a trust service-oriented scheduling model for workflow applications in cloud computing

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IEEE SYSTEMS JOURNAL 1

A Trust Service-Oriented Scheduling Model forWorkflow Applications in Cloud Computing

WenAn Tan, Yong Sun, Ling Xia Li, GuangZhen Lu, and Tong Wang

Abstract—Cloud services have been utilized in large-scaledistributed environments. As an effective service aggregationmethodology, workflow technology has been used to constructcomposite services. Efficient and dependable workflow scheduling(WFS) is crucial for integrating enterprise systems. While WFShas been widely studied, WFS-related algorithms are mainly fo-cused on optimizing execution time or cost. However, in cloud com-puting environment, WFS is up against the threats of the inherentuncertainty and unreliability to the applications. Therefore, trustservice-oriented strategies must be considered in WFS. As a result,this paper proposes a trust service-oriented workflow schedulingalgorithm. The scheduling algorithm adopts a trust metric thatcombines direct trust and recommendation trust. In addition,we provide balance policies to enable users to balance differentrequirements, including time, cost, and trust. A case study wasconducted to illustrate the value of the proposed algorithm. Theexperimental results show that the proposed approach is effectiveand feasible.

Index Terms—Cloud computing, enterprise systems (ES), fuzzysets, industrial information integration engineering, trust, work-flow scheduling (WFS).

I. INTRODUCTION

ENTERPRISE SYSTEMS (ES) are being challenged byincreased complexity and growing needs for systems in-

tegration to meet manufacturing and service industry require-ments [1], [2]. Due to increasing competition and economicglobalization, ES are being required to integrate extendedenterprises in various industry-specific environments in thesupply chain environment [3], [4]. As a result, the integra-tion of various ES is being conducted in many large enter-prises [5]–[13].

Cloud computing, which aims to aggregate and share alarge pool of cloud services, is a promising technique for

Manuscript received December 18, 2008; revised February 7, 2013; acceptedFebruary 2, 2013. This work was supported in part by the National Natural Sci-ence Foundation of China under Grants 60874120, 61272036, and 71132008,by the Innovation Program of Shanghai Municipal Education Commissionunder Grant 11ZZ188, by the Changjiang Scholar Program of the Ministryof Education of China, and by the U.S. National Science Foundation underGrant 1044845.

W. Tan is with the Nanjing University of Aeronautics and Astronautics,Nanjing 210016, China, and also with the Shanghai Second Polytechnic Uni-versity, Shanghai 100044, China (e-mail: [email protected]).

Y. Sun and G. Lu are with the Nanjing University of Aeronautics andAstronautics, Nanjing 210016, China (e-mail: [email protected]).

L. X. Li is with the Old Dominion University, Norfolk, VA 23529 USA(e-mail: [email protected]).

T. Wang is with the Shanghai Second Polytechnic University, Shanghai100044, China.

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSYST.2013.2260072

systems integration [14]–[17]. So far, cloud computing has beensuccessfully used to integrate various services for governments,businesses, and industries. Cloud computing can be defined asa new style of collaborative environment in which dynamicallyscalable and virtualized resources are provided as a service overthe Internet [18], [19]. Due to the heterogeneous problems indata formats, structures, and semantics, it is hard to integratecloud services into a composite service for supporting collabo-rative business process [20], [21].

Typically, the service workflow is deployed as the aggrega-tion of services to facilitate the requirements and automationof large-scale distributed systems [22]–[25]. A workflow ap-plication has become a standard solution for managing com-plicated processes in many business organizations [26]–[33].These workflow applications consist of multiple tasks whichcan be executed by a series of similar cloud services at differentlevels of quality. Workflow scheduling (WFS) is defined asthe allocation of workflow tasks to suitable cloud services. Asan effective service method for integration, WFS has attracteda lot of attention from both researchers and practitioners inrecent years. A number of scheduling algorithms have beendeveloped to satisfy industrial requirements in many large-scalesystems. Currently, most of these algorithms are focused onminimizing their execution time or cost on commercial cloudservice platforms.

However, most of the existing commercial cloud services areowned and operated by geographically distributed and hetero-geneous organizations and enterprises in a cloud computingenvironment [34]. The inherent uncertainty and unreliabilityof large-scale distributed systems often pose threats to theoperation of workflow applications [35]–[37]. For example,cloud services sometimes may be offline unexpectedly due topower outage or other reasons.

Scheduling complex tasks on dependable services becomeseven more important in large-scale network [38]. As theautonomous requirement of each participating service provider,the network is a failure-prone environment. Different serviceproviders without interaction before may cause failure to theexecution of a composite service [39]. Trust between serviceproviders is essential to collaborative business process [40]. Tobe effective, WFS needs to consider the trust factor in additionto time and cost factors. Optimizing both time and cost for thetrust service-oriented workflow scheduling (TWFS) model isan NP-hard problem. WFS usually requires certain policies likeLOSS, GAIN, and deadline-Markov Decision Process (MDP)to balance different and conflicting requirements such as time,cost, and trust at the same time in the cloud environment[48]–[50].

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In this paper, we develop specific scheduling strategies toeffectively optimize time, cost, and trust factors in ES. In orderto address the problem of WFS in an optimal and reliable way,a TWFS model is proposed to meet the requirements of ESintegration.

The rest of this paper is organized as follows. We discuss therelated work about WFS and trust calculation in Section II. TheWFS model, multiobjective optimization, and fuzzy methodsare presented in Section III. In Section IV, a TWFS model isproposed, followed by a discussion on the scheduling algorithmin Section V. A case study was conducted to illustrate the valueof the proposed algorithm in Section VI. The experimentalresults are presented in Section VII, followed by the conclusion.

II. RELATED WORK

A. Trust Computing

Trust plays an important part in e-commerce. Thus, buildingtrust is also essential for cloud service providers. In general,customers are given the opportunity to provide feedback alongwith a rating to the service provider after each transaction.In this case, positive feedback rate can be calculated as R =P/(P +N), where P is the number of positive ratings and Nis the number of negative ratings from the buyers. Another wayof calculating positive feedback rate considers a central author-ity model where ratings are supplied by different users [41].However, the user ratings may be incorrect for some reasons.

In the area of distributed information sharing networks,considerable amount of research work has been done to shareand distribute information with trustworthy participating peers.For example, the PowerTrust model proposed by Zhou andHwang computed the local trust value based on the feedbackof successful transactions [42]. The EigenTrust algorithm com-puted the global trust value of a given peer based on the trustvalue of local successful transactions by using binary ratingmodel [43]. A trust model proposed by Jøsang et al. calculatedsubjective rating by using the Dempster–Shafer belief theory[44]. A WFS model was proposed to optimize execution timeand reliability on heterogeneous systems [45]. Technically, theaforementioned trust aggregation approaches can be catego-rized as direct trust.

With the increasing presence of services on the Internet,researchers have developed a collaborative filtering (CF) modelfor predicting QoS values to make service recommendations.As a strategy that predicts user needs or interests, recommen-dation has been widely deployed for service selection in manywell-known commercial systems and health care informationsystems [46], [47]. A major approach used in recommendationsystems is CF where a user is given recommendation based onthe ratings of other users who are similar with the given user insome aspects. Recommendation systems have been helpful andsuccessful for service composition and selection.

A trust value is easy to calculate for service selection andcomposition. Nevertheless, so far, there are only few studiesconsidering the trust factor in workflow applications. As cloudservices may fail unexpectedly from time to time and cause thetrust issue for workflow applications in cloud computing envi-ronments, trust service-oriented strategies must be considered

in WFS. In this paper, a scheduling algorithm is proposedto adopt a general trust metric that combines direct trust andrecommendation trust.

B. WFS

In the community grid, services are freely shared by dif-ferent organizations. Typically, best effort-based schedulingis adopted to minimize the execution time without consid-ering other factors such as the monetary cost of accessingresources and the satisfaction level associated with varioususers’ QoS. Best effort-based scheduling typically uses rela-tively simple methods such heterogeneous earliest-finish-timealgorithm, min-min algorithm, and max-min algorithm. Thistype of scheduling is mainly focused on time or cost optimiza-tion without considering other factors.

Compared with the traditional distributed computing system,a cloud computing system has a cost saving benefit in variousaspects. As service contracts are signed by users and serviceproviders, cloud service providers have an obligation to adopta series of marketing and technical strategies to ensure thesuccessful fulfillment of service contracts while maintainingtheir profits and satisfying the requirement of the users. Thus,workflow execution costs must be considered for schedul-ing based on the user’s QoS constraints. In [48], schedulingworkflow model with budget, denoted as constraint LOSS andGAIN strategy, is to start from an assignment which has goodperformance under either of the two optimization criteria and,then, to swap tasks between services in order to optimize asmuch as possible for the other criterion. The two strategiesare mainly focused on cost and time. In practice, they need toiteratively adjust time to find a good solution.

A cost-based WFS algorithm, denoted as deadline-MDP, wasproposed by Yu et al. to minimize the execution cost whilemeeting the deadline [49], [50]. The algorithm was imple-mented by distributing the deadline over each task partition[49], [50]. Another deadline-MDP algorithm was developed byYuan et al. who use a backward method to create a new al-gorithm called deadline bottom level (DBL) [51]. Specifically,the workflow deadline is segmented into the time intervals of alltasks. All tasks in each bottom level have the same subdeadline,but the starting time of a task in each level is determined bythe maximum finishing time of its predecessors, rather than thefinishing time of its parent group which is adopted by deadlinetop level (DTL). The DBL algorithm can considerably improvethe average performance of DTL. Although DBL and DTLare very simple and relatively effective, the temporal relation-ships may be partially changed. As a result, workflow appli-cations with shorter deadline constraints cannot be optimizedeffectively. The compromised-time-cost model accommodatesinstance-intensive cost-constrained workflow by compromisingexecution time and cost with the deadline [52]. Overall, theaforementioned three algorithms aim to minimize the totalexecution cost while meeting the deadline.

In summary, the aforementioned WFS algorithms are mainlyfocused on minimizing execution time or cost and do notconsider the critical influence of uncertainty and unreliabil-ity in large-scale distributed systems. Unfortunately, in cloud


TAN et al.: TRUST SERVICE-ORIENTED SCHEDULING MODEL FOR WORKFLOW APPLICATIONS 3

computing environments, many discrete events lead to failuresof a workflow application. Thus, ES are strongly expected toaggregate and share trustworthy services in cloud computingenvironments. From this perspective, we made an effort todevelop a TWFS model in order to balance different andconflicting requirements of enterprises.

III. SYSTEM MODELS

In this section, we provide a brief overview of basic termsrelated to the scope of this paper, including WFS model, fuzzymodeling, and multiobjective modeling. In addition, we discussthe problem related to the TWFS models.

A. WFS Model

If enterprise users submit their requirements for integrationwith the workflow application requiring maximum QoS con-straints, the WFS planner will return the results to users andallow users to choose the most suitable schedule and confirmthe information. Finally, ES start to execute the workflowaccording to the conventional information. In the context ofcloud computing, a wide range of integrated systems can berepresented as workflows which can be modeled as directedacyclic graphs (DAGs) [53]. The following model provides aneffective way to map the node (or task) to service.

Definition 1: Let W = {T,E} denote a workflow whichconsists of a set of tasks T = {T1, . . . , Ti, . . . , Tn} and a set ofdependences among the tasks; tasks are sometimes also callednodes. E = {〈T1, T2〉, . . . , 〈Ti, Tj〉, . . . , 〈Tn−1, Tn〉}, and Ti isthe parent task of Tj , where Tj , Tj ∈ T . A child task cannot beexecuted until all of its parent tasks have been completed.

Definition 2: In a workflow DAG, we call a task which doesnot have any parent tasks an entry task and denote it as Tentry ,while a task which does not have any child tasks is called anexit task, denoted as Texit.

Definition 3: Let m be the total number of candidate ser-vices. There are a set of sij (where 1 ≤ i ≤ n, 1 ≤ j ≤ mi)which are available for the task. Services have varied processingcapability delivered at different prices and time. We denote tijas the sum of processing time and data transmission time andcij as the sum of service price and data transmission cost forprocessing ti on service sij .

Definition 4: The minimum time to complete the schedule isthe length of the critical path from the entry node of workflowto the exit node. In symbols

EF =ES +Di (1a)

LS =LF −Di (1b)

Slack =LF − EF (1c)

where ES represents the earliest start time, EF is the earliestfinish time, LS denotes the latest start time, LF is the latestfinish time, the slack for a task is the difference between itslatest finish time and its earliest finish time, and Di is theestimated duration of the task Ti. Each task with zero slack ison a critical path through the workflow DAGs.

Fig. 1. Membership function for objective.

B. Fuzzy Model

Using max-min as the operator [54], [55], the membershipfunction of objectives is formulated by separating every objec-tive into its maximum and minimum values [56]. The linearmembership functions for optimization goal (Zk, Zl) are givenas follows:

u(x) =

⎧⎪⎨⎪⎩

1, zk ≤ zmink

zmaxk

−zk(x)

zmaxk

−zmink

, zmink ≤ zk ≤ zmax

k

0, zmaxk ≤ zk

(2)

u(x) =

⎧⎪⎨⎪⎩

0, zl ≤ zminl

zl(x)−zminl

zmaxl

−zminl

, zminl ≤ zl ≤ zmax

l

1, zmaxl ≤ zl

(3)

where zmaxk and zmin

k can be computed through solving themultiobjective problem as a single objective in each time. zmax

k

is the maximum value of zk(x), and zmink is the minimum value

of zk(x), as shown in Fig. 1.

C. Multiobjective Model

The workflow planning problem is a multiobjective opti-mization problem in which groups of conflicting objectives aresimultaneously optimized [57].

Definition 5: In order to formulate this model, the notationsare defined as follows:D time constraint (deadline);B cost constraint (budget);Tr trust constraint;tij time of the jth service assigned for the ith task;xij if the jth service is assigned for the ith task, then xi

j =

1; else, xij = 0;

n number of all tasks;mi number of services available for the ith task.Definition 6: Mathematically, a typical model for WFS in

the service cloud can be described as follows:

Minimize: Z1 =

n∑i=1

mi∑j=1

xijc

ij (4a)



Minimize: Z2 =

n∑i=1

mi∑j=1

xijt

ij (4b)

Maximize: Z3 =

n∑i=1

mi∑j=1

xijtr

ij (4c)

subject to: Z1 ≤ B

Z2 ≤ D

Z3 ≥ Tr (4d)

where Zi is the object or criteria for minimization like time,cost, etc. Xd is the set of feasible solutions that satisfy theset of system and policy constraints. The optimization of theworkflow is to map every Ti onto some Si

j in order to achieveminimum execution cost and time while meeting the constraintsof trust.

IV. TWFS MODEL

Based on the aforementioned discussion, the present workconsiders the WFS problem as a fuzzy multiobjective prob-lem which is subject to time, cost, and trust constraints. Theproblem formulation proposed here considers three differentobjectives related to trust, time, and cost.

A. Member Function for Trust Evaluation

In a large-scale distributed system, many discrete eventscould lead to the failures of a workflow application. Therefore,we present a trust service-oriented scheduling algorithm whichconsiders the reliability of a service for workflow execution. Ageneral trust metric that combines direct trust and recommen-dation trust is defined as follows:

Tr(Si) = wi ∗DT (Si) + (1− wi) ∗RT (Si) (5)

where DT (Si) is a direct trust of the ith service by the experi-ences which is based on the history of using the service by theusers. RT (Si) is a recommendation trust of the ith service byother users. wi is the weight of direct trust and recommendationtrust for the ith service, which can be computed as follows:

wi = 1− 1

ek(6)

where k is the number of times that the ith service is used by theservice client and wi is adaptively changing with the value ofk. For a more frequently used service, higher weights should beassigned for the direct trust evaluation. For example, if wi = 0,it means that the client never uses the ith service, so Tr(Si)depends completely on the recommendation trust evaluation.DT (Si) is established through observations on whether the

previous interactions among the services are successful used.The observation is often described by two variables: ni, denot-ing the number of successful interactions, and Ni, denoting thetotal number of interactions for the ith service. The direct trustvalue can be calculated as

DT (Si) =ni + 1

Ni + 2(7)

Fig. 2. Data structure of mapping tasks.

where the trust value of a service is initialized to 1/2.RT (Si) is the recommendation trust value of the ith service.

RT (Si) is the weighted sum of ratings by others, where theweight corresponds to the similarity between the active user andeach of the other users.

To describe the prediction algorithm formally, let S be the setof services being rated, n be the number of users, Si be the setof services rated by user i, and vij be the rating given by user ito the jth service. Next, let average vi be the average rating byuser i as given by

avg(vi) =1

|Si|∗∑j∈Si

vij . (8)

The weight wai reflects the similarity between user a anduser i. Based on these, we can define the predicted rating of theactive user a for service j as (n is the number of users)

RT (Sj) = avg(va) +

∑ni=1 wai(vij − vi)∑n

i=1 |wai|. (9)

The weights for the users are calculated by the Pearsoncorrelation coefficient (PCC) which is widely used to computethe degree of similar relationship between two variables. Thesimilarity between users a and i would be computed as follows:

wai =

∑j∈S(vaj − va)(vij − vi)√∑j∈S(vaj − va)2(vij − vi)2

(10)

where S = Sa ∩ Sb is the subset of services which have beenused by both users a and i.

Using max-min as the operator, the membership function forthe trust evaluation of the service is formulated as follows:

UTi(trx) =

trix − trimin

trimax − trimin

, if trimin ≤ trix ≤ trimax

(11a)

UTi(trx) = 0, if trimin = trix (11b)

UTi(trx) = 1, if trimax = trix (11c)

where trix is the trust value for the service selected for the ithtask, trimax is the max value of trust for the service available forthe ith task, and trimin is the min value of trust for the service.

B. Member Function for Execution Time

There is a set of services in which sij (where 1 ≤ i ≤ n,1 ≤j ≤ mi) is capable of executing the task. The linked list datastructure is employed to solve the problem of mapping task Ti

onto sij , as shown in Fig. 2.In the linked list, we store each service in a node that

contains three elements such as time, cost, feedback value anda reference to the next node in the list, and the list of services issorted in a descending way based on the value of time.



Using max-min as the operator, the membership function forexecution time is formulated as follows:

UTi(tx) =

timax − tixtimax − timin

, if timin ≤ tix ≤ timax (12a)

UTi(tx) = 0, if timax = tix (12b)

UTi(cx) = 1, if timin = tix (12c)

where tix is the time for the service selected for the ith task, timax

is the max value of time for the service available for the ith task,and timin is the min value of time for the service available forthe ith task.

Member function indicates that the closer the value of tixis to timin, the better the selected service is, and the more theobjective is satisfied.

C. Membership Function for Execution Cost

Similar to the membership function of execution time, themembership function for the execution cost of the ith task canbe defined as follows:

UTi(cx) =

cimax − cixcimax − cimin

, if cimin ≤ cix ≤ cimax (13a)

UTi(cx) = 0, if cimax = cix (13b)

UTi(cx) = 1, if cimin = cix (13c)

where cix is the cost for the service selected for the ith task, cimax

is the max value of cost for the service available for the ith task,and cimin is the min value of cost for the service available for theith task.

The time and cost are conflicting requirements. According tothe analysis of the membership function for execution cost, thelist of services is stored in a descending order based on the time.In contrast, the list is in an ascending order based on the cost.

The equation of the membership function for cost indicatesthat, if the value of cix is high, the membership value is low.On the other hand, if the value of cimin is low, the highermembership value is assigned, the better the selected serviceis, and the more the objective is satisfied.

V. MODEL ALGORITHM

WFS require policies to strike a balance for the different re-quirements. For example, enterprise may expect to save budgetbut have longer time, or aim to minimize the execution timeof schedule. In order to satisfy the multiple criteria simultane-ously, a compromise should be made to find a suitable solution.

Using max-min as the operator, the aforementioned fuzzymodel can be converted to the following crisp model as:

Maxmize λi (14a)

UTi(tx) ≥ λi (14b)

UTi(cx) ≥ λi (14c)

UTi(trx) ≥ λi (14d)

tx ≥ 0, cx ≥ 0, trx ≥ 0 (14e)

λi ∈ [0, 1], I = 1, . . . , n (14f)

The degree of overall satisfaction is the minimum of all theaforementioned membership values. The fuzzy decision maybe considered as the choice that satisfies all of the objectives.Therefore, the fuzzy decision for overall satisfaction is given asfollows:

λij = min {UTi

(tx), UTi(cx), UTi

(trx)} . (15)

The selection of services for WFS is the maximum of alldegrees of satisfaction. We define the selection decision asfollows:

λik = max{λi

j} (16)

where k means the kth service which has the maximum ofdegrees of satisfaction for the ith task, so the kth service ismapping on the ith task.

The approaches used in [55] do not consider the relativeimportance of objectives. In the max-min model, the weights ofobjectives are equal. However, the relative importance of objec-tives is often different. The selection model can be formulatedby the weighted arithmetic mean operator

Maximize U ∗WT (17a)

wt + wc + wtr = 1, for wt, wc, wtr ∈ [0, 1] (17b)

where W is the weight vector containing wt, wc, and wtr

values; wt means the weight of time, wc is the weight ofcost, and wtr denotes the weight of trust in the model. UT isthe vector of service membership values, which is includingUTi

(tx), UTi(cx), and UTi

(trx).From the aforementioned analysis, the TWFS model is stated

in Algorithm 1.

Algorithm 1 TWFS

Input: request processing time, cost, and trust valueOutput: A workflow schedule strategy for Enterprise Infor-mation Systems1: request processing time, cost, and trust value from avail-

able service ∀Ti ∈ T2: repeat3: compute the indegrees for ∀Ti ∈ T4: while !stack.isEmpty() do5: forall s in available services of the ith task do6: compute λk = max{

∑2j=0 U

Ti

Zj(x)∗ wj}

7: end for8: Mapping service Sk onto the ith task9: forall edge e in adjacent do10: vertices of the ith task11: vertex w = e.dest12: if w.indegree == 0 then13: insert w into stack14 end if15: end for16: end while17: until all tasks have been scheduled



Fig. 3. Sample DAG of the workflow application.

TABLE ISAMPLE OF THE RATINGS OF ENTERPRISES FOR CLOUD SERVICES

TABLE IIENTERPRISE USER-BASED PEARSON CORRELATION

VI. CASE STUDY

In this section, a case study is used to illustrate the value ofthe proposed algorithm. The algorithm is explained using anexample of workflow applications described in Fig. 3.

A. Trust Evaluation for Candidate Services

To illustrate the value of trust evaluation, we consider thefollowing example based on the ratings of Table I.

Example: An enterprise needs to evaluate the candidate ser-vice A for the ith tasks in the example workflow applicationthat the enterprise has not yet used. The blank in Table I meansthat a user’s rating is missing, and it is treated as if it equals thatuser’s average rating.

The similarities between the pairs of enterprises in the exam-ple are computed by PCC, which is given in Table II.

The predicted rating would be calculated as follows:

RT (Sj) = avg(va) +

∑ni=1 wai(vij − vi)∑n

i=1 |wai|

=3.5 +−0.66 ∗ 0.75 + 0.4 ∗ 1.75 + 3.6 ∗ 0.92

0.66 + 0.84 + 0.92

≈ 5. (18)

TABLE IIISAMPLE OF SERVICE LIST

B. Mapping Task Onto Suitable Services

Each task in the workflow can be executed by a series ofsimilar cloud services at different levels of quality. All candi-date cloud services are in the service list of the ith task. Forexample, {(5,3,3),(10,2,4),(20,1,5)} is a candidate service listfor the 6th task. Table III shows the service list of each task forthis workflow example.

Service selection and service ranking are based on the objec-tive functions for cost, time, and trust, respectively. The stepsof service selection are described in the following steps.

Step 1: Using max-min operator, compute the vectors of(UTi

(tx), UTi(cx), UTi

(trx)) for the ith task

t6max=3, t6min=3; c6max=20, c6min=5; tr6max=5, tr6min=3

U1T6

=

(20−5

20−5

3−3

3−1

3−3

5−3

)= (1 0 0)

U2T6

=

(20−10

20−5

3−2

3−1

5−3

5−3

)=

(2

3

1

21

)

U2T6

=

(20−20

20−5

3−1

3−1

4−3

5−3

)=

(0 1

1

2

).

Step 2: Compute max of U ∗WT of execution time and costfor the ith task

U ∗WT = (UTi(tx) UTi

(cx) UTi(trx)) ∗ (wt wc wtr)

T .

Step 3: If λix is max, then six is selected, therefore mapping

the ith task onto the xth service.In various situations, users often have different preferences

for service selection, and the relative importance of their ob-jectives is often different. In this paper, we set a typical weightvector value to illustrate the value of the proposed algorithm.

Case 1: Give W = (1/3 1/3 1/3) to the set of (wt wc wtr)for test. The weights of objectives are equal, which means that



the user aims at reaching a compromise for all objectives⎛⎝U1

T6

U2T6

U3T6

⎞⎠=

⎛⎝ 1 0 0

2/3 1/2 10 1 1/2

⎞⎠

⎛⎝ 1/3

1/31/3

⎞⎠=

⎛⎝ 1/3

13/181/2

⎞⎠. (19)

In this case, the most suitable service for the 6th task is the ser-vice s26, which is supported by U2

T6> U1

T6and U2

T6> U3

T6. The

results indicate that the solution enables users to compromisefor different requirements.

Case 2: Assuming W = (1, 0, 0), the weight of time isthe most important, which means that enterprise users aim atoptimizing a single objective of execution time⎛⎝U1

T6

U2T6

U3T6

⎞⎠ =

⎛⎝ 1 0 0

2/3 1/2 10 1 1/2

⎞⎠

⎛⎝ 1

00

⎞⎠ =

⎛⎝ 1

2/30

⎞⎠ . (20)

In this case, the service s16 is selected based on matrix computa-tion. As shown in Table III, the service s16 is the most optimizedchoice for execution time.

Case 3: Assume W = (0, 1, 0), focusing on minimizing ex-ecution cost for service selection. Similar to Case 2, the services36 is selected⎛⎝U1

T6

U2T6

U3T6

⎞⎠ =

⎛⎝ 1 0 0

2/3 1/2 10 1 1/2

⎞⎠

⎛⎝ 0

10

⎞⎠ =

⎛⎝ 0

1/21

⎞⎠ . (21)

Case 4: Assuming W = (0 0 1), we aim to get the maximumof the trust value. The most trustworthy service is selectedbased on the member function for trust evaluation⎛⎝U1

T6

U2T6

U3T6

⎞⎠ =

⎛⎝ 1 0 0

2/3 1/2 10 1 1/2

⎞⎠

⎛⎝ 0

01

⎞⎠ =

⎛⎝ 0

11/2

⎞⎠ . (22)

The other tasks in the workflow are scheduled by the sameway as the 6th task. The solution for the example WFS is shownin Table IV.

By performing the topological sorting, we can get the se-quence order list as follows: 1 5 4 8 3 2 6 7 11 10 9 14 12 1315 16; the critical path is 1 2 6 7 10 14 15 16, and the executiontime of the WFS is 76.

VII. EXPERIMENTS

A. Setting of the Experiments

To evaluate the proposed scheduling model, we considertime and cost as key factors in the comparison with otherWFS strategies: minimum critical path (MCP), greedy cost,and DBL. MCP sorts services by their time and selects theservice with the minimum time for the corresponding task. Onthe contrary, the greedy-cost approach sorts services by theircost and selects the service with the minimum cost. For DBL,all tasks are partitioned into bottom level groups by using abackward method.

In our experiment, DAGs are randomly generated with differ-ent scales and structures. The expected time for each service israndomly generated from a uniform distribution in the interval[10], [60]. To simplify the problem, the corresponding costof each service is defined by the equation cost = 360/time.

TABLE IVOPTIMUM SOLUTION FOR THE WORKFLOW

All the algorithms are coded in Java and performed on anAdvanced Micro Device (AMD) Athlon II Dual-Core M300with a 2.00-GHz processor and 2-GB RAM which uses theoperating system Window XP.

B. Weight of Impact

Algorithm 2 Computing the Weight for TWFS

Input: A workflow graphOutput: A workflow schedule strategy for EISs1: wt = 0.5; wc = 0.5//wt: weight of time, wc: weight

of cost; the initial value of weights is equal 0.52: while executionTime <= deadline do3: wt = wt + 0.1; wc = wc − 0.14: for all vertex in Topical Sort Array do5: for all s in available services of the ith task do6: if s.trust <= trustConstraint of the ith task then7: λj = UTi

Z0(x) ∗ wt + UTi

Z1(x) ∗ wc

8: λk = max{λj}9: end if10: end for11: Mapping service sk onto the ith task.12: end for13: for all vertex in critical Path Array do14 executionTime+ = vertex.executionTime15: end for16: end while

The key idea of the TWFS algorithm is to find the optimumsolution with the deadline constraint by adjusting the weights oftime and cost. Let the initial weights of the objectives be equal.When the execution time of all tasks is much lower than thedeadline, the weight of cost is adjusted incrementally until the



Fig. 4. Execution cost with respect to different weight of time.

Fig. 5. Time of each task of three strategies.

execution time of all tasks satisfies the deadline. The calculationof weights among the objectives can be obtained by the way of astandard dynamic programming algorithm with value iteration,which is stated in Algorithm 2.

As shown in Fig. 4, when the weight of time is close to thedeadline, TWFS is equivalent to MCP. As the weight of timedecreases to 0, TWFS is equivalent to greedy cost in which theweight of cost is the most important.

C. Comparison of Different Algorithms

The comparison of TWFS, MCP, and greedy-cost models ispresented by using these sample data described in Fig. 3. Theresults of execution time and cost for each task are recorded inFigs. 5 and 6. It concludes that TWFS enables users to find acompromising solution.

The next experiment is to sequentially test the aforemen-tioned three algorithms with different sizes. The number oftasks varies from 10 to 100 with an increment of 20. Thecomparisons of execution time and cost of TWFS, MCP, andgreedy cost are shown in Figs. 7 and 8, respectively.

Fig. 6. Cost of each task of three strategies.

Fig. 7. Comparison of execution time.

Fig. 8. Comparison of execution cost.



Fig. 9. Execution time with different deadlines.

MCP has the shortest execution time among the algorithmsbut with the highest cost. Conversely, greedy cost has the leastcost but with the longest execution time. As for the TWFSapproach, time and cost are both considered simultaneously,which enables the user to compromise requirements to yielda genuinely better solution.

The aforementioned experiments are tested without strictdeadlines provided by the user. When deadline constraints areprovided, DBL sometimes is a better choice for WFS. In thenext experiment, we compare the proposed algorithm with DBLwith a strict deadline.

In order to evaluate the comparison results with a criticalconstraint, the execution time is normalized by using Execu-tionTime/Deadline, and each workflow execution is given withdifferent deadlines that are defined as follows:

D = Tmin + k ∗ (Tmax − Tmin) (23)

where D is the value of the deadline, Tmin is the min-imum execution time produced by MCP and Tmax is themaximum execution time produced by greedy cost; let k ∈{0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1}. Usually, the longerthe deadline is, the less the workflow pays for service.

Comparisons of execution time and cost for TWFS and DBLat different deadlines are shown in Figs. 9 and 10 separately.

As shown in Figs. 9 and 10, both DBL and TWFS cansatisfy the deadline constraint. As the time to the deadlineincreases, the constraint is more relaxed, and the execution timeof TWFS is much closer to the constraint time than that of DBL.Meanwhile, the execution cost produced by the TWFS andDBL decreases significantly. According to the aforementionedanalysis, the experiment indicates that TWFS is able to find theoptimum solution with the deadline constraint. TWFS performsslightly better than DBL.

VIII. CONCLUSION AND FUTURE WORK

In dynamic and failure-prone large-scale distributed systems,a lack of trust between enterprise and cloud service providersoften prevent businesses from fully adopting cloud services.

Fig. 10. Execution cost with different deadlines.

Integrating several cloud services into a composite serviceeffectively is still a research challenge. In this paper, we dis-cuss the WFS-related problem for system integration in cloudcomputing environments. A TWFS model is proposed for ES.The contribution of this paper is listed as follows.

1) We designed cloud service selection algorithms to formoptimum workflow application while meeting differentconstraints from users.

2) The proposed scheduling algorithm adopts a general trustmetric that combines direct trust and recommendationtrust.

For the TWFS model, the cloud service criteria such asexecution time, cost, and trust are considered simultaneouslyto yield a genuinely optimal solution.

The performance of the proposed algorithm has been dis-cussed and compared through an experiment. The TWFS al-gorithm could satisfy the requirements of the enterprise byadjusting the weights of different criteria, and the experimentalresults show that the proposed approach is effective.

There are a lot of research challenges and opportunities inscheduling of workflow applications [58]–[75]. Currently, fewresearch studies have been done about how dynamic run timechanges can affect a statically predetermined schedule. Dueto increased competition and evolution of cloud services overtime, adaptive and context-aware collaboration systems modelare being required to support for uncertainty and unreliableenvironments. As part of our future work, we plan to extend theTWFS algorithm for adaptive collaborative model in responseto changes in cloud computing environments.

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WenAn Tan received the Bachelor of Engineeringdegree in computer software and the Ph.D. degree incomputer software and theory from Beihang Univer-sity, Beijing, China, in 1987 and 2001, respectively.

He is currently a Professor of computer sciencewith the Nanjing University of Aeronautics andAstronautics, Nanjing, China. He is also a Profes-sor with Shanghai Second Polytechnic University,Shanghai, China. He was a Postdoctoral Fellow inthe Software Institute, Chinese Academy of Sci-ences, for two years and a Visiting Scientist in the

National Research Council of Canada for one year. His current researchinterests include software engineering, process engineering and developmentenvironment, enterprise dynamic modeling, trusted service computation, andenterprise intelligent information systems. He has published more than 80scientific papers in scientific journals and international conferences/workshops.

Yong Sun received the M.S. degree in computerscience from the China University of Geosciences,Wuhan, China, in 2007. He is currently workingtoward the Ph.D. degree in the School of Com-puter Science and Technology, Nanjing University ofAeronautics and Astronautics, Nanjing, China.

His main fields of research are software engineer-ing, cooperative computing, service computing, gridand cloud computing based on workflow manage-ment, and intelligent information systems.

Ling Xia Li received the M.S. and Ph.D. degreesin production/operations and logistics from the OhioState University, Columbus, OH, USA.

She is currently a Professor of production/operations with Old Dominion University, Norfolk,VA, USA, and a fellow in production and inven-tory management of the Association for OperationsManagement.

Dr. Li has served as an Associate Editor for theIEEE TRANSACTIONS ON INFORMATION TECH-NOLOGY IN BIOMEDICINE, IEEE TRANSACTIONS

ON INDUSTRIAL INFORMATICS, and other IEEE journals. She has pub-lished research articles in journals, including the IEEE TRANSACTIONS ON

SYSTEMS, MAN, AND CYBERNETICS PART C, IEEE TRANSACTIONS ON

INFORMATION TECHNOLOGY IN BIOMEDICINE, IEEE SYSTEMS JOURNAL,European Journal of Operational Research, Journal of Operations Manage-ment, International Journal of Production Research, International Journal ofOperations and Production Management, International Journal of ProductionEconomics, Annals of Operations Research, Computers and Operations Re-search, OMEGA, etc.

GuangZhen Lu is currently working toward theM.S. degree in computer science and technology atthe Nanjing University of Aeronautics and Astronau-tics, Nanjing, China.

His research interests include software engineer-ing, cooperative computing, and service computing.

Tong Wang received the B.S. degree from the Insti-tute of Computer Science and Technology, TaiyuanUniversity of Science and Technology, Taiyuan,China, in 2002, the M.S. degree from the Instituteof Computer Science and Technology, Lanzhou Uni-versity of Technology, Lanzhou, China, in 2006,and the Ph.D. degree from the Institute of ImageProcessing and Pattern Recognition, Shanghai JiaoTong University, Shanghai, China, in 2009.

She is currently a Lecturer with the Institute ofComputer and Information, Shanghai Second Poly-

technic University, Shanghai. Her research interests include data mining,artificial intelligence, and pattern recognition.

a trust service-oriented scheduling model for workflow applications in cloud computing

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