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7/31/2019 [KAIS]_Resource Service Optimal-selection Based on Intuition is Tic Fuzzy Set and Non-functionality QoS in Manufa…
Abstract In manufacturing grid (MGrid) system, according to functional requirements
of a task, there exist a lot of resource services which have similar functional characteris-
tics. Multiple resource services with similar functional characteristics raise the concern over
resource service optimal-selection (RSOS). It is important to select the optimal resource
service according to their non-functionality characteristics or quality of service (QoS). How-
ever, QoS attributes are not easy to measure due to their complexity and involvement of
ill-structured information. In this study, user’s feeling is taken into account in RSOS in anMGrid system. The non-functionality QoS evaluation of resource services is based on users’
feeling and transaction experiences using intuitionistic fuzzy set (IFS). Furthermore, the
dynamics of non-functionality QoS is considered, and a time-decay function is introduced
into non-functionality QoS evaluation. A new method is proposed for RSOS based on IFS
and non-functionality QoS, and the procedures are presented in detail. A practice case study
is used to illustrate the proposed method and procedure. The performance and advantage of
the proposed method are discussed.
Keywords Manufacturing grid (MGrid) · Resource service optimal-selection (RSOS) ·
Non-functionality quality of service (QoS) · Intuitionistic fuzzy set (IFS)
F. Tao (B) · L. Zhang
School of Automation Science and Electrical Engineering, Beihang University,
Manufacturing grid (MGrid) utilizes grid technologies, information technologies, computer
and advanced management technologies overcome the barrier resulting from spatial dis-
tance in collaboration among different enterprises to make various manufacturing resources,including design resources, manufacturing resources, human resources, and application sys-
tem resources, to be fully connected [6,24,30]. In an MGrid system, various manufacturing
resources distributed in heterogeneous systems andin multiple sites canoffer numerousman-
ufacturing services to users in a transparent way by encapsulating and integrating resources
into different corresponding resource service templates. User can use all remote resources in
an MGrid system conveniently as if they are local resources [6,24,30].
MGrid has been widely researched and accepted [17,24,30]. Existing works on MGrid
primarily concentrate on its concept, architecture, application prototype system, and applica-
tion foreground [30]. The application fields of MGrid involve virtual manufacturing, die and
mould industry, aeronautical manufacturing, modern logistic, rapid manufacturing, equip-
ment support, engineering simulation, etc. [30]. The concept and connotation of MGrid
(including MGrid architecture, key technologies, research contents, technical driving forces,
and related works of MGrid), digital description of resource service (DDoRS), resource ser-
vice match and search, QoS modeling and evaluation, composition and optimal-selection
have been studied in detail in the authors’ previous works [30–33].
In a MGrid system, there are primarily two kinds of users [30]: (a) resource enterprise
or resource service provider (RSP), and (b) user enterprise or resource service demander
(RSD), as shown in Fig. 1. The former, RSP publishes its idle resource, product, manufac-
turing ability, and provides manufacturing resource service to meet user’s requirements. Thelatter, RSD searches the optimal manufacturing resource and service required, and selects
the corresponding partner to establish a collaboration manufacturing net.
One of the key technologies to realize resource service exchange in an MGrid is resource
service selection (RSS). There are two steps to realize RSS:
Fig. 1 Resource services
transaction between RSD and
RSP in MGrid [32]
Resource Service Demander
(RSD)
Resource Service Provider
(RSP)
MGrid Platform
Resource
service request
/manufacturing
task
Provideresource
service
Result
Result
R e q u e s t
R e s p o n s e
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7/31/2019 [KAIS]_Resource Service Optimal-selection Based on Intuition is Tic Fuzzy Set and Non-functionality QoS in Manufa…
[35], market-oriented [40], UML [44], expertise [8], and goal-based Web service discovery
with sophisticated semantic matchmaking [29], etc.
The above studies emphasize on resource service discovery from the aspect of function-ality requirements of a task. What is being ignored is the method that addresses how to select
the optimal resource service from CRSS generated by RSMS, i.e., the problem of RSOS. It
is expected that there exist hundreds of outsourced services with different QoS properties
that offer the same business function, and, as a result, the user faces the trouble of choosing
the optimal one among numerous candidate resource services (CRSs). For example, if a user
wants to search a ‘parametric design service’ for a product in an MGrid system, the RSMS
may find out a large number of (parametric design) services providing similar functional
characteristics, such as the service developed by SolidWorks, Unigraphics, Pro/Engineer,
and CATIA. Multiple services with similar functional characteristics introduce the problem
of RSOS. Users not only expect the selected resource services to meet functional aspects butthey also demand good quality of services such as reliability, security, and trust [20]. It is
therefore important to address the problem of RSOS in an MGrid system.
Existing research efforts on RSOS have been undertaken in the direction of QoS-aware
RSOS as shown in Table 1.
However, the methods in Table 1 are more suitable to be employed when QoS attributes
are functional properties, but they are not fitting well for evaluating QoS attributes which are
non-functional prosperities such as reliability, security, and trust. Compared with functional
QoS prosperities, non-functional QoS attributes are not easy to assess due to their complexity
and the involvement of ill-structured information. Furthermore, these approaches fall shot of addressing the following issues:
Problem 1 User’s feeling and transaction experiences are not considered during QoS eval-
uation of resource service. Although QoS in Web service discovery and selection attracts a lot
of attention, most current research emphasizes the objective and functionality QoS informa-
tion, which is offered by corresponding service providers. In such case, the QoS information
cannot respond to the feeling of users who actually use services. If users’ thought or evalua-
tion can be embedded into Web service selection, the search quality could be improved. This
kind of information is named non-functionality QoS information in this study.
Problem 2 The issue on how to combine time fact into QoS evaluation is not considered. The
QoS information considered in the above researches are treated to be static. They are given
out by the owner of resource service and never change with time. In fact, QoS of resource
service are dynamically changing with time and transactions results. Take the QoS criterion,
trust, for an example, if the trust of a resource service is ‘very trustworthy’ in last transaction
at ‘20080101’, and from then on, there was no transaction till current time, e.g., ‘20080831’,
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7/31/2019 [KAIS]_Resource Service Optimal-selection Based on Intuition is Tic Fuzzy Set and Non-functionality QoS in Manufa…
Menasce [22] Probability techniques are used to calculate the cost and execution time of
component Web services for five different execution scenarios, i.e.,probabilistic invocation, parallel invocation (fork), sequential activation,
fastest-predecessor-triggered activation, and synchronized activation (join).
The best composition of Web services are decided based on their optimum
combined score cost and execution time.
Lin et al. [19,20] A QoS consensus moderation approach (QCMA) is presented in order to realize
QoS evaluation on the basis of consumers’ consensus so as to alleviate the
differences on QoS requirements in the Web services discovery and selection.
Jaeger et al. [11,12] “The aggregation of QoS for service composition is defined by using a number
of pre-defined composition patterns, and a pattern-based QoS aggregation
mechanism for composite Web services is studied. The QoS aggregation is
used to verify that the candidate resource services satisfy the QoS requirement
for the required composite Web services. The concept of the composition
pattern is inspired by van der Aalst’s [34] Workflow Pattern” [20].
Liu et al. [21] Graph presentation of structural model is investigated and the problems of
component service selection and execution path selection are studied based on
structural model. The component service selection and execution path
selection problem is translated into a hierarchy optimization problem and
addressed by using colony system.
Zeng et al. [42] The problem of selecting Web service is addressed by maximizing user
satisfaction expressed as utility functions over QoS attributes.
Hu et al. [9] A decision model of QoS criteria, called DQos which consists of an extensible
QoS model, decision model and constraints, for evaluating Web services is
presented. Service selection is formulated as Multiple Attribute Decision
Making (MADM) problem that can be solved by using subjective weigh
mode, single weight mode, objective weight mode and subjective-objective
weight mode.
Lin et al. [18] The service selection problem is formalized as constraint satisfaction problem,
and ‘deep-first branch-and-bound ’ method with some adjustments is
employed to search the optimal solution for service composition.
Sirine et al. [28] In order to help users filter and select services while building the composition, a
goal-oriented and interactive composition approach is developed by using
matchmaking algorithms.
Dai and Wang [4] In order to maximize grid service reliability, a genetic algorithm is used to solvethe problem of optimally allocating services on the grid system.
then the trust may decay into ‘trustworthy’. Hence the dynamic of QoS should be considered
during QoS evaluation.
Problem 3 The issues associated with aggregating different service consumers’ and experts’
evaluation on the importance of each QoS criterion are not considered [20]. Different users
have different views on the importance of a QoS criterion, and although they may make the
same evaluation (e.g., very important ) to the importance to a QoS criterion, the definitions of these evaluation terms (e.g., very important ) may differ. Hence, it is important to aggregate
different users’ evaluation on the importance of each QoS criterion.
Motivated by addressing the above issues and realizing RSS, this paper emphasizes on
RSOS from the aspect of non-functionality QoS based on intuitionistic fuzzy set (IFS). In this
work, a RSS framework is proposed based on the authors’ previous work [31]. Users’ feeling
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Definition 3 The similarity between two IFSs A and B is defined as [10,38]
S( A, B) = 1 −1
2(|μ A( x ) − μ B ( x )| + |υ A( x ) − υ B ( x )|) (12)
Apparently, 0 ≤ S( A, B) ≤ 1.
Definition 4 Let λ ∈ [0, 1], A ∈ IFS, according to Eq. (11)
A2 = { x , (μ A( x ))2, 1 − (1 − υ A( x ))2| x ∈ Z } (13)
A3 = { x , (μ A( x ))3, 1 − (1 − υ A( x ))3| x ∈ Z } (14)
In general, the operator of multiplication between λ and A is defined as follows [14]:
λ A = { x ,
1 − (1 − μ A( x ))λ
, (υ A( x ))λ| x ∈ Z } (15)
Definition 5 Let λ = (λ1, λ2, . . . , λi , . . . , λn ), ∀λi ∈ [0, 1] and = ( A1, A2, . . . , Ai , . . . , An ), ∀ Ai ∈ IFS. For the usage of the later in this article, the operator ‘’ between
According to Eq. (12), the similarities of RSm with PIS (i.e., E +) and NIS (i.e., E −) are
defined as follows:
d( E +, RSm ) = 1 −1
2
J
j =1
(|μ E
+
j
( x ) − μV m
j
( x)| + |υ E
+
j
( x ) − υV m
j
( x )|) (44)
d( E −, RSm ) = 1 −1
2
J j =1
(|μ E − j( x ) − μV m j
( x)| + |υ E − j( x ) − υV m j
( x )|) (45)
According to [39], the final synthetic QoS evaluation scores of RSm are calculated as
follows:
V RS
m =d( E −, RSm )
d( E +, RSm ) + d( E −, RSm )(46)
3.3.6 Ranking the order of candidate resource services (CRSs)
Ranking all CRS RSm according to the closeness coefficients (i.e., synthetic QoS evalua-
tion),V RSm , the greater the value of V RSm , the better the RSm .
3.4 Data structure design
Data structure for recording resource service’s QoS evaluation in a RSD ( DRS): As statedearlier, at a time period t k , the QoS evaluation values of an ORS are calculated based on the
experiences of the users who transacted with it. Therefore, we design each entity of resource
service maintaining a table of data structure, which is responsible for recording the transac-
tion evaluation results, named DRS. Clearly, DRS is decided by the factors of time period,
the resource service, the specific QoS criteria, and the evaluation results from users. Hence,
DRS is defined as
DRS =
RS, C , E , T
(47)
where
• RS denotes the resource service set (RSS).
• C denotes the QoS criteria set of resource service,C = (c1, c2, c3, . . . , c J )
• E denotes the QoS criteria evaluation and E = ({ N P}, { N N}, { N E}), where N P, N N and
N E are the number of users who made a positive evaluation, negative evaluation, and did
not make a evaluation or have no idea to a specific QoS criterion c j ∈ C of a RSm ∈ R S
at a time period t k ∈ T , respectively.
• T stands for the set of transaction time periods.
For example, the data structure about RS1’s QoS evaluation is as follows:
DRSm =
⎧⎪⎪⎨⎪⎪⎩
(RS1 , c1, ({121}, {31}, {11}), 20060301) ,
(RS1 , c2, ({129}, {29}, {5}), 20060301) ,
(RS1 , c3, ({135}, {18}, {10}), 20060301)
⎫⎪⎪⎬⎪⎪⎭
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7/31/2019 [KAIS]_Resource Service Optimal-selection Based on Intuition is Tic Fuzzy Set and Non-functionality QoS in Manufa…
It is assumed a user (or RSD), RSD1, looks for a parameterized radial magnetic design ser-
vice on ourexperimental prototypeplatform,MBRSSP–MGrid [30]. The systemsearchesout
five resource services,RS1, RS2, RS3, RS4, RS5, that qualified for its functional requirements
using the search and match mechanism in [30]. In order to select the optimal resource service
to serve RSD1 and reduce the decision-making time for RSD1 to select the best resource
service, the system can evaluate the non-functionality QoS of the five resource services, andselect the optimal one from them based on the above-proposed method.
The six non-functionality QoS criteria selected in this case study are Trust, Reliability,
Availability,Scalability, Accuracy, and Security, which is defined as C = {c1, c2, c3, c4,
Thus, the user, i.e., RSD1, should better choose RS5 to execute the task, and then RS4,
RS1, RS2, and RS3 in sequence.
5 Performance analysis and discussion
5.1 Scalability and efficiency
In order to test the scalability (i.e., the relationship between the number of data and process-
ing time) of the proposed method, a set of experiments are conducted. In our experiments,
the number of CRSs for each task, i.e., N Candidate or M defined in Sect. 3.1, varies from 500
to 5,000 with an increment of 500, and the number of QoS (i.e., J defined in Sect. 3.1)
for each resource service changes from 4 to 20 with an increment of 4. The experiments
are implemented in MATLAB 7.4 on an AMD 2.2 GHz with 2.0G RAM under Microsoft
Windows XP. The algorithms were coded with MATLAB 7.4 and saved in a MATLAB file.Then it was executed in MATLAB 7.4 directly. The result of each test is an average of ten
executions. The summary of the results is shown in Fig. 4.
It can be concluded from Fig. 4 that, with the increase of the number of QoS criteria,
the entire processing time of the proposed method under the same amount of CRSs for each
task increases in a small extent. When the number of CRSs for each task is 500, and number
of QoS criteria is 20, i.e., N Candidate = 500 and J = 20, the entire processing time for the
proposed method is about 6 s. When N Candidate ≤ 2,000 and J ≤ 12, the entire processing
time for the proposed method is within 15 s. This solving scale suits the requirements of
most RSOS problems. Furthermore, most of the RSOS problems in MGrid are addressedby high-performance computer, which will shorten the processing time under the same data
scale sharply. Hence, the efficiency and scalability of the proposed approach is apparent.
5.2 Effectiveness
To validate the performance of the proposed method, in addition to the above case study, a
set of experiments are conducted. Recall and Precision, which are the standard measures that
have been used in information retrieval for measuring the accuracy of a search method or
search engine, are borrowed and selected as the criteria to test the accuracy of the proposed
method. But the specific meanings of Recall and Precision are different with that in [16].Let N Candidate be the number of all candidate resource resources for a task, N Qualified be the
0
10
20
30
40
50
60
70
500 1000 1500 2000 2500 3000 3500 4000 4500 5000
Number of candidate resource service, i.e., M
P r o c e s s i n g
t i m e ( s )
J=4 J=8 J=12 J=16 J=20
Fig.4 Scalability (i.e., relationships between thenumberof data andprocessing time) of theproposed method
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7/31/2019 [KAIS]_Resource Service Optimal-selection Based on Intuition is Tic Fuzzy Set and Non-functionality QoS in Manufa…
number of the resourceservices thatqualifies for the requirementsof the taskamong all candi-
date resource resources, N Front
N Front = 1, 2, 3, . . . and N Front ≤ N Qualified
be the number
of the resource services located in front in the recommend list generated by using the pro-
posed method, and N FrontQualified N Front
Qualified = 1, 2, 3 . . . and N FrontQualified ≤ N Font be the number
of the qualified resource service among N Front. In this paper, recall, Recall, and Precision,Precision, are defined as follows:
Recall = N Front
Qualified
N Qualified, Precision =
N FrontQualified
N Front
Let QualifiedRate be the ratio of N Qualified and N Candidate , and Quali f ied Rate =N Qualified
N Candidate.
In our experiments, the number of CRSs is set to 500 for each task, i.e., N Candidate = 500.
The QualifiedRate varies from 10 to 70% with an increment of 20% and Recall changes from
0.1 to 1 with an increment of 0.1. The parameters of each CRS are generated by computerwith regard to specific QualifiedRate. The precision of the proposed method under different
QualifiedRate and Recall is shown in Fig. 5, and the result of each test is the average of ten
executions.
It can be concluded from Fig. 5 that, when recall is under 0.4, the precision of the proposed
method almost close to 100%. It means that, when N Candidate = 500, N Qualified = 50 and
Recall = 0.4, the first 20 resource services located in front in the recommend list generated
by using the proposed method are qualified for use’s requirements. This resolving scale suits
the requirements of most RSOS problems in MGrid system. Hence, the effectiveness of the
proposed approach is apparent.
5.3 Comparison with the method of Bedi et al.’s [3]
The authors compared their proposed method with that of Bedi et al.[3] according to the
comments of the reviewer. At first the authors tried to compare the performance of the two
methods but failed. That is because the original data source is impossible to be used by the
two methods. The main differences are as follows.
Although both methods use IFS to represent the evaluation of criteria to a product, the
computing way and the data they used are totally different. In the authors’ proposed method,
the evaluation of criteria to a product (e.g., a resource service) is based on the evaluation orexperiences of inexhaustible users who transacted with it, as described in Sect. 3.3.1. How-
ever, in [3], the computing of the evaluation of criteria (i.e., trust) to a product is primarily
based on “preference list” and“uncertain list” using temporal ontology, which are maintained
by the recommender agent, as shown in the Sect. 3.3 in [3]. Apparently, the data source is
different and is impossible to be shared by the two methods.
Even though the authors can compare their performances by using different data sources,there is no meaning in fact. Because the authors cannot conclude that their method is better
than Bedi’s, or Bedi’s is better than the authors’ based on the comparison results generated
by different data source. Hence, the authors compared their proposed method with Bedi’s
from the aspect of procedure. Based on the comparison of the realize processes of the two
methods, the advantage of the proposed method we think are as follows:
• More users can be involved in theevaluation of a product, andtheir evaluation andtransact
experiences with a resource service can be considered during the evaluation. Compared
with the method in [3], it is easy to get the original evaluation to a criterion with IFS. We
can compare Eq. (19) in Sect. 3.3.1 and Eq. (1) in [3]. It is easy to maintain and record theevaluate data of users in the proposed method. In the authors’ proposed method, it only
needs to record the numbers of the users who made negative and positive evaluation of a
product. As the increment of users, the data size to be recorded remains the same. But in
[3], each recommender agent needs to maintain a ‘preference list’ and an ‘uncertain list’,
and the corresponding ontology must be developed and used. As the recommenders (or
recommender agents)increase, more data or bigger data size needs to be maintained.
• The evaluation of criteria to a product is more dynamic because of the introduction of
time–decay function during the evaluation process. In general, the evaluation of a user
who has transaction with a product or service should decay with time. For example, in
the last transaction at time t 0 (e.g., ‘20070101’), the evaluation of user U i to the trust of a product is very ‘trustworthy’, but at current time t c (e.g., ‘20080815’), U i ’s evaluation
should be decreased to a certain extent when considering U i ’s evaluation and when cal-
culating the product’s current trust degree, because U i and RSm have no transaction from
t 0 to t c. It is the same in real life. For example James told his good friend Jack that the
food of “Cherry Restaurant” is very good and cheap when he just finished his dinner. Jack
must believe it very much. But it is assumed that on that day, James moved to another city
for 2 years and he never visited “Cherry Restaurant” again. Two years later, when James
meets Jack,and tells Jack that the food of “Cherry Restaurant” is very good and cheap,
maybe Jack cannot believe it this time. Because we do not know what has happened to“Cherry Restaurant” during the 2 years.
6 Conclusions and future works
Resource service optimal-selection (RSOS) is the key in implementing a real-time MGrid.
Existing research on RSOS primarily has been undertaken in the direction of QoS-aware
RSOS. But the users’ feeling and transactions experiences are not considered in their RSOS
method. The dynamics of the QoS is not taken into account neither. This work proposed a
method for addressing RSOS in MGrid from the aspect of non-functionality QoS and IFS,which are very different from traditional methods. The primary works and contribution of
this paper are as follows:
(1) The evaluation methods for non-functionality QoS of resource service are proposed.
The non-functionality QoS evaluations of resource service are based on user’s feeling
and transaction experiences using IFS. Furthermore, the dynamic of non-functionality
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7/31/2019 [KAIS]_Resource Service Optimal-selection Based on Intuition is Tic Fuzzy Set and Non-functionality QoS in Manufa…
QoS are considered, and a time-decay function are introduced in non-functionality QoS
evaluation.
(2) A new method for RSOS based on IFS and non-functionality QoS are proposed, and the
procedures are presented in detail. A case study is presented to illustrate the application
of the proposed method. The performance analysis and comparison results indicate thatthe proposed method is excellent both on effectiveness and efficiency.
Further study is planned to investigate resource service composition based on IFS and
non-functionality QoS. Investigation for conflicts and failures detection and recovery during
RSOS is another recommended topic that can be explored.
Acknowledgements Thanks for thefinancial support of theExcellentDoctoral Dissertation Fund of WHUT
(Wuhan University of Technology). This paper is partly supported by Hubei Digital Manufacturing Key Lab-
oratory Opening Fund (No. SZ0621), National High-Tech. R&D Program of China (No. 2007AA04Z153),
and Key project of National Programs for Fundamental R&D of China (No. 2007CB310900). The idea of this
paper was formed at WHUT when the first author was a Ph.D student. The work was conducted at Universityof Michigan, Dearborn, US. The revisions of the paper were finished at Beihang University (i.e., Beijing Uni-
versity of Aeronautics and Astronautics), Beijing, China. Thanks for the help from Z. Zhang, a PhD student at
Beihang University, for discussing and checking the testing programs. We would also like to express our great
appreciation to the valuable comments made by three anonymous reviewers and the editors of this journal.
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