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Knowl Inf Syst (2010) 25:185–208

DOI 10.1007/s10115-009-0263-6

REGULAR PAPER

Resource service optimal-selection based on intuitionistic

fuzzy set and non-functionality QoS in manufacturinggrid system

Fei Tao · Dongming Zhao · Lin Zhang

Received: 25 October 2008 / Revised: 4 September 2009 / Accepted: 28 September 2009 /

Published online: 27 October 2009© Springer-Verlag London Limited 2009

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,

100191 Beijing, People’s Republic of China

e-mail: [email protected]

F. Tao

Hubei Digital Manufacturing Key Laboratory, Wuhan University of Technology,

430070 Wuhan, People’s Republic of China

D. Zhao

Department of Electrical and Computer Engineering, The University of Michigan-Dearborn,

Dearborn, MI 48128-1491, USA

123



186 F. Tao et al.

1 Introduction

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

123



Resource service optimal-selection 187

(1) Searching the qualified resource services according to the functionality requirements

of a task submitted by a user, and generating a candidate resource service set (CRSS),

namely, resource service match and search (RSMS) [31].

(2) Selecting the optimal resource service from CRSS to execute the task, namely, resource

service optimal-selection (RSOS).

A number of studies for addressing RSMS have been carried out in distributed system

such as computing grid system. The key technologies overcoming RSMS are searching

engine and matching algorithms. The searching engine makes use of matching algorithms to

retrieve related resource service according to user’s requirements. Some researchers investi-

gate RSMS from theperspectives of behavior signature[27], service community[23], context

ofservice [2,5,15,25], userexperience [13], role-based interactionmodel [7], met-interaction

[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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188 F. Tao et al.

Table 1 Summary of Web service selection

References Methods

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

123




is taken account into RSOS in MGrid system. Non-functionality QoS evaluation of resource

service is based on users’ feeling and transaction experiences using IFS. Furthermore, the

dynamic of non-functionality QoS is considered, and a time-decay function is introduced

into the evaluation of non-functionality. A new method for RSOS based on IFS and non-

functionality QoS are proposed, and the procedures are presented in detail. A practice caseis used to illustrate the developed method and procedure.

The paper is organized as follows: In Sect. 2, the framework for RSS is reviewed, and

the key role of RSOS plays in RSS is pointed out. A new method for addressing RSOS

problem in MGrid from the aspect of non-functionality QoS based on IFS is proposed in

Sect. 3. A case study based on proposed method is described in Sect. 4; and Sect. 5 discusses

the performance and advantages of the proposed method. Section 6 concludes the whole

paper.

2 Framework of resource service selection

The responsibility of RSS is to select the optimal resource service according to the require-

ments after analyzing the required resource QoS submitted by user. RSS mechanism and its

architecture in MGrid are shown in Fig. 2 [31]. The model is primarily based on two key

components: RSMS and RSOS. As mentioned in the introduction, RSMS is responsible for

searching the resource service according to the functionality requirements of task submit-

ted by user, and generating CRSS. However, RSOS is responsible for selecting the optimal

resource service from CRSS to execute the task. The brief working flows are described as

follows [31]:

(a) A user or RSD submits its request (i.e., a manufacturing task or a resource service

request) to MGTMS (MGrid task management system) via corresponding human-

machine interface of MGrid.

MGrid

Resource

ServicePublication

Center

Resource

ServiceInformation

Center (RSIC)

Parser

MTS:GeneralInfo

Inputs

Outputs

QoSPre-Conditions

MGrid Task

ManagementSystem

(MJTMS)

Candidate resource service set (CRSS)

result

Resourceenterprise

(RSP)

MGRS:

GeneralInfoInputs

Outputs

QoS

Pre-Conditions

MGRS:GeneralInfo

Inputs

Outputs

QoS

Pre-Conditions

MGRS:GeneralInfo

Inputs

Outputs

QoS

Pre-Conditions

Userenterprise

(RSD)

Word -matching algorithms

Number interval-matching algorithms

Trapezoidal f uzzy number -matching algorithms

Triangular f uzzy number -matching algorithms

Entity class-matching algorithms

Similarity MatchingAlgorithms (SMAs )

Basic-Matching

I/O-Matching

QoS-Matching

Integrated-Matching

Resource ServiceMatcher (RS-Matcher)

(a) ( b ) ( c )

( d )

( e )

(f)

(g)

(h)

(f)

(f)

RSMS

Resource service optimal -

selection (RSOS)

Fig. 2 Framework for resource service selection in MGrid [31]

123



190 F. Tao et al.

(b) MGTMS decomposes the task into several corresponding subtasks that cannot be

decomposed again, and submits them to system parser.

(c) Thesystem parser transfers thedecomposedtasks’ requirementsintostandardresource

service describing information, including general information, inputs information,

outputs information, QoS information, etc.(d–f) The system selects the corresponding resource services information from RSIC

(resource service information center). The system parser transfers them into cor-

responding general information, inputs information, outputs information, QoS infor-

mation, etc.

(g) The RS-Matcher (resource service matcher) matches the requested resource service

information with each advertising resource service extracted from RSIC by invoking

the component SMAs (similarity matching algorithms).

(g–h) The indices of the qualified resource services are recorded in CRSS. Then RSOS

selects the optimal resource service.

In MGrid resource service selection system, RSOS plays a very important role. It decides

the quality of the selected resource service and the QoS of whole MGrid. The key technolo-

gies and algorithms for realizing RSMS have been presented in the authors’ previous work

[31]. In this work, the component of RSOS implementation is emphasized.

3 RSOS based on IFS in MGrid

3.1 Symbols and notations

DA an arbitrary resource service domain

n the number of the resource services in DA, n = 1, 2, 3, . . .

M the number of candidate resource services, M = 1, 2, 3, . . .

RSm the mth candidate resource service, and m = 1, 2, 3, . . . , M

J the number of QoS criteria, J = 1, 2, 3, . . .

C QoS criteria set, C = (c1, c2, c3, . . . , c J )

c j the j th QoS criterion of a candidate resource service,

and j = 1, 2, 3, . . . , J

E m j (t k ) the integrated evaluation about c j of RSm at periods t k , and

E m j (t k ) is an IFS

t c the current time of the transaction

t 0 the earlier time of a transaction

t k time periods of k , k = 1, 2, 3 . . . , P

E m j the synthetic evaluation about c j of RSm from t 0 to t c, and E m jis an IFS

f (t k ) time decay function of E m j (t k ) in E m j from t 0 to t c

N the number of expert participating in evaluation of the

importance of QoS criteria, N = 1, 2, 3, . . .U i the i th expert, and i = 1, 2, 3, . . . , N

U iw(c j ) U i ’s evaluation of the importance of QoS criteria c j , and

U iw(c j ) is an IFS

wi (c j ) the weight of U iw(c j )

S(U iw(c j ), U i∗

w (c j )) the similarity between U iw(c j ) and U i∗

w (c j ), i ∗ = 1, 2, 3, . . . , N

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A(U iw(c j )) the average agreement degree of U iw(c j )

w j the weight of QoS criteria c j

W the weight of QoS criteria C , and W = {w1, w2, . . . , w j , . . . , w J }

V RSm the synthetic QoS evaluation score of RSm

3.2 Preliminaries on intuitionistic fuzzy set (IFS)

Fuzzy set theory, proposed by Zadeh [41], has been proved to be very effective in handling

the vagueness and uncertainty intrinsically existing in the knowledge possessed by people or

implied in numerical data [37,43]. For the sake of understanding in the following sections,

the basic concept of fuzzy set and IFS are introduced briefly in this section.

Definition 1 Let a set Z be fixed, a fuzzy set F is given by [41] as follows:

F = { x, μF ( x )| x ∈ Z } (1)

where

μF : Z → [0, 1], x ∈ Z → μF ( x ) ∈ [0, 1] (2)

and μF ( x ) denotes the degree of membership of the element x to the set Z .

Definition 2 Let a set Z be fixed, an IFS A in Z is given by [1] as an object having the

following form:

A = { x , μ A( x ), υ A( x )| x ∈ Z } (3)

where the functions

μ A : Z → [0, 1], x ∈ Z → μ A( x ) ∈ [0, 1] (4)

and

υ A : Z → [0, 1], x ∈ Z → υ A( x ) ∈ [0, 1] (5)

with the condition

0 ≤ μ A( x ) + υ A( x) ≤ 1, ∀ x ∈ Z (6)

μ A( x ) and υ A( x ) denote the degree of membership and the degree of non-membership of

the element x ∈ Z to the set Z , respectively. In addition, for each IFS A, if

π A( x ) = 1 − μ A( x ) − υ A( x ) (7)

then π A( x ) is called the degree of indeterminacy (i.e., the degree of uncertainly) of x to A,

or called the degree of hesitancy of x to A. Especially, if π A( x ) = 0, for all x ∈ Z , then IFS

A is reduced to a fuzzy set.

For every two IFSs A and B, the following relations and operations are valid [14,36]:

A ∩ B = { x, min (μ A( x ), μ B ( x )) , max (υ A( x ), υ B ( x ))| x ∈ Z } (8)

A ∪ B = { x, max (μ A( x ), μ B ( x )) , min (υ A( x ), υ B ( x ))| x ∈ Z } (9)

A ⊕ B = { x, μ A( x ) + μ B ( x ) − μ A( x)μ B ( x ), υ A( x )υ B ( x )| x ∈ Z } (10)

A ⊗ B = { x, μ A( x )μ B ( x ), υ A( x ) + υ B ( x ) − υ A( x )υ B ( x )| x ∈ Z } (11)

123



192 F. Tao et al.

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

λ and is defined as follows:

λ = (λ1 A1) ⊕ (λ2 A2) ⊕ · · · ⊕ (λi Ai ) ⊕ · · · ⊕ (λn An ) (16)

Apparently, the result of λ is still an IFS.

3.3 RSOS based on IFS

Let the system search out M candidate resource services (CRS) for a job T ask that cannot

be decomposed further using the method in [31] and the CRS set is RS = {RS1, RS2, . . . ,

RSm , . . . , RS M }. The non-functionality QoS criteria set of each resource service is defined

as C = (c1, c2, c3, . . . , c J ). The problem of RSOS in MGrid is to select the optimal one

from RS to execute T ask . In this work, users’ feeling is integrated into non-functionality QoS

evaluation of resource service using IFS. Based on the basic operators of IFS, a new method

for addressing RSOS problem based on IFS and non-functionality QoS is proposed in the

following subsections.

3.3.1 Evaluating c j of RSm at a time periods t k

Resource services in MGrid can be classified into two kinds: old resource service (ORS) and

new resource service (NRS). ORS is the resource service that has been invoked or used at

least one time. NRS is the resource service that be published recently and has never been

invoked or used since it was registered in MGrid.

It is assumed that there are n resource services existing in resource service domain

DA and RSm ∈ DA, where DA = {RS1, RS2, . . . , RSm−1, RSm , RSm+1, . . . , RSn }. Let

{( E 11 , E 12 , E 13 , . . . , E 1 J ), ( E 21 , E 22 , E 23 , . . . , E 2 J ) , . . . , ( E n1 , E n2 , E n3 , . . . , E n J )} be the current

evaluation of QoS criteria of the n resource services in DA. If RSm is a NRS in DA, the QoS

evaluation of RSm , E m

j, is set to be the original value, and

E m j =1

n − 1( E 1 j ⊕ E 2 j ⊕ · · · E m−1

j ⊕ E m+1 j · · · ⊕ E n j ) (17)

If RSm is an ORS, the evaluation values of its QoS criteria are calculated based on the

experiences of the users who transacted with it. There are two common ways to get initial

uncertainty values for an object from agent (or user, expert) according to [26]. In the first one

123




each agent makes a crisp evaluation of the proposition and then frequencies are computed for

a set of agents. In the second one, the agent directly assigns an uncertainty value (membership

value, probability, etc.). In this paper, the first one is used. Let N k be the total number of users

in MGrid who had a transaction with RSm during time periods t k , the number of the users

who made a positive and negative evaluation to c j of RSm

be N P and N N, respectively, thenumber of the users who did not make a evaluation be N E, and N P + N N + N E = N k . Then

the integrated evaluation, E m j (t k ), of the c j of RSm at periods t k can be represented with an

IFS as follows:

E m j (t k ) = { x, μ E m j (t k )( x ), υ E m j (t k )( x )| x ∈ Z } (18)

where

μ E m j (t k )( x ) = N p/ N k (19)

υ E m j (t k )( x ) = N N/ N k (20)

π E m j

(t k )( x ) = 1 − μ E Am j

(t k )( x ) − υ E m j

(t k )( x ) = N E/ N k (21)

For example, from August 1 to August 8, 2008, denoted as periods t k , there are 100 users

had transactions with RS1, of which 78 made a positive evaluation, 11 made a negative eval-

uation against c1 of RS1, then the integrated evaluation of c1 of RS1 at periods t k can be

expressed using an IFS (0.78, 0.11).

3.3.2 Calculating the synthetic evaluation of c j of RS

m

from t 0 to t c

In general, the evaluation of a user who has transaction with RSm should decay with time. For

example, in the last transaction at time t 0 (e.g., ‘20070101’), user U i ’s evaluation to trust of

RSm is very ‘trustworthy’, but at current time t c (e.g., ‘20080815’), U i ’s evaluation should be

decreased a certain extent when considering U i ’s evaluation while calculating RSm ’s trust.

Because U i and RSm has no transaction from t 0 to t c. Therefore, an adjustment function

should be employed when the system evaluates QoS criteria. Let f (t k ) be the time decay

value of E m j (t k ), and f (t k ) is expressed as follows:

f (t k ) = 1 − e−t k /θ , f (t k ) ∈ [0, 1], t k = t c − t k , θ = N date/1.95 (22)

where N date is the time required when the performance of a QoS criterion decays from

the best to the worst, e.g., the trust decays from the ‘highest trustworthy’ to the ‘highest

untrustworthy’. The time decay function curves according to different N date are shown in

Fig. 3.

Let f (t ) = ( f (t 1), f (t 2) , . . . , f (t k ) , . . . , f (t p)) and E m j (t ) = ( E m j (t 1), E m j (t 2) , . . . ,

E m j (t k ) , . . . , E m j (t P )), then the synthetic evaluation, E m j , about c j of RSm from t 0 to t c can

be formulated as

E m j = f (t ) E

m j (t )

= f (t 1) · E m j (t 1) ⊕ f (t 2) · E m j (t 2) ⊕ · · · ⊕ f (t k ) E m j (t k ) ⊕ · · · ⊕ f (t p) E m j (t p) (23)

According to Eq. (15)

f (t k ) E m j

(t k ) =

x,

1 −

1 − μ E m

j(t k )( x )

f (t k )

,

υ E m

j(t k )( x )

f (t k ) x ∈ Z

(24)

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194 F. Tao et al.

Fig. 3 Time decay function curves according to different N date ( N date = 30, 60, 90, 180, 360, 540

respectively)

By Eqs. (11), (16), and (24)

μ E m j= 1 −

pk =1

1 − μ E m

j(t k )

f (t k )

(25)

υ E m j =

pk =1

υ E m j (t k )

f (t k )

(26)

It can be easily verified that

0 ≤ 1 −

pk =1

1 − μ E m

j(t k )

f (t k )

+

pk =1

υ E m

j(t k )

f (t k )

≤ 1 (27)

0 ≤ 1 −

pk =1

1 − μ E m

j(t k )

f (t k )

≤ 1 (28)

0 ≤

pk =1

υ E m

j(t k )

f (t k )

≤ 1 (29)

Hence, E m j is still an IFS for any positive real number f (t k ). Therefore, by Eqs. (11), (16)

and (24), Eq. (23) can be rewritten as follows:

E m j =

x , 1 −

pk =1

1 − μ E m

j(t k )( x )

f (t k )

,

pk =1

υ E m

j(t k )( x )

f (t k )

x ∈ Z

(30)

3.3.3 Determining the weights of QoS criteria

The procedure to determine the weights of QoS criteria is organized into five steps based on

[9] and [20].

(1) Each expert U i uses the symbolic linguistic terms defined in Table 2 to evaluate the

importance of each QoS criterion c j . Let U iw = (U iw(c1), U iw(c2) , . . . , U iw(c j ) , . . . ,

U iw(c J )) be U i ’s evaluation of the importance of QoS criterion C of a resource service.

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Table 2 Linguistic terms for the

importance of a QoS criterion

[36]

Linguistic terms IFS

Very important [0.9, 0.1 − x]

Important [0.7, 0.3 − x]

Medium [0.5, 0.5 − x]

Unimportant [0.3, 0.7 − x]

Very unimportant [0.1, 0.9 − x]

It’s does not matter [0.0, 0.0 − x]

(2) This step calculates the similarity between any two experts’ (e.g., U i and U i∗

) eval-

uation (i.e., U iw and U i∗

w ) for each specific c j . Let the similarity between U i and

U i ∗

, Sim(U iw, U

i ∗

w ), be

Sim(U iw, U i∗

w ) = {S(U iw(c1), U i∗

w (c1)), S(U iw(c2), U i∗

w (c2) ) , . . . ,

S(U iw(c J ), U i∗

w (c J ))} (31)

According to Eq. (12)


w (c j )) = 1 −1

2(|μU iw (c j ) − μU i

∗w (c j )| + |υU iw (c j ) − υU i

∗w (c j )|) (32)

(3) This stepbuildsanagreement matrix, (AMc j ) N × N , forshowing eachsimilarity betweeneach pair of experts

(AMc j ) N × N =

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

1 S(U 1w(c j ), U 2w (c j )) · · · · · · S(U 1w(c j ), U N w (c j ))

S(U 2w(c j ), U 1w (c j )) 1 · · ·... S(U 2w(c j ), U N

w (c j ))

.

.

....

. . ....

.

.

....

S(U iw(c j ), U 1w (c j )) S(U iw(c j ), U 2w (c j )) · · · · · · S(U iw(c j ), U N w (c j ))

.

.

....

.

.

....

. . ....

S(U N w (c j ), U 1w(c j )) S(U N

w (c j ), U 2w(c j )) · · · · · · 1

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(33)

This step calculates the average agreement degree, A(U iw), for each single expert U i

A(U iw) = { A(U iw(c1)), A(U iw(c2) ) , . . . , A(U iw(c J ))} (34)

where

A(U iw(c j )) =1

N

N

i ∗=1


w (c j )) (35)

(4) Calculating the relative average agreement degree wi (c j ), i.e., the weight of U iw(c j ),

for each single expert U i .

wi (c j ) = A(U iw(c j )) N

i ∗=1 A(U i∗

w (c j ))(36)

123



196 F. Tao et al.

(5) Calculating the final weights of each QoS criterion c j . Let W be the weight vector of

QoS criteria C , and W = {w1, w2, . . . , w j , . . . , w J },where

w j = (U 1w(c j ) ⊗ w1(c j )) ⊕ · · · ⊕ (U iw(c j ) ⊗ wi (c j )) ⊕ · · · ⊕ (U N w (c j ) ⊗ w N (c j ))

(37)and

μw j= 1 −

N i=1

1 − μU i

w(c j )

wi (c j )

(38)

υw j=

N i=1

υU i

j(c j )

wi (c j )

(39)

3.3.4 Evaluating fuzzy synthetic of each CRS

The QoS performance rating matrix, QPRM, for all CRSs is shown as follows:

c1 c2 · · · c j · · · c J

QPRM = [ E m j ] =

RS1

RS2

...

RSm

.

..RS M

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

E 11 E 12 · · · E 1 j · · · E 1 J

E 21 E 22 · · · E 2 j · · · E 2 J

......

. . ....

. . ....

E m1 E m2 · · · E m j · · · E m J

.

..

.

...

. .

.

...

. .

.

.. E M

1 E M 2 · · · E M

j · · · E M J

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(40)

Then we get the weighted rating matrix, QPRMW, of RSm as follows:

c1 c2 · · · c j · · · c J

QPRMW = [V m j ] = [w j ⊗ E m j ] =

RS1

RS2

...

RSm

...

RS M

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

V 11 V 12 · · · V 1 j · · · V 1 J

V 21 V 22 · · · V 2 j · · · V 2 J

......

. . ....

. . ....

V m

1V m

2· · · V m

j· · · V m

J ......

. . ....

. . ....

V M 1 V M

2 · · · V M j · · · V M

J

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(41)

3.3.5 Calculating the closeness coefficient of each CRS

According to [36], define E + = { E +1 , E +2 , . . . , E + j , . . . E + J } and E − = { E −1 , E −2 , . . . ,

E − j , . . . , E − J } are the positive idea solution (PIS) and negative idea solution (NIS), respec-

tively, where

E + = ( E +1 , E +2 , . . . , E + J )

= {(max(μV m1), min(υV m1

)),(max(μV m2), min(υV m2

) , . . . , (max(μV m J ), min(υV m J

)} (42)

E − = ( E −1 , E −2 , . . . , E − J )

= {(min(μV m1), max(υV m1

)),(min(μV m2), max(υV m2

) , . . . , (min(μV m J ), max(υV m J

)}

(43)

123




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)

⎫⎪⎪⎬⎪⎪⎭

123



198 F. Tao et al.

then it illustrates that at time ‘20060301’, 121 users made positive evaluation of c1 of RS1,

31 users made negative evaluation, and 11 users did not make any evaluation. N P, N N and

N E on c2 and c3 of RS1 are 129, 29, 35 and 135, 18, 10, respectively. According to Eqs.

(17)–(20), at time ‘20060301’, the integrated evaluation of c1, c2 and c3 of RS1 with IFS are

(c1, 0.74, 0.19), (c2, 0.79, 0.18) and (c3, 0.82, 0.11), respectively

4 Case study

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,

c5, c6} = {Trust , Reliability, Availability, Scalability , Accuracy, Security}.

Step 1 Extracting the related data of RS1, RS2, RS3, RS4, RS5

The corresponding DRS about RS1, RS2, RS3, RS4, RS5 in database are as follows:

DRS1 =

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

( RS1, c1, ({99}, {16}, {4}), 20080520 ),

( RS1, c2, ({87}, {24}, {8}), 20080520 ),

( RS1, c3, ({103}, {14}, {2}), 20080520 ),

( RS1, c4, ({81}, {32}, {6}), 20080520 ),

( RS1, c5, ({87}, {23}, {9}), 20080520 ),

( RS1, c6, ({99}, {13}, {7}), 20080520 ),

( RS1, c1, ({132}, {25}, {8}), 20080620 ),

( RS1, c2, ({123}, {36}, {6}), 20080620 ),

( RS1, c3

, ({129}, {29}, {7}), 20080620 ),

( RS1, c4, ({97}, {45}, {23}), 20080620 ),

( RS1, c5, ({101}, {41}, {23}), 20080620 ),

( RS1, c6, ({121}, {23}, {21}), 20080620 ),

( RS1, c1, ({124}, {21}, {9}), 20080720 ),

( RS1, c2, ({126}, {23}, {5}), 20080720 ),

( RS1, c3, ({135}, {19}, {0}), 20080720 ),

( RS1, c4, ({111}, {34}, {9}), 20080720 ),

( RS1, c5, ({99}, {41}, {14}), 20080720 ),

( RS1, c6, ({124}, {23}, {7}), 20080720 ),

( RS1, c1, ({165}, {32}, {154}), 20080820 ),( RS1, c2, ({169}, {31}, {12}), 20080820 ),

( RS1, c3, ({179}, {28}, {5}), 20080820 ),

( RS1, c4, ({135}, {34}, {43}), 20080820 ),

( RS1, c5, ({159}, {32}, {21}), 20080820 ),

( RS1, c6, ({167}, {33}, {12}), 20080820 ),

⎫⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎬⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎭

123



200 F. Tao et al.

T a b l e 3

Q o S e v a l u a t i o n o f c a n d i d a t e r e s o u r c e s e r v i c e s

R S 1 ,

R S 2 ,

R S 3 ,

R S 4 , a n d R S 5

T i m e

C R S s

T r u s t c 1

R e l i a b i l i t y c 2

A v a i l a b i l i t y c 3

S c a l a b i l i t y c 4

A c c u r a c y c 5

S

e c u r i t y c 6

2 0 0 8 0 5 2 0

R S 1

( 0 . 8

3 ,

0 . 1

3 ,

0 . 0

3 )

( 0 . 7

3 ,

0 . 2

0 ,

0 . 0

7 )

( 0 . 8

7 , 0 . 1

2 ,

0 . 0

2 )

( 0 . 6

8 ,

0 . 2

7 ,

0 . 0 5 )

( 0 . 7

3 ,

0 . 1

9 ,

0 . 0

8 )

(

0 . 8

3 ,

0 . 1

1 ,

0 . 0

6 )

2 0 0 8 0 5 2 0

R S 4

( 0 . 7

2 ,

0 . 2

4 ,

0 . 0

4 )

( 0 . 7

8 ,

0 . 2

0 ,

0 . 0

2 )

( 0 . 9

4 , 0 . 0

6 ,

0 . 0

0 )

( 0 . 8

5 ,

0 . 1

0 ,

0 . 0 5 )

( 0 . 6

7 ,

0 . 1

5 ,

0 . 1

9 )

(

0 . 7

3 ,

0 . 1

9 ,

0 . 0

8 )

2 0 0 8 0 5 2 0

R S 5

( 0 . 8

8 ,

0 . 1

0 ,

0 . 0

2 )

( 0 . 8

2 ,

0 . 1

4 ,

0 . 0

4 )

( 0 . 9

2 , 0 . 0

5 ,

0 . 0

3 )

( 0 . 8

0 ,

0 . 0

8 ,

0 . 1 2 )

( 0 . 7

4 ,

0 . 1

5 ,

0 . 1

1 )

(

0 . 8

0 ,

0 . 1

3 ,

0 . 0

6 )

2 0 0 8 0 6 2 0

R S 1

( 0 . 8

0 ,

0 . 1

5 ,

0 . 0

5 )

( 0 . 7

5 ,

0 . 2

2 ,

0 . 0

4 )

( 0 . 7

8 , 0 . 1

8 ,

0 . 0

4 )

( 0 . 5

9 ,

0 . 2

7 ,

0 . 1 4 )

( 0 . 6

1 ,

0 . 2

5 ,

0 . 1

4 )

(

0 . 7

3 ,

0 . 1

4 ,

0 . 1

3 )

2 0 0 8 0 6 2 0

R S 2

( 0 . 6

5 ,

0 . 2

6 ,

0 . 0

9 )

( 0 . 6

1 ,

0 . 3

0 ,

0 . 0

9 )

( 0 . 7

4 , 0 . 1

7 ,

0 . 0

9 )

( 0 . 5

2 ,

0 . 1

7 ,

0 . 3 0 )

( 0 . 5

7 ,

0 . 3

0 ,

0 . 1

3 )

(

0 . 6

1 ,

0 . 3

0 ,

0 . 0

9 )

2 0 0 8 0 6 2 0

R S 3

( 0 . 7

9 ,

0 . 1

3 ,

0 . 0

8 )

( 0 . 6

9 ,

0 . 1

8 ,

0 . 1

3 )

( 0 . 9

0 , 0 . 0

8 ,

0 . 0

3 )

( 0 . 7

7 ,

0 . 1

3 ,

0 . 1 0 )

( 0 . 7

4 ,

0 . 1

0 ,

0 . 1

5 )

(

0 . 8

2 ,

0 . 1

3 ,

0 . 0

5 )

2 0 0 8 0 6 2 0

R S 4

( 0 . 6

9 ,

0 . 2

0 ,

0 . 1

1 )

( 0 . 6

9 ,

0 . 2

1 ,

0 . 1

0 )

( 0 . 7

7 , 0 . 2

0 ,

0 . 0

3 )

( 0 . 5

7 ,

0 . 2

0 ,

0 . 2 2 )

( 0 . 6

1 ,

0 . 1

7 ,

0 . 2

2 )

(

0 . 6

4 ,

0 . 3

0 ,

0 . 0

6 )

2 0 0 8 0 6 2 0

R S 5

( 0 . 8

8 ,

0 . 0

8 ,

0 . 0

4 )

( 0 . 9

1 ,

0 . 0

6 ,

0 . 0

3 )

( 0 . 9

3 , 0 . 0

6 ,

0 . 0

1 )

( 0 . 8

3 ,

0 . 0

6 ,

0 . 1 1 )

( 0 . 9

1 ,

0 . 0

8 ,

0 . 0

2 )

(

0 . 8

4 ,

0 . 1

2 ,

0 . 0

4 )

2 0 0 8 0 7 2 0

R S 1

( 0 . 8

1 ,

0 . 1

4 ,

0 . 0

6 )

( 0 . 8

2 ,

0 . 1

5 ,

0 . 0

3 )

( 0 . 8

8 , 0 . 1

2 ,

0 . 0

0 )

( 0 . 7

2 ,

0 . 2

2 ,

0 . 0 6 )

( 0 . 6

4 ,

0 . 2

7 ,

0 . 0

9 )

(

0 . 8

1 ,

0 . 1

5 ,

0 . 0

5 )

2 0 0 8 0 7 2 0

R S 2

( 0 . 5

7 ,

0 . 2

9 ,

0 . 1

4 )

( 0 . 6

3 ,

0 . 2

3 ,

0 . 1

4 )

( 0 . 7

3 , 0 . 2

0 ,

0 . 0

7 )

( 0 . 6

4 ,

0 . 0

9 ,

0 . 2 7 )

( 0 . 6

4 ,

0 . 1

6 ,

0 . 2

0 )

(

0 . 5

9 ,

0 . 1

4 ,

0 . 2

7 )

2 0 0 8 0 7 2 0

R S 3

( 0 . 9

1 ,

0 . 0

9 ,

0 . 0

0 )

( 0 . 9

3 ,

0 . 0

7 ,

0 . 0

0 )

( 0 . 9

5 , 0 . 0

5 ,

0 . 0

0 )

( 0 . 6

5 ,

0 . 1

6 ,

0 . 1 9 )

( 0 . 6

5 ,

0 . 1

4 ,

0 . 2

1 )

(

0 . 5

8 ,

0 . 2

3 ,

0 . 1

9 )

2 0 0 8 0 7 2 0

R S 4

( 0 . 7

2 ,

0 . 1

9 ,

0 . 1

0 )

( 0 . 7

3 ,

0 . 1

9 ,

0 . 0

8 )

( 0 . 8

1 , 0 . 1

7 ,

0 . 0

1 )

( 0 . 7

2 ,

0 . 1

5 ,

0 . 1 2 )

( 0 . 7

7 ,

0 . 1

7 ,

0 . 0

6 )

(

0 . 6

9 ,

0 . 2

5 ,

0 . 0

6 )

2 0 0 8 0 7 2 0

R S 5

( 0 . 9

0 ,

0 . 0

7 ,

0 . 0

3 )

( 0 . 9

1 ,

0 . 0

7 ,

0 . 0

2 )

( 0 . 9

4 , 0 . 0

6 ,

0 . 0

0 )

( 0 . 8

4 ,

0 . 1

0 ,

0 . 0 6 )

( 0 . 9

2 ,

0 . 0

7 ,

0 . 0

1 )

(

0 . 8

6 ,

0 . 1

0 ,

0 . 0

4 )

2 0 0 8 0 8 2 0

R S 1

( 0 . 7

8 ,

0 . 1

5 ,

0 . 0

7 )

( 0 . 8

0 ,

0 . 1

5 ,

0 . 0

6 )

( 0 . 8

4 , 0 . 1

3 ,

0 . 0

2 )

( 0 . 6

4 ,

0 . 1

6 ,

0 . 2 0 )

( 0 . 7

5 ,

0 . 1

5 ,

0 . 1

0 )

(

0 . 7

9 ,

0 . 1

6 ,

0 . 0

6 )

2 0 0 8 0 8 2 0

R S 2

( 0 . 7

3 ,

0 . 2

0 ,

0 . 0

6 )

( 0 . 7

7 ,

0 . 1

6 ,

0 . 0

6 )

( 0 . 8

9 , 0 . 1

1 ,

0 . 0

0 )

( 0 . 6

2 ,

0 . 3

2 ,

0 . 0 6 )

( 0 . 7

5 ,

0 . 1

6 ,

0 . 0

9 )

(

0 . 7

1 ,

0 . 1

6 ,

0 . 1

3 )

2 0 0 8 0 8 2 0

R S 3

( 0 . 4

7 ,

0 . 5

2 ,

0 . 0

1 )

( 0 . 4

1 ,

0 . 5

6 ,

0 . 0

2 )

( 0 . 7

1 , 0 . 2

3 ,

0 . 0

6 )

( 0 . 4

3 ,

0 . 4

8 ,

0 . 0 9 )

( 0 . 3

6 ,

0 . 5

2 ,

0 . 1

3 )

(

0 . 4

4 ,

0 . 4

9 ,

0 . 0

7 )

2 0 0 8 0 8 2 0

R S 4

( 0 . 8

9 ,

0 . 1

0 ,

0 . 0

1 )

( 0 . 8

4 ,

0 . 1

4 ,

0 . 0

2 )

( 0 . 9

2 , 0 . 0

7 ,

0 . 0

2 )

( 0 . 8

0 ,

0 . 1

7 ,

0 . 0 2 )

( 0 . 8

6 ,

0 . 1

1 ,

0 . 0

2 )

(

0 . 8

6 ,

0 . 1

0 ,

0 . 0

5 )

2 0 0 8 0 8 2 0

R S 5

( 0 . 8

3 ,

0 . 1

2 ,

0 . 0

5 )

( 0 . 7

9 ,

0 . 1

3 ,

0 . 0

7 )

( 0 . 8

7 , 0 . 0

8 ,

0 . 0

5 )

( 0 . 7

4 ,

0 . 1

5 ,

0 . 1 1 )

( 0 . 7

4 ,

0 . 1

3 ,

0 . 1

2 )

(

0 . 7

2 ,

0 . 1

4 ,

0 . 1

3 )

123




Table 4 QoS evaluation of candidate resource services RS1, RS2, RS3, RS4, RS5

CRSs Trust c1 Reliability c2 Availability c3 Scalability c4 Accuracy c5 Security c6

RS1 (0.97, 0.02) (0.96, 0.02) (0.98, 0.02) (0.90, 0.04) (0.92, 0.04) (0.96, 0.02)

RS2 (0.86, 0.08) (0.88, 0.07) (0.95, 0.04) (0.81, 0.05) (0.86, 0.05) (0.84, 0.05)

RS3 (0.92, 0.06) (0.91, 0.07) (0.97, 0.02) (0.81, 0.08) (0.78, 0.07) (0.81, 0.10)

RS4 (0.96, 0.02) (0.96, 0.03) (0.99, 0.01) (0.95, 0.02) (0.96, 0.02) (0.95, 0.03)

RS5 (0.99, 0.01) (0.98, 0.01) (0.99, 0.00) (0.96, 0.01) (0.98, 0.01) (0.97, 0.01)

Step 4 Determining the weights of each QoS criterion

The original evaluation of users for the importance of QoS criteria are shown in Table 5.

According to Eqs. (31)–(36), the relative weights of each QoS criterion of each user arecalculated and their results are shown in Table 6.

Table 5 The evaluation of users for the importance of QoS criteria

Trust c1 Reliability c2 Availability c3 Scalability c4 Accuracy c5 Security c6

U 1 (0.90,0.06,0.04) (0.90,0.10,0.00) (0.90,0.10,0.00) (0.30,0.50,0.20) (0.70,0.20,0.10) (0.70,0.20,0.10)

U 2 (0.70,0.20,0.10) (0.70,0.20,0.10) (0.90,0.10,0.00) (0.70,0.20,0.10) (0.90,0.10,0.00) (0.90,0.10,0.00)

U 3 (0.90,0.08,0.02) (0.90,0.10,0.00) (0.70,0.20,0.10) (0.90,0.10,0.00) (0.70,0.20,0.10) (0.70,0.20,0.10)

U 4 (0.50,0.20,0.30) (0.70,0.10,0.20) (0.90,0.10,0.00) (0.10,0.50,0.40) (0.90,0.10,0.00) (0.90,0.10,0.00)

U 5 (0.70,0.20,0.10) (0.70,0.20,0.10) (0.70,0.10,0.20) (0.00,0.00,1.00) (0.90,0.10,0.00) (0.30,0.50,0.20)

U 6 (0.50,0.20,0.30) (0.70,0.30,0.00) (0.90,0.00,0.10) (0.90,0.10,0.00) (0.50,0.50,0.00) (0.50,0.50,0.00)

U 7 (0.70,0.20,0.10) (0.90,0.00,0.10) (0.50,0.20,0.30) (0.70,0.20,0.10) (0.90,0.10,0.00) (0.90,0.10,0.00)

U 8 (0.90,0.10,0.00) (0.90,0.10,0.00) (0.90,0.10,0.00) (0.50,0.40,0.10) (0.50,0.30,0.20) (0.70,0.10,0.20)

U 9 (0.90,0.00,0.10) (0.70,0.10,0.20) (0.70,0.20,0.10) (0.00,0.00,1.00) (0.90,0.10,0.00) (0.70,0.20,0.10)

U 10 (0.90,0.10,0.00) (0.90,0.10,0.00) (0.90,0.10,0.00) (0.50,0.50,0.00) (0.30,0.50,0.20) (0.90,0.10,0.00)

Table 6 The weight of each user to each QoS criterion

Trust c1 Reliability c2 Availability c3 Scalability c4 Accuracy c5 Security c6

U 1 0.1024 0.1026 0.1048 0.1137 0.1057 0.1076

U 2 0.1046 0.1000 0.1048 0.1256 0.1071 0.1047

U 3 0.1027 0.1026 0.0995 0.1043 0.1057 0.1076

U 4 0.0881 0.0987 0.1048 0.0948 0.1071 0.1047

U 5 0.1046 0.1000 0.0981 0.0450 0.1071 0.0698

U 6 0.0881 0.0948 0.0995 0.1043 0.0893 0.0858

U 7 0.1046 0.0974 0.0796 0.1256 0.1071 0.1047

U 8 0.1024 0.1026 0.1048 0.1256 0.0923 0.1032

U 9 0.0999 0.0987 0.0995 0.0450 0.1071 0.1076

U 10 0.1024 0.1026 0.1048 0.1161 0.0714 0.1047

123



202 F. Tao et al.

According to Eqs. (37)–(39), the final weights of C = (c1, c2, c3, . . . , c J ) are as follows:

W = {w1, w2, . . . , w j , . . . , w J }

= (0.8125, 0.0000) , (0.8296, 0.0000) , (0.8341, 0.0000) , (0.5848, 0.0000) ,

(0.7922, 0.1753) , (0.7790, 0.1685)

According to Eq. (41), the QPRMW of RS1, RS2, RS3, RS4, RS5 is calculated and the

results are shown in Table 7.

Step 5 Calculating the closeness coefficient of each CRS

According to Eq. (42) and Eq. (43), the positive idea solution is

E + = { E +1 , E +2 , . . . , E + j , . . . E + J }

= {(max(μV m1 ), min(υV m1 )),(max(μV m2 ), min(υV m2 ) , . . . , (max(μV m6 ), min(υV m6 )}= {(0.80, 0.01) , (0.82, 0.01) , (0.83, 0.00) , (0.56, 0.01) , (0.78, 0.18) , (0.75, 0.08)}

and negative idea solution is

E − = { E −1 , E −2 , . . . , E − j , . . . , E − J }

= {(min(μV m1), max(υV m1

)),(min(μV m2), max(υV m2

) , . . . , (min(μV m6), max(υV m6

)}

= {(0.70, 0.08) , (0.73, 0.07) , (0.79, 0.04) , (0.47, 0.08) , (0.62, 0.24) , (0.63, 0.26)}

According to Eqs. (44)–(46), the similarities of RSm with E +and E −, and the closeness

coefficient are calculated and the results are shown in Table 8.

Step 6 Ranking the order of CRSs

Apparently, according to the result of V RSm in Table 8,

RS5 RS4 RS1 RS2 RS3

Table 7 QoS evaluation of candidate resource services RS1, RS2, RS3, RS4, RS5

CRSs Trust c1 Reliability c2 Availability c3 Scalability c4 Accuracy c5 Security c6

RS1 (0.78, 0.02) (0.80, 0.02) (0.82, 0.02) (0.52, 0.04) (0.73, 0.20) (0.75, 0.18)

RS2 (0.70, 0.08) (0.73, 0.07) (0.79, 0.04) (0.47, 0.05) (0.68, 0.22) (0.66, 0.21)

RS3 (0.75, 0.06) (0.76, 0.07) (0.81, 0.02) (0.47, 0.08) (0.62, 0.24) (0.63, 0.26)

RS4 (0.78, 0.02) (0.79, 0.03) (0.82, 0.01) (0.55, 0.02) (0.76, 0.19) (0.74, 0.19)

RS5 (0.80, 0.01) (0.82, 0.01) (0.83, 0.00) (0.56, 0.01) (0.78, 0.18) (0.75, 0.18)

Table 8 The final evaluation

result of resource services RS1

,RS2, RS3, RS4, and RS5

m d( E +, RSm ) d( E −, RSm ) V RSm

1 0.8800 0.6340 0.5813

2 0.6059 0.9081 0.4002

3 0.5838 0.9303 0.3856

4 0.9220 0.5921 0.6089

5 1.0000 0.5140 0.6605

123




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

123



204 F. Tao et al.

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-

0.90

0.92

0.94

0.96

0.98

1.00

1.02

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Recall

P

r e c i s i o n

QualifiedRate=10% QualifiedRate=30%QualifiedRate=50% QualifiedRate=70%

Fig. 5 Performance of the proposed method

123




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

123



206 F. Tao et al.

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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Author Biographies

Fei Tao is currently an associate professor in Beihang University

(Beijing University of Aeronautics and Astronautics) since April 2009.

He obtained his PhD from Wuhan University of Technology (WHUT),

China, in 2008. From 2007 to 2009, he worked as a research scholar

and postdoctoral researcher at University of Michigan-Dearborn, USA.

His research interests include manufacturing grid, distributed manu-

facturing system, intelligent optimization theory and algorithm, and

resource service management. He is author of about 20 journal and

conference articles of these subjects. He was awarded the Excellent

Doctoral Dissertation Fund of WHUT in 2007 and the Best Presen-tation Award at 2007 Doctoral Forum of China, and he served as the

Chairman of the Mechanical Manufacturing branch. Dr Tao was nom-

inated and elected to be a research affiliate of CIRP (The International

Academy for Production Engineering) in 2009.

Dongming Zhao is currently an associate professor in Electrical

and Computer Engineering at the University of Michigan-Dearborn,

Michigan. Prof. Zhao received his BSE, MSE in electronic informa-

tion engineering in Huazhong University of Science and Technology,Wuhan, China, and an MSEE from the University of Michigan, Ann

Arbor, Michigan, and PhD from Rutgers University, New Brunswick,

New Jersey. Prof. Zhao’s research interests include statistical model-

ing and pattern forecasting, IT distributed tasking and computing, 3-D

imaging, image processing, machine vision, and pattern recognition.

Lin Zhang received the B.S. degree in 1986 from the Department

of Computer and System Science at Nankai University, China. He

received the M.S. degree and the Ph.D. degree in 1989 and 1992 from

the Department of Automation at Tsinghua University, China, where he

worked as an associate professor from 1994. He served as the director

of CIMS Office, National 863 Program, China Ministry of Science and

Technology, from December 1997 to August 2001. From 2002 to 2005

he worked at the US Naval Postgraduate School as a senior research

associate of the US National Research Council. Now he is a full pro-

fessor in Beijing University of Aeronautics and Astronautics. He isan Editor of “ International Journal of Modeling, Simulation, and Sci-

entific Computing”, and “Simulation in Research and Development ”.

His research interests include integrated manufacturing systems, sys-

tem modeling and simulation, and software engineering.

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