
by the credibility factor (C
(c, s)), and the total of
subjective assessments (|V (s)|). Thus, the subjective
trust is represented by Equation 6, in which n refers
to the VO’s members who evaluated a CP s.
(s) =
(c, s) C
(c, s)
|V (s)|
The credibility factor allows to identify some mis-
leading feedbacks from attacks (Noor et al., 2013).
Particularly at the VO’s dissolution stage, the subjec-
tive trust should be evaluated, analyzing the credibil-
ity of the feedbacks’ set provided by a VO’s mem-
ber. The attack types presented in Section 2.2 are
A Trust Reputation Architecture for Virtual Organization Integration in Cloud Computing Environment
considered in the credibility analysis, where D(s) and
U(c,s), respectively, represent the feedback density
(collusion) and unfair rating attack.
Then, the credibility factor, presented by Equa-
tion 7, is understood as the average of these attack
factors multiplied by their weights (ρ and ) deter-
mined by the VO’s manager.
(c, s) =
(ρ D(s)) + ( U(c,s))
The feedback density (D(s)), exposed in Equa-
tion 8, aims to address the scenario where VO’s mem-
bers give numerous feedbacks to manipulate the sub-
jective trust (Noor et al., 2013). This factor consists of
the feedback mass M(s), which denotes the total num-
ber of VO’s members who gave feedback to a CP s,
|V (s)| represents the total number of feedbacks given
to a CP s, and L(s) represents the feedback collusion
D(s) =
|V (s)|L(s)
The feedback collusion factor (L(s)), identified by
Equation 9, aims to reduce the credibility of those
VO’s members who send multiple feedbacks to the
same CP (Noor et al., 2013). This factor is calculated
as the ratio of the number of feedbacks given by VO’s
members |V
(c, s)|, who gave more feedbacks than
specified in volume collusion threshold e
(s), defined
by VO’s manager, over the total of feedbacks received
by that CP s.
L(s) = 1 +
|V (s)|
(c, s)|
Lastly, regarding unfair rating attacks, malicious
VO’s members give several misleading feedbacks, in
a period of time to promote or prejudice the subjec-
tive trust of a CP s. This attack can be identified by
applying a K-Means clustering algorithm, on all his-
torical feedbacks to form K clusters, and the centroid
of the most densely populated cluster is called major-
ity cluster (M) (Malik and Bouguettaya, 2009). Then,
the unfair rating factor (U(c, s)), is understood as the
euclidean distance between M and the reported rat-
ings (V
), where σ is the standard deviation, and n are
the total number of ratings for a VO’s member c.
U(c, s) =
, if
(M V
< σ
, otherwise
An experiment scenario simulating VO and CC inte-
gration was developed to evaluate the proposed work.
This scenario, depicted in Figure 2, was built in a P2P
network simulator, called PeerFactSim.KOM (Stingl
et al., 2011). Thus, different network nodes are cre-
ated to represent each element presents in the pro-
posed architecture. The scenario concerns the ex-
change of messages and operations between VO’s
members and the proposed architecture, allowing to
evaluate trust assessment and reputation provided.
Monitoring Node
CP2 CP3 CP n
Architecture Node
Cloud Provider Node
Enterprise Node
Figure 2: Experimental Scenario.
The following are the node types developed:
(i) Architecture Node (ARQ): receives messages des-
tined to architecture, such as feedback ratings
send operation, request reputation list, request
cloud provider’s reputation, and monitoring ac-
tions. This node has implemented the function-
ality of Reputation Broker Service (RBS);
(ii) Enterprise Node (En): it is a VO’s member in an
organized VO as a logical ring topology;
(iii) Cloud Provider Node (CP): represents the cloud
providers offering their services to the VO;
(iv) Monitoring Node: perform the QoS Monitoring of
each CP.
During the simulation step, the following parame-
ters were used: VO’s duration (simulation time 10080
min), ten cloud providers, ten historical participa-
tions, ten simulation rounds and, the weights for
each QoS indicator are defined as: A (0.3830), RT
(0.2317), E (0.1861), S (0.1350), and C (0.0642), ob-
tained by means of multi-criteria trust assessment.
Thus, in the following sections the experimental re-
sults are presented.
4.1 Reputation
The reputation of a cloud provider’s s uses the his-
torical objective and subjective trust according to the
ICEIS 2017 - 19th International Conference on Enterprise Information Systems
methodology proposed in the Section 3.1.
In this sense, to compose the historical objective
CP’s data, random values were generated to each one
QoS indicator. The values were generated through a
linear distribution, and the average values of ten past
participations are presented in Table 1, where each
column refers to QoS indicators.
Table 1: QoS Indicators values.
Provider A RT E S C
Rackspace 0,9648 99ms 62 3 $ 0,75
GreenGeeks 0,9439 647ms 58 4 $ 0,65
Dot5Hosting 0,9188 114ms 57 7 $ 0,53
Cari 0,9683 20ms 59 8 $ 0,79
JustCloud 0,8924 535ms 64 7 $ 0,84
GoGrid 0,5743 250ms 36 5 $ 0,32
ElephantDrive 0,5604 869ms 67 7 $ 0,64
GoDaddy 0,7809 691ms 12 2 $ 0,52
EpmSolutions 0,4959 332ms 33 9 $ 0,48
AgileIT 0,6370 696ms 49 8 $ 0,64
Then, the historical subjective CP’s data was
based in the dataset presented in (Noor et al., 2013).
In the dataset was performed a pre-processing step
with the purpose of extract ten subjective evaluations
from ten cloud providers, where each one subjective
evaluation is composed by a ratings set, comprising
values in a scale from 0 to 5, for each one QoS indi-
Afterwards the objective and subjective trust data
are defined, a VO composed by ten business partners
and five cloud providers was simulated, with the pur-
pose of presenting the updated CP’s reputation after
a new VO’s participation. In this sense, to calculate
the reputation, weights are defined as 0.85 and 0.15,
respectively, to objective and subjective sources.
Therefore, the reputation of each cloud provider is
depicted in Figure 3, and it is calculated considering
subjective trust with credibility analysis (Reputation
W/ F. Cred.) and disregarding credibility (Reputation
W/O F. Cred.).
rackspace greengeeks dot5hosting cari agileit
Cloud Provider’s Reputation
Reputation W/O F. Cred.
Reputation W/ F. Cred.
Figure 3: Cloud Provider’s Reputation.
It is possible to note that some of the cloud
providers (rackspace, dot5hosting, and cari) have
higher reputation values, because they present best
past performances and they are best subjectively as-
sessed. Otherwise, when subjective credibility fac-
tor was not considered, the CP’s reputation dispropor-
tionately increases, under malicious attack, regarding
to real behavior.
4.2 Architecture Evaluation
The average time for each architecture operation dur-
ing VO’s operation stage was analyzed in architecture
evaluation. The analyzed operations were reputation
of a CP, subjective assessment of a VO’s member to a
CP, and the QoS monitoring.
In this sense, the simulation are performed con-
sidering business partners (VO’s members) varying
from 5 to 25 to verify the scalability and the afore-
mentioned operations are uniformly distributed over
simulation time. Thus, Figure 4 presents the results
of simulation.
5 10 15 20 25
Total of Business Partners
Average Response Time (ms)
Send Feedbacks
Figure 4: Average Response Time.
It is possible to note that send feedback operation
is tightly coupled to the number of business partners.
The monitoring operations, which consists of mes-
sages carrying data about QoS being exchanged be-
tween monitoring node, cloud providers and the arc
node. Therefore, one can conclude that monitoring
operation, according to the presented approach, con-
sumes most of the time during VO’s operation, and
increases in relation to the number of business part-
ners. This assumption is due to the fact that when
there are more business partners, consequently more
monitoring actions will be carried out.
This paper presented a trust reputation architecture
applied to a VO which uses CC resources. The pro-
posed architecture allows to assist the VO’s manager
during the decision-making processes of the VO’s
life-cycle. It was also presented a proposal to address
the trust issue existent in this integration.
A Trust Reputation Architecture for Virtual Organization Integration in Cloud Computing Environment
The proposed architecture presents a centralized
approach composed of four modules: the Aggrega-
tion Module, Monitoring Module, Reputation Broker
Service and Data Repository. The CP’s reputation is
calculated by the aggregation module, using two trust
sources: objective and subjective.
For the evaluating purpose, a simulation environ-
ment was developed in PeerFactSim.KOM, compris-
ing the architecture and the VO’s elements (cloud
providers and enterprises). The achieved results
demonstrate that this work provided a promising way
to deal with the attack types during reputation calcu-
lation. Moreover, the presented average time results
show that the reputation architecture can be used dur-
ing the VO’s operation, given the trust importance in
this context.
Finally, future works include to analyze other as-
pects, such as scalability feedback optimization, test-
ing of objective trust using historical data generated
by other probability distributions, and a objective re-
ward and penalty mechanism.
The authors would like to thank to UDESC PROMOP
financial programme as well as to LabP2D.
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