IEEE TRANSACTIONS ON SERVICES COMPUTING, VOL. 5, NO. 3, JULY-SEPTEMBER 2012 1

QoS-Aware Dynamic Composition of WebServices using Numerical Temporal Planning

Guobing Zou, Qiang Lu, Yixin Chen, Senior Member, IEEE, Ruoyun Huang, You Xu, and Yang Xiang

Abstract—Web service composition (WSC) is the task of combining a chain of connected single services together to create a morecomplex and value-added composite service. Quality of Service (QoS) has been mostly applied to represent nonfunctional propertiesof Web services and differentiate those with the same functionality. Many research has been done on QoS-aware service composition,as it significantly affects the quality of a composite service. However, existing methods are restricted to predefined workflows, whichcan incur a couple of limitations, including the lack of guarantee for the optimality on overall QoS and for the completeness of findinga composite service solution. In this paper, instead of predefining a workflow model for service composition, we propose a novelplanning-based approach that can automatically convert a QoS-aware composition task to a planning problem with temporal andnumerical features. Furthermore, we use state-of-the-art planners, including an existing one and a self-developed one, to handlecomplex temporal planning problems with logical reasoning and numerical optimization. Our approach can find a composite servicegraph with the optimal overall QoS value while satisfying multiple global QoS constraints. We implement a prototype system andconduct extensive experiments on large Web service repositories. The experimental results show that our proposed approach largelyoutperforms existing ones in terms of solution quality and is efficient enough for practical deployment.

Index Terms—WSC, QoS, automated planning, temporal reasoning, numerical optimization.

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1 INTRODUCTION

WEB services are modular, self-describing, self-contained, platform-independent software com-

ponents that can be published by service providersover the Internet. Since Web services became available,many organizations prefer to only keep their principalbusiness, but outsource other application services overthe Internet [1]. Web service composition (WSC) hasbeen widely applied, allowing construction and sharingof independent and autonomous software [2]. As thenumber of Web services proliferates, automated WSC ismotivated by the need to improve the effectiveness andefficiency of integrating services [3].

WSC is the task of combining a set of single Webservices together to create a more complex, value-addedand cross-organizational business process. WSC requiresa computer program to automatically select, integrate,and invoke multiple Web services in order to achieve auser-defined objective [3]. For those Web services provid-ing the same functionality, QoS has been mostly applied

• G. Zou is with the School of Computer Engineering and Science,Shanghai University, Shanghai 200072, China.Washington University, MO 63130, USA.E-mail: guobingzou@gmail.com

• Y. Chen, R. Huang and Y. Xu are with the Department of ComputerScience and Engineering, Washington University, MO 63130, USA.E-mail: {chen, rh11, yx2}@cse.wustl.edu

• Q. Lu is with the Department of Computer Science and Technology,University of Science and Technology of China, Hefei 230026, China.E-mail: rczx@mail.ustc.edu.cn

• Y. Xiang is with the Department of Computer Science and Technology,Tongji University, Shanghai 201804, China.E-mail: shxiangyang@tongji.edu.cn

to represent their nonfunctional properties and differ-entiate them for service composition. QoS is a broadconcept that encompasses a group of nonfunctionalproperties, such as execution price, execution duration,availability, execution success rate, and reputation [4].Given a set of multiple global QoS constraints and userpreferences, the challenge is how to efficiently constructa composite service such that its overall QoS is optimal,while all the QoS constraints are satisfied.

Existing QoS-aware WSC approaches fall short onfinding solutions with globally optimal QoS, becauseit is a very difficult optimization problem with logicalreasoning, discrete decisions, temporal constraints, andnumerical optimization. In particular, when the numberof Web services becomes large, there is a huge searchspace. As a result, most existing QoS-aware WSC meth-ods are restricted to predefined workflows [4], [5], [6],[7], [8], [9]. That is, it has a predefined workflow modelto support service selection, as the one shown in Fig.1. A predefined workflow consists of a set of tasks. Foreach task, it corresponds to a group of candidate Webservices so that each of them can perform the task. Fig. 2illustrates the candidate services for a workflow modelwith p tasks. Since these conventional approaches arebased on predefined workflows, their search space isreduced to a smaller one, which results in two limita-tions. One is that they cannot make sure its overall QoSis optimal, considering other workflows. Another is thatthese approaches do not guarantee finding a solutionsatisfying the global QoS constraints for a compositiontask, even if there exists one under a different workflow.

Automated planning has been a popular method forWSC [10], [11], [12], [13], [14], [15], [16], [17], [18], [19].

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Fig. 1. A predefined workflow model for a WSC. It consists of8 business process tasks.

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Fig. 2. The candidate services for a composite service work-flow model containing p tasks. Each task ti(1 ≤ i ≤ p)corresponds to a set of candidate services Ti.

However, these approaches only try to find a compositeservice satisfying the functionality requirement, but donot consider QoS at all.

To address the above issues, we propose a novelplanning-based approach to WSC with QoS optimiza-tion. One can specify multiple global QoS constraintsand user preferences, and our method finds a compositeservice that optimizes the overall QoS, while satisfyingthose specified global QoS constraints.

Instead of predefining a workflow composition modelfor the selection of Web services, our approach trans-forms a composition task with multiple global QoSconstraints and preferences to a planning problem withtemporal and numeric features. We leverage advances intemporal numerical planning to optimally and efficientlysolve the resulting planning problems by our temporallynumeric planner.

Our new approach has several advantages. It ensuresthat a composition solution can be found if one exists,while existing work may not when no solution withina predefined workflow satisfies global QoS constraints.Furthermore, our new approach can optimize the QoS,while conventional approaches only find the optimalQoS under a predefined workflow, which may not beglobally optimal. The experimental results show that,our approach significantly extends the capability of priorwork by ensuring global satisfiability and optimalitywithout assuming a predefined workflow. The drawbackof our approach is in its computational cost for a com-position task. However, although our approach is slowerthan existing approaches, it can solve large instancesin a few seconds and is still fast enough for practicaldeployment, thanks to efficient search engines in state-of-the-art automated planners.

2 RELATED WORK

2.1 QoS-aware WSCConventional QoS-aware WSC approaches can be clas-sified into the following five categories, all of whichassume a predefined workflow, which constrains their

solution space. As a result, they are not globally com-plete or optimal.

• Exhaustive search. This approach [8] tries to enu-merate all possible combinations by using candidateWeb services for each task. As a consequence, acomposite service with the optimal QoS value fora predefined workflow model can be selected, ifone exists and satisfies all global QoS constraints.However, the time complexity of this approach ishigh, i.e., O(mp), where m and p are, respectively,the maximum number of candidate services for atask and the number of tasks in a workflow.

• Local optimization. This is a locally optimal QoSservice selection process [4] for WSC. A QoS vectoris used to represent QoS of each service and aMultiple Criteria Decision Making (MCDM) process[2], [8] is applied to calculate QoS value of a service,using the weights assigned by a user to each QoScriterion. For each task in a workflow, the servicewith the optimal QoS value is selected from its can-didate service group, as shown in Fig. 2. Althoughthis approach is locally optimal and efficient witha low time complexity of O(m ∗ p), it does notguarantee to satisfy global QoS constraints.

• Integer programming. Since high complexity makesexhaustive search impractical in real applicationsand local optimization does not take global QoSconstraints into account, Zeng et al. [4] proposed amethod based on Integer Programming (IP). Basedon a predefined workflow model, it transforms aQoS-aware WSC problem to an IP problem. An IPsolver is then used to find a solution.

• Approximative algorithm. In order to decrease thetime complexity, Zhang et al. [7] model the QoS-aware WSC problem as a multi-dimensional multi-choice knapsack problem. It still requires a pre-defined workflow. Although it is an NP-completeproblem, a heuristic algorithm can be used. Anapproximative algorithm of constructing the convexhull of related points [7] was applied to generate acomposite service with a suboptimal QoS value.

• Situation calculus and Golog. Considering richuser preferences as a key component, McIlraith’sgroup proposed two ways of automated WSC withusers’ customized preferences [3], [20]. They first-ly proposed a means of performing automatedWSC by exploiting the agent programming lan-guage Golog to represent generic procedures anda first-order preference language to represent quali-tative temporal user preferences [20]. Subsequently,a middle-ground execution engine [3] has been pre-sented to generate high-quality compositions basedon an Hierarchical Task Network (HTN) WSC sys-tem, HTNWSC-P [21], which recursively decompos-es a composition task into subtasks, and stops whenit reaches primitive tasks that can be performeddirectly by planning operators.

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In addition to above hard QoS contracts and userpreferences of WSC, Rosario et al. [22] argued that userswould find it very natural to ”soften” contracts so thatprobabilistic QoS and soft contracts should be taken fortransaction-based Web service orchestration.

2.2 Planning-based WSCFor automated WSC, some approaches use AI searchtechniques to find a composition solution, such asheuristic forward and regression search [13], [16] andplanning graph construction [14]. Rahmani et al. [23]proposed a novel backward search algorithm for au-tomatically directing the composition process of Webservices. Although it considers nonfunctional user pref-erences for optimizing quality of WSC, multiple hardglobal QoS constraints are not taken into account whenfinding a composition solution. Based on an aggregatedmetric proposed by Blanco et al. [24] to estimate qualityof service composition, two algorithms called DP-BF andPT-SAM are presented to select the best compositions,respectively. DP-BF combines a best first strategy witha dynamic-programming technique and PT-SAM adaptsa Petri-net unfolding algorithm trying to find a desiredmarking from an initial state. By extending Colored PetriNet (CPN) formalism, Cardinale et al. [25] presenteda CPN transactional Web service selection (CPN-TWS)algorithm to address the issue of selecting and compos-ing Web services, considering functional and Quality ofService (QoS) requirements combined with transactionalproperties. The result is a CPN corresponding to a trans-actional composite service whose components locallyoptimize the QoS. For composition-oriented discoveryof Web services, an extensive matchmaking algorithmcalled SAM (Service Aggregation Matchmaking) [26]features a more flexible matching by accounting forservice composition.

There are also some composition approaches, whereautomated planners have been applied to select Webservices in real applications [27], [28] [29], [30] [31], [32],[33] for building operational business processes. Theservice composition planner OWLS-XPlan [27], [28] hasbeen applied in an agent based mobile eHealth systemfor emergency medical assistance (EMA) tasks. Anotherimportant application area for automated planner is thecreation of new processes in Business Process Manage-ment (BPM) at the SAP corporation. In this application[29], [30], a Status and Action Management (SAM) modelwas developed to employ planning in a real-time BPMprocess modeling environment, SAP NetWeaver platfor-m. O-Plan [31], [32], another automated planner, is usedin a wide variety of WSC applications [32], [33], includ-ing air campaign planning, non-combatant evacuationoperations, and biological pathway discovery.

3 PROBLEM FORMULATION

In this section, we first focus on the understandingof QoS-aware service composition problem by a set of

formal definitions, and then clearly demonstrate what acomposition solution is to a QoS-aware WSC problem.

Definition 1: (Web service). A Web service w con-sists of a finite set of operations, denoted as w ={op1, op2, · · · }, where each op ∈ w, it is a 3-tuple (I,O,Q),where I = {I1, I2, · · · } is a set of input interface pa-rameters, O = {O1, O2, · · · } is a set of output interfaceparameters, Q = (Q1, Q2, · · · ) is a set of QoS values fora group of QoS criteria {q1, q2, · · · }. We use op.I , op.Oand Q(op) to denote I , O and Q in op, respectively.

Each Web service plays a role that can perform a set ofoperations. A Web service repository is a set of disjointservices. We denote it as W = {w1, w2, · · · }.

Definition 2: (Functionality request). A user’s func-tionality request, r, is a 2-tuple (rin, rout), where rin ={r1in, r2in, · · · } is an interface parameter set provided asrequest inputs, and rout = {r1out, r2out, · · · } is a goalspecification provided as desired results.

A functionality request (rin, rout) is specified by auser who provides a set of input parameters as requestcondition and goal facts as desired results.

QoS criteria can be divided into two categories: pos-itive and negative. Positive QoS criteria denote betterquality with higher values, while negative ones corre-spond to lower quality with higher values. Based onwidely used QoS criteria [4], [34], we use a QoS vectorQ(op) to represent QoS values of each operation op.

Q(op) = (qprice(op), qtime(op), qsucc(op), qavail(op), qrep(op))

where it models the values of a group of QoS criteria{execution price, execution time, probability of success,availability, reputation} in an operation op.

Definition 3: (Global QoS constraints). Given a groupof QoS criteria {q1, q2, · · · }, global QoS constraints, de-noted as C, are a set of QoS values (c1, c2, · · · ). Eachci ∈ C is a lower bound on a positive QoS criterion qior an upper bound on a negative QoS criterion qi.

Each global QoS constraint in C is used to restrict onits corresponding QoS criterion as global QoS value of acomposite service.

Definition 4: (User preferences). Given a group of QoScriteria {q1, q2, · · · }, user preferences, denoted as P , are aset of QoS weights (p1, p2, · · · ). Each pi ∈ P , it denotes auser’s preference on the QoS criterion qi. The preferencesmust satisfy

∑|P |i=1 pi = 1, and 0 ≤ pi ≤ 1.

For a user preference in P , it denotes a bias on itscorresponding QoS criterion by a user.

Given a set of services, a functionality request, a setof global QoS constraints and preferences, we define theproblem of QoS-aware service composition as below.

Definition 5: (QoS-aware WSC Problem). A QoS-aware WSC problem, denoted as Q-WSC, is defined by(W,C, P, rin, rout), where (1) W = {w1, w2, · · · } is a Webservice repository, (2) C = (c1, c2, · · · ) is a set of globalQoS constraints, (3) P = (p1, p2, · · · ) is a set of userpreferences, (4) rin = {r1in, r2in, · · · } is an input parameterset, and (5) rout = {r1out, r2out, · · · } is a goal specification.

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TABLE 1Input and output parameters of each operation. Column

‘Service’ denotes the Web service that an operation belongs to.Columns ‘op.I’ and ‘op.O’ are input and output parameters set.

Operation Service op.I op.O

op1 w1 {par1, par2} {par3, par4}op2 w1 {par3} {par5, par6}op3 w1 {par4} {par7, par8, par9}op4 w2 {par7, par8, par9} {par10}op5 w2 {par5, par6, par10} {par11, par12, par13}op6 w3 {par11, par12} {par14}op7 w3 {par13} {par15, par16}op8 w3 {par14, par15, par16} {par17, par18, par19}

The above Q-WSC problem defines a compositionproblem where a user can specify multiple global QoSconstraints, a set of user preferences, and a functionalityrequest based on a service repository.

Example 1. Consider a QoS-aware WSC problem(W,C, P, rin, rout). For the service repository W , it hasthree services {w1, w2, w3}, where w1 = {op1, op2, op3},w2 = {op4, op5} and w3 = {op6, op7, op8}. Table 1 showsall the operations in W , and their input and outputparameters. Table 2 shows five QoS criteria as well astheir QoS values for each operation in W . Assume thata user submits C = {240, 150, 0.40, 0.35, 3.8} as globalQoS constraints on the five QoS criteria, as shown inTable 2. Also, suppose that the user provides five QoSpreferences P = {0.25, 0.3, 0.15, 0.2, 0.1}. Finally, an inputparameters set rin = {par1, par2} and a goal specifica-tion rout = {par17, par18, par19} are also specified asfunctionality request. �

Given a request r = (rin, rout), we need to find asequence of operations L = (op1, op2, · · · , opm) from W ,such that it satisfies the functionality request. We definea service state as a set of input and/or output interfaceparameters R = {x1, x2, · · · }.

Definition 6: (Operation applicability). Given an op-eration op = (I,O,Q), it is applicable at a service stateR if op.I ⊆ R. We denote this as R⊕ op.

Above definition describes the applicability of an op-eration to a service state by checking whether inputrequirements of the operation are subsumed in the state.

When an applicable operation op is applied to R, theresulting service state R′ = R ⊕ op is R′ = R ∪ op.O,in which the values of the parameters in op.O are setby executing op. An operation sequence is an orderedlist L = (op1, op2, · · · , opm) where each element is anoperation op. Applying a sequence L to a service state Rresults in R′ = R⊕L = (· · · ((R⊕ op1)⊕ op2) · · ·⊕ opm) ifevery step is applicable (otherwise R⊕ L is undefined).

Definition 7: (Solution satisfiability). Given two ser-vice states X = {x1, x2, · · · }, Y = {y1, y2, · · · } and a setof services W , if X⊕opi⊕· · ·⊕opm ⊇ Y , 1 ≤ i,m ≤ |W |,we say (opi, · · · , opm) is a solution sequence and denotethis satisfiability as (opi ⊗ · · · ⊗ opm) ∝ (X → Y ).

TABLE 2The QoS values of each operation shown in Table 1.

OperationExec. Exec. Probability

Availability ReputationPrice Time of success

op1 26 15 0.85 0.93 4.6op2 34 22 0.90 0.88 3.3op3 18 36 0.84 0.89 5.0op4 49 19 0.96 0.95 3.7op5 37 20 0.91 0.97 4.8op6 15 11 0.98 0.86 3.1op7 35 28 0.93 0.92 3.5op8 19 23 0.82 0.75 4.1

Solution satisfiability identifies the applicability of anordered sequence of operations from a service state.

Definition 8: (Composition solution). Given a Q-WSCproblem (W,C, P, rin, rout), a composition solution to theproblem is a solution sequence, L∗ = (op1, · · · , opm),such that (op1 ⊗ · · · ⊗ opm) ∝ (rin → rout) is satisfiable.

Each composition solution corresponds to a composi-tion service graph, which reflects the invocation orderof operations including sequential and parallel onesbetween two operations, as we will define below.

Our objective is not to find any composition solution,but the one that leads to the composite service graphwith the optimal global QoS. As to global constraints andQoS optimization, we define composite service graphand then discuss them in the subsequent section.

4 QOS MODEL

Given each operation op and its QoS values Q(op) =(qprice(op), qtime(op), qsucc(op), qavail(op), qrep(op)), wenow define composite service graph and its QoS model.

4.1 Composite Service QoS and Global ConstraintsTo describe global constraints and QoS optimization of acomposite service, we first define operation dependencyand composite service graph.

Definition 9: (Operation dependency). Given a com-position solution, L∗ = (op1, op2, · · · , opm), an operationopj depends on opi (denoted as opi ≺ opj) if and onlyif i < j and there exists at least one output interfaceparameter Ok ∈ opi.O, such that opi is the last operationin op1, · · · , opj−1 that satisfies Ok ∈ opj .I .

Operation dependency describes the connectivity oftwo operations during the invocation. Based on the op-eration dependency relationships, the composite servicegraph is defined as follows.

Definition 10: (Composite service graph). Given acomposition solution, L∗ = (op1, op2, · · · , opm), its com-posite service graph is a directed acyclic graph G =(V,E), such that V = L∗ and there is an edge (opi, opj) ∈E if and only if opi ≺ opj .

A composite service graph G describes an executionprocess that provides the partial orders among the oper-ations in L∗. For example, Fig. 3 illustrates an example

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Fig. 3. A composite service graph consists of 8 operations.Each operation has a set of QoS values on their correspondingQoS criteria, as shown in Table 2.

of a composite service graph G, including 8 operationsand 11 operation dependencies.

Given a G and a group of global QoS constraintsC = (c∗(price), c∗(time), c∗(succ), c∗(avail), c∗(rep)) on{execution price, execution time, probability of success, avail-ability, reputation}, the QoS value of G on each criterionand its global constraint are as follows.

• Execution price. Given a composite service graph G,its execution price Qprice(G) is the sum of executionprices of all the operations in G. Taking the givenglobal QoS constraint c∗(price) as its upper bound,we have an inequality for the global constraint:

N∑i=1

qprice(opi) ≤ c∗(price) (1)

where N is the number of operations in G.• Execution time. Given a G, its execution timeQtime(G) depends on the maximum span of exe-cution time of operations in G. It is determined bya directed acyclic path starting from an initial stateand ending at a goal state, called critical operationpath, which consists of a subset of operations in Gsuch that the sum of their execution time is maxi-mized. That is, it is computed by the expression:

Qtime(G) =∑

opi∈CPqtime(opi)

where qtime(opi) is the execution time of opi, and opiis an operation in the critical operation path CP . Forexample, Table 2 shows the execution time of eachoperation in Fig. 3. The critical operation path in Fig.3 corresponds to CP = {op1, op3, op4, op5, op7, op8}.Based on the given c∗(time) as its upper bound, theglobal constraint on execution time is:

∑opi∈CP

qtime(opi) ≤ c∗(time) (2)

• Probability of success. Given a composite servicegraph G, its probability of success Qsucc(G) is theproduct of probability of success of all the opera-tions in G. Using c∗(succ) as a lower bound, wehave global constraint on probability of success:

N∏i=1

qsucc(opi) ≥ c∗(succ)

Since the above inequality is a nonlinear one thatcannot be easily handled by AI planners, we trans-

form it into a linear global constraint as follows:

log 12

N∏i=1

qsucc(opi) ≤ log 12c∗(succ)

or equivalently,

N∑i=1

log 12qsucc(opi) ≤ log 1

2c∗(succ) (3)

• Availability. Given a composite service graph G, itsavailability Qavail(G) is the product of availability ofall the operations in G. Taking c∗(avail) as its lowerbound, the global constraint on availability is:

N∏i=1

qavail(opi) ≥ c∗(avail)

Similarly, we transform it into a linear constraint:

N∑i=1

log 12qavail(opi) ≤ log 1

2c∗(avail) (4)

• Reputation. Given a composite service graph G,its reputation Qrep(G) is the average reputation ofall the operations in G. Using c∗(rep) as its lowerbound, the global constraint on reputation is:

∑Ni=1 qrep(opi)

N≥ c∗(rep) (5)

Notice that we calculate the reputation of a com-posite service graph G by the mean of components.However, reputation is a very subjective term andcan be evaluated by multiple rating standards. An-other possibility is to use the minimal function min()among a group of operations. Our approach can stillbe applied with this choice.

Example 2. Consider the composite service graph Gin Fig. 3. Table 2 shows QoS values of each operation.Reconsider the five global QoS constraints in Example1, where c∗(price) = 240, c∗(time) = 150, c∗(succ) =0.40, c∗(avail) = 0.35 and c∗(rep) = 3.8. The globalconstraints on five QoS criteria in G are as follows.(1)

∑8i=1 qprice(opi) = 233 ≤ c∗(price) = 240;

(2)∑opi∈CP qtime(opi) = 141 ≤ c∗(time) = 150;

(3)∑8i=1 log 1

2qsucc(opi) = 1.253 ≤ log 1

2c∗(succ) = 1.322;

(4)∑8i=1 log 1

2qavail(opi) = 1.328 ≤ log 1

2c∗(avail) = 1.515;

(5)∑8

i=1 qreq(opi)

8 = 4.013 ≥ c∗(req) = 3.8;After checking above global constraints on five QoS

criteria, G satisfies the given global QoS constraints. �

4.2 QoS Normalization and Graph OptimizationWhen calculating the QoS score of a single operation,we adopted a weighted sum of values on QoS criteria.Since they have different ranges, we first normalize theQoS values to the range of [0,1] before using them in

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the weighted sum. QoS normalization applied to eachoperation is designed to avoid frequent case, whereseveral high scores on some QoS criteria in an operationreduce the discrimination of those low scores on someother QoS criteria within the same operation. Dependingon the features of QoS criteria, normalization strategy isclassified for positive QoS criteria and negative ones.

For positive QoS criteria, such as probability of suc-cess, availability, and reputation, we denote better qual-ity by higher values. Since our formulation is a mini-mization problem, each positive QoS criterion, its QoSvalue of an operation is normalized to

qji =

⎧⎨⎩Qmaxi − qi(opj)

Qmaxi −Qmini

, if Qmaxi �= Qmini

1, otherwise

where qi(opj) and qji represent the QoS values on theith QoS criterion in opj before and after QoS normal-ization, respectively. Qmaxi and Qmini are, respectively,the maximum and minimum QoS values on the ith QoScriterion among all the operations in a repository.

For negative QoS criteria, such as execution priceand execution time, we denote lower quality by highervalues. Each negative QoS criterion, we normalize itsoriginal QoS value in an operation by:

qji =

⎧⎨⎩qi(opj)−Qmini

Qmaxi −Qmini

, if Qmaxi �= Qmini

1, otherwise

We use a QoS vector Q′(op) to represent the normal-ized values of an operation op as:

Q′(op) = (q′price(op), q′time(op), q

′succ(op), q

′avail(op), q

′rep(op))

Given a Q′(op), the overall QoS value qos(op) is cal-culated by a weighted sum of Q′(op):

qos(op) =n∑i=1

(Q′(op)[i] ∗ pi)

where Q′(op)[i] is the normalized value of op on the ithQoS criterion, n is the number of QoS criteria in op, andpi is a user’s preference on the ith QoS criterion. Theweights satisfy

∑ni=1 pi = 1 and 0 ≤ pi ≤ 1.

Given a G, its QoS value QoS(G) is calculated by thesum of the QoS values of all the operations in G.

QoS(G) =N∑i=1

qos(opi)

Example 3. Reconsider the G illustrated in Fig. 3. Weuse Table 2 as the original QoS of each operation. AfterQoS normalization, Table 3 shows the normalized QoSvalues. The user preferences on these five QoS criteriaare {0.25, 0.3, 0.15, 0.2, 0.1}, as shown in Example 1.The QoS value of each operation can be calculated.For example, the QoS of operation op1 is calculated byqos(op1) =

∑5i=1Q

′(op1)[i] ∗ pi = 0.308.

TABLE 3The normalized QoS values of each operation. The original

QoS values of each operation are shown in Table 2.

OperationExec. Exec. Probability

Availability ReputationPrice Time of success

op1 0.324 0.160 0.813 0.182 0.211op2 0.559 0.440 0.500 0.409 0.895op3 0.088 1.000 0.875 0.364 0.000op4 1.000 0.320 0.125 0.091 0.684op5 0.647 0.360 0.438 0.000 0.105op6 0.000 0.000 0.000 0.500 1.000op7 0.588 0.680 0.313 0.227 0.789op8 0.118 0.480 1.000 1.000 0.474

As a result, the QoS value of G in Fig. 3 is calculatedby QoS(G) =

∑8i=1 qos(opi) = 3.442. �

To put all pieces together, the problem we solve is thefollowing. Given a Q-WSC problem (W,C, P, rin, rout)(Definition 5), our goal is to find a composite servicegraph (Definition 10), G, such that it minimizes theoverall QoS, while functionality request (rin, rout) and allglobal QoS constraints in C are satisfied. The objectivefunction of the Q-WSC problem is:

argminG∈Gs

QoS(G) (6)

where Gs represents all of the possible composite servicegraphs to the Q-WSC problem.

To solve a given Q-WSC problem, we transform it intoa CSTE planning problem solved by our developed SCPsolver in the following.

5 QOS-AWARE SERVICE COMPOSITION US-ING AUTOMATED PLANNING

We develop a planning-based approach to optimallysolve the Q-WSC problem. Fig. 4 illustrates an overviewof our approach. It has a couple of major steps. 1)Translate a Q-WSC problem into a cost sensitive tem-porally expressive (CSTE) planning problem [35], [36],[37], which is a numeric planning problem with actionduration and cost optimization features. 2) Solve theCSTE planning problem by our developed SAT-basedcost planning (SCP) solver, which not only takes logicalreasoning and temporal planning into account, but alsooptimizes overall QoS of a composite service graph.

For a restricted class of Q-WSC problems where thereare no global QoS constraints on temporal restriction(e.g., execution time) and average-based constraint (e.g.,reputation), we can also solve the planning problemusing a numeric planner (Metric-FF [38]).

5.1 CSTE Planning ProblemWe first formally define the CSTE planning problem [35],[36], [37] and CSTE action as follows.

Definition 11: (CSTE planning problem). A cost sensi-tive temporally expressive planning problem is defined

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Fig. 4. Overview of our approach of QoS-aware WSC using numerical temporal planning.

as (A,F, V, s0, g), where A is a set of CSTE actions, F isa set of logical facts, V is a set of numeric variables, s0is the initial state, and g is a goal specification.

A CSTE planning problem describes a planning prob-lem with logical reasoning, temporal constraints plan-ning, and numerical optimization.

Definition 12: (CSTE action). A CSTE action a is de-fined by a tuple (pre, eff, μ, ρ), where

• pre(a) is the action precondition that consists of a setof numeric constraints based on numeric variablesV and a set of logical facts, each of which is anatomic proposition f ∈ F .

• eff(a) is the action effect that consists of a set ofnumeric effects based on V and a set of logical factsfrom F .

• μ(a) is the action duration.• ρ(a) is the action cost.A solution to a CSTE planning problem (A,F, V, s0, g),

is a composite dependency graph G∗ = (V,E) whichtransforms the initial state s0 to a goal state g∗, suchthat all the logical facts in g are subsumed in g∗, i.e., g ⊆g∗. Moreover, an optimal solution to a CSTE planningproblem (A,F, V, s0, g) minimizes the cost of all CSTEactions in G∗, denoted as min

∑ai∈G∗ ρ(ai).

5.2 CSTE Planning Formulation of Q-WSCGiven a Q-WSC problem (W,C, P, rin, rout), we translateit into a CSTE planning problem (A,F, V, s0, g).QoS numeric variables V. We divide V into four groupsto represent objective QoS value function, QoS numericconstraints and numeric effects.

1) an objective QoS value variable V o. It representsthe overall QoS value by the costs of all the actions in aCSTE planning state.

2) a set of QoS value variables V d. Each V d[i],it represents the sum of QoS values for the ith QoScriterion in all the operations, which correspond to theCSTE actions in a CSTE planning state.

3) a set of global QoS constraint variables V g. EachV g[i], it is transformed from the ith global QoS con-straint ci ∈ C.

4) a set of user preference variables V w. Each V w[i],it represents preference pi ∈ P on the ith QoS criterion.CSTE actions A. For each Web service w ∈ W , we

transform each of its operation op = (I,O,Q) into aCSTE action a = (pre, eff, μ, ρ).

Precondition pre(a). As to the numeric constraints inpre(a), we specify a QoS expression as an arithmeticexpression over V and the rational numbers with a setof arithmetic operators {+,−, ∗, /}. Then, by using this,we define a QoS numeric constraint as follows.

Definition 13: (QoS numeric constraint). Given a set ofV and a QoS expression, a QoS numeric constraint is atriple (v, comp, exp), where v ∈ V is a numeric variable,exp is a QoS expression, and comp ∈ {<,≤, >,≥} is acomparative operator.

A QoS numeric constraint restricts a numeric variablev to satisfy a QoS expression by a comparative operator.We use a set of QoS numeric constraints for numericrepresentation in pre(a).

1) for each input parameter Ii ∈ op.I , we introduce aprecondition fact (yes Ii) in pre(a). Here, we represent aprecondition fact by a predicate (yes ?p) which indicatesthe availability of an input or output parameter p in aCSTE planning state.

2) for each QoS value variable V d[i], if it correspondsto a negative QoS criterion by addition (e.g., executionprice), we develop a QoS numeric constraint (V g[i],≥, V d[i]+Q(op)[i]) in pre(a); otherwise, if it representsa positive QoS criterion by product (e.g., probabilityof success, availability), we introduce a QoS numericconstraint (V g[i],≥, V d[i]+log 1

2Q(op)[i]) in pre(a).

Effect eff(a). For the numeric representation in eff(a), wedefine a QoS numeric effect over a set of V and a QoSexpression.

Definition 14: (QoS numeric effect). Given a set of Vand a QoS expression, a QoS numeric effect is a triple(v, ass, exp), where v ∈ V is a variable, ass ∈ {:=, +=} isan assignment operator, and exp is a QoS expression.

A QoS numeric effect updates QoS value of a numericvariable v by assigning or adding the value of a QoSexpression. This way, we use a set of QoS numeric effectsto represent the value changes of QoS numeric variableson each QoS criterion in eff(a).

1) for each output parameter Oi ∈ op.O, we introducean effect fact (yes Oi) in eff(a).

2) for each QoS value variable V d[i], if it representsa QoS criterion by addition or average (e.g., execution

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price, reputation), we develop a QoS numeric effect(V d[i], +=, Q(op)[i]) in eff(a); otherwise, it is a QoS crite-rion by product (e.g., probability of success, availability),we introduce (V d[i], +=, log 1

2Q(op)[i]) in eff(a).

Duration μ(a). We set the duration of action a as theQoS value of execution time in op. Assume that executiontime is the ith QoS criterion in op, we have μ(a)=Q(op)[i].

Action cost ρ(a). 1) we set action cost by a weightedsum of normalized QoS values of op. Thus, we haveρ(a)=

∑ni=1(Q

′(op)[i] ∗ V w[i]), where n is the number ofQoS criteria in op. The weights satisfy

∑ni=1 V w[i] = 1,

where 0 ≤ V w[i] ≤ 1.2) for the objective QoS value variable V o, we in-

crease its QoS value by action cost ρ(a) after the invo-cation and execution of action a. So we introduce a QoSnumeric effect (V o, +=, ρ(a)) in eff(a).

Facts F. The facts include all precondition and effect factsby input and output parameters from every operationop ∈ w ∈W . That is, for each Ii ∈ op.I or Oi ∈ op.O, weadd a fact (yes Ii) or (yes Oi) to the facts F .

Initial state s0. We set initial state s0 by the initial factsand initial QoS values of the numeric variables V .

1) for each riin ∈ rin, we add an initial fact (yes riin).2) we set the objective QoS value variable V o as 0.3) for each QoS value variable V d[i], we set its initial

QoS value as 0.4) for each QoS constraint variable V g[i], if it is used

to restrict a QoS criterion by product (e.g., probability ofsuccess, availability), we set its initial value as log 1

2C[i];

otherwise, its value is directly initialized as C[i].5) for each user preference variable V w[i], we directly

set its value as the corresponding preference in P . Thatis, we set each V w[i] as user preference P [i].

Goal specification g. We set g as a set of goal facts.For each goal parameter riout ∈ rout, we add a goal fact(yes riout) in g.

Example 4. Reconsider the QoS-aware WSC problem(W,C, P, rin, rout), as shown in Example 1. After Q-WSCtemporal planning translation, it is transformed into aCSTE planning problem (A,F, V, s0, g).

The QoS numeric variables V :1) V o = qos total;2) V d = (price d, time d, succ d, avail d, rep d);3) V g = (price g, time g, succ g, avail g, rep g);4) V w = (price w, time w, succ w, avail w, rep w);After the translation, an operation in W is translated to

a CSTE action. We take the operation op1 as an example,its corresponding CSTE action a1 is as follows.

For the precondition pre(a1):1) precondition facts: {(yes par1), (yes par2)};2) QoS numeric constraints: {(price g,≥, price d +

26), (succ g,≥, succ d+ log 120.85), (avail g,≥, avail d+

log 120.93)};

For the effect eff(a1):1) effect facts: {(yes par3), (yes par4)};

2) QoS numeric effects: {(price d, +=, 26), (succ d, +=,log 1

20.85), (avail d, +=, log 1

20.93), (rep d, +=, 4.6)};

For the action duration μ(a1):It is the QoS value on execution time of the operation

op1, so we have μ(a1)=15.For the action cost ρ(a1):1) we set cost ρ(a1) as the QoS value of operation op1.

Thus, we have ρ(a1) =∑5i=1(Q

′(op)[i] ∗ V w[i]) = 0.308,where V w[i] equals with the ith user preference P [i].

2) add ρ(a1) to V o: {(qos total, +=, 0.308)};After the translation, we get 8 CSTE actions in A =

{a1, a2, · · · , a8}, each of which is transformed from itscorresponding operation in Example 1.

The facts F consists of all the atomic propositions byinput and output parameters of each operation. Thus,we have F = {(yes par1), (yes par2), · · · , (yes par19)}.

The initial state s0 consists of five parts:1) initial facts: {(yes par1), (yes par2)};2) initial objective QoS value: qos total = 0;3) initial value on each QoS criterion: (price d = 0,

time d = 0, succ d = 0, avail d = 0, rep d = 0);4) global QoS constraints: (price g = 240, time g =

150, succ g = 1.322, avail g = 1.515, rep g = 3.8);5) user preferences: (price w = 0.25, time w = 0.3,

succ w = 0.15, avail w = 0.2, rep w = 0.1);Finally, goal specification consists of a set of goal facts

by rout, so g = {(yes par17), (yes par18), (yes par19)}. �

5.3 Time Complexity Analysis of CSTE TranslationThe CSTE planning formulation of Q-WSC consists ofa CSTE domain and a CSTE problem. The former partmodels each operation op ∈ w in W as a CSTE action a ∈A. The latter translates a functionality request (rin, rout)and QoS requirements (C,P ) to an initial state so and agoal state g.

Since CSTE domain translation is dominated by theconversion time from all of the operations in W to theirCSTE actions in A, its time complexity is calculated byO(

∑w∈W

∑op∈w((|op.I|+|V d|+|op.O|+|V d|)+(n+3))),

where n is the number of QoS criteria in an operation. Weuse N and M to denote the number of services in W andthe maximum number of operations in a Web service.Furthermore, K = maxop∈w {|op.I|+ |op.O|} is an upperbound on the number of parameters in an operation inW . Meanwhile, |V d| is equal to n. So the time complexi-ty is O(NM(K+2n)+NM(n+3)) = O(NM(K+3n+3)).For a large scale W , we have N � M , N � K, N � n,and K � n. As a result, the time complexity of CSTEdomain translation is O(NMK).

The time complexity of CSTE problem translation isdominated by the number of numeric variables. The timecomplexity is bounded by O(|V d|+|V g|+|V w|+|rin|+|rout|). Since the number of numeric variables is equalto the number of QoS criteria n, the time complexityis O(3n + |rin| + |rout|). In a composition request, wehave n > |rin|, n > |rout|. Thus, the time complexity ofCSTE problem translation is O(n), which is linear to thenumber of QoS criteria.

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Fig. 5. The architecture of the SCP planner.

5.4 CSTE Planning by the SCP SolverGiven a CSTE planning instance transformed from a Q-WSC problem, we solve it by a CSTE planner, called theSCP solver [35], [36], [37]. SCP translates a CSTE probleminto an optimization problem with Satisfiability (SAT)constraints and multiple global constraints, denoted asQoS-MinCost SAT problem and defined as follows.

Definition 15: (QoS-MinCost SAT Problem) A QoS-MinCost SAT problem is a tuple Φc = (U,L, δ), where Uis a set of boolean variables, L is a set of clauses, andδ is a set of cost function {δqos, δprice, δsucc, δavail, δreq},where each δi : U → N. A solution to Φc is a variableassignment ψ that minimizes the objective function:

QoS(ψ) =∑x∈U

δqos(x)vψ(x),

subject to:vψ(p) = 1, ∀p ∈ L,

and multiple global constraints on QoS criteria:∑x∈U

δprice(x)vψ(x) ≤ c∗(price);

∑x∈U

δsucc(x)vψ(x) ≤ log 12c∗(succ);

∑x∈U

δavail(x)vψ(x) ≤ log 12c∗(avail);

∑x∈U

δreq(x)vψ(x) ≥ c∗(rep).

Fig. 5 shows the architecture of the SCP planner. Basedon our previous work [36], we turn a CSTE instance intoan optimization problem with SAT-based constraints,which is called a MinCost SAT instance as defined above.To solve the encoded MinCost SAT instance, we developBB-CDCL, a Branch-and-Bound algorithm based on theConflict Driven Clause Learning (CDCL) procedure. Theplanning algorithm follows the bounded SAT solvingstrategy, originally proposed in SATPlan . It starts from alower bound of the makespan (N=1), encodes the CSTEplanning problem as a MinCost SAT instance, either

proves it unsatisfiable and increase the makespan by 1,or finds an optimal solution to the MinCost SAT instance.

Given a Q-WSC problem (W,C, P, rin, rout), after ourCSTE planning formulation and problem solving by theSCP solver, we can generate a composite dependencygraph which describes the correct invocation and execu-tion order of CSTE actions, and can be directly mappedto a composite service graph by a simple mapping fromplanning actions to operations.

5.5 Numeric Planning by Metric-based PlannerFor a class of restricted Q-WSC problems, we transforma Q-WSC (W,C, P, rin, rout) into a numeric planningproblem and solve it by a numeric planner.

5.5.1 Finding a Linear Solution PlanA numeric planning problem is a CSTE planning prob-lem (A,F, V, s0, g), except that, 1) there are no temporaland average-based global constraints in s0, and 2) everyaction a ∈ A is a numeric action.

Definition 16: (Numeric action). A numeric action is aCSTE action without action duration, which is denotedas a triple a = (pre, eff, ρ). The precondition is pre(a) =(p(pre), q(pre)), where p(pre) are precondition facts andq(pre) are QoS numeric constraints. The effect is eff(a) =(p(eff), q(eff)), where p(eff) are effect facts and q(eff) areQoS numeric effects.

Given a numeric planning problem (A,F, V, s0, g), wedefine a numeric planning state S = (Sp, Sq) in itssolution space, where Sp is a fact state with a set of facts{(yes p1), (yes p2), · · · }, and Sq is a numeric state with aset of QoS values (V o, V d[1], V d[2], · · · , V d[n]).

Definition 17: (Action applicability). A numeric actiona = (pre, eff, ρ) is applicable to S (denoted as S � a), if itcan satisfy: (1) p(pre) is subsumed in S, p(pre) ⊆ Sp, and(2) QoS numeric constraints in q(pre) must be satisfiedby using the QoS value V d[i] (1 ≤ i ≤ n) in Sq , wheren is the number of QoS criteria.

When a numeric action a is applicable to a state S,the resulting planning state is S′ = S � a, whereS′p = Sp ∪ p(eff), and S′

q = Sq � q(eff), meaning thatthe QoS values (V o, V d[1], V d[2], · · · , V d[n]) in S′

q

are updated by applying the corresponding QoS valuesin Sq to QoS numeric effects in q(eff).

An action sequence, Π = (a1, a2, · · · , am), is an or-dered list of numeric actions. Applying a Π to a nu-meric planning state S results in a new planning stateS′ = S � Π = (((S � a1) � a2) � · · · � am) if every stepis applicable (otherwise S � Π is undefined).

Definition 18: (Plan satisfiability). Given a numericplanning state S = (Sp, Sq), an action sequence Π =(ai, aj , · · · , ak), and a set of propositional facts X ={(yes x1), (yes x2), · · · }, if S′ = S � Π is defined andX ⊆ S′

p, we denote it as (ai � aj � · · · � ak) �� (S → X),where 1 ≤ i, j, k ≤ |Π|.

Plan satisfiability describes the applicability of an or-dered sequence of actions to a numeric planning state.

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Fig. 6. The linear solution plan with suboptimal QoS valuefound by Metric-FF. Each numeric action ai corresponds to anoperation opi from the Q-WSC problem in Example 1.

Definition 19: (Linear solution plan). Given a numericplanning problem (A,F, V, s0, g), a linear solution planis an action sequence, Π∗ = (ai, aj , · · · , ak), such that(ai � aj � · · · � ak) �� (s0 → g) is satisfiable.

A linear solution plan, Π∗ = (ai, aj , · · · , ak), is optimal,if the objective variable V o in S∗

q has the minimal QoSvalue, where S∗ = (S∗

p , S∗q ) = so � Π∗ is a resulting

numeric planning state after applying every action a ∈Π∗ to the initial state s0.

Example 5. Reconsider the QoS-aware WSC problem(W,C, P, rin, rout), as shown in Example 1. Here, we onlyconsider global QoS constraints C = (240, 0.40, 0.35) onexecution price, probability of success and availability.After translation, we generate a numeric planning prob-lem (A,F, V, s0, g). We then use Metric-FF [38] to finda linear solution plan, as shown in Fig. 6. The solutionplan satisfies: (a1 � a3 � a4 � · · · � a8) �� (s0 → g),where s0 and g are initial state and goal state. �

We use Metric-FF [38] as an automated optimizerto address the problem, because it is a well-knownnumeric planner with the best performance in numerictrack of the International Planning Competition. Notethat, unlike SCP, it is a suboptimal planner that cannotguarantee optimality of the solution. However, it is veryefficient and gives high quality solutions in practice.

5.5.2 Constructing a Composite Dependency GraphGiven a linear solution plan, we convert it into a com-posite dependency graph which includes both sequentialand parallel order among numeric actions.

Definition 20: (Action dependency). Given a numericplanning problem (A,F, V, s0, g), and a linear solutionplan Π∗ = (a1, · · · , am), a numeric action aj depends onai (denoted as ai � aj), if and only if i < j and thereexists at least one precondition fact f ∈ p(pre) in aj ,such that f /∈ s0 and ai is the last action in a1, · · · , aj−1

that satisfies f ∈ p(eff) in ai.By performing action dependency on a given Π∗ =

(a1, · · · , am), we convert it into a composite dependencygraph G∗ as follows.

Definition 21: (Composite dependency graph). Given alinear solution plan Π∗ = (a1, · · · , am), the composite de-pendency graph is a directed acyclic graph, G∗ = (V,E),such that V = Π∗ and there is an edge (ai, aj) ∈ E if ajdepends on ai (ai � aj).

Example 6: Reconsider the linear solution plan Π∗, asshown in Fig. 6. After performing action dependency on

Fig. 7. The composite dependency graph for the linear solutionplan shown in Fig. 6.

the Π∗, Fig. 7 illustrates the generated composite depen-dency graph, where each numeric action ai correspondsto an operation opi in Example 1. �

Once a linear solution plan Π∗ with suboptimal QoSvalue is found by Metric-FF, we only need to convertthis plan into a composite dependency graph G∗ once,which still keeps suboptimal in terms of comprehensiveQoS. In fact, the conversion from Π∗ to G∗ can be donefast enough because we only need to make a conversionwithin a very small finite number of numeric actionsinvolved in Π∗. After the conversion, we directly map theG∗ to a composite service graph G by replacing actionswith their corresponding operations, as shown in Fig. 3.

6 EXPERIMENTAL EVALUATION

6.1 Experimental Setup and Datasets

Experiments are conducted on a DELL PC with IntelDual Core 3.1 GHZ CPU and 4G RAM. We implementeda prototype system in Java. We also implemented the IP-based approach [4] by automatically generating AMPLmodel and data from Web service repositories, which canbe solved by an IP solver.

We have conducted extensive experiments on 30 Webservice repositories with 7, 275 number of Web services.Table 4 shows the number of operations in each of these30 Web service repositories. They are from 6 simulat-ed predefined composition workflow models with thenumber of tasks 9, 16, 18, 7, 14 and 20, respectively. Wemark these 6 workflow models as A, B, C, D, E, andF. By using our developed module Q-services generatorto generate randomly specified number of operations,every workflow corresponds to 5 Web service reposito-ries. These repositories are generated respectively by thenumber of 5, 10, 15, 20 and 25 candidate operations foreach task in the workflow, as well as randomly addingcertain number of operations outside of the workflow.

For the QoS values of each operation in a Web servicerepository, we use the module Q-services generator to ran-domly generate 5 QoS values on a group of QoS criteria,including execution price, execution time, probability ofsuccess, availability and reputation. Every QoS value ona QoS criterion is generated by Q-services generator witha specified value domain. Specifically, the range of 5 QoSvalues for each operation in the workflow models A, B,and C is {5-100, 1-50, 0.65-1, 0.65-1, 3.5-5} on the fiveQoS criteria. On the other hand, we have the range of5 QoS values {5-50, 1-15, 0.65-1, 0.65-1, 3.5-5} for thoseoperations in the workflow models D, E, and F.

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TABLE 4The 30 Web service repositories for testing Metric-FF, SCP

solver and the IP-based approach. 5, 10, 15, 20, and 25 are thenumber of candidate operations for a task in a workflow. ‘WO’is the number of operations covered by tasks in a workflow.‘TO’ is the number of operations in a service repository.

Tasks 5 10 15 20 25

No. WO TO WO TO WO TO WO TO WO TO

A 45 55 90 110 135 165 180 220 225 275

B 80 90 160 180 240 270 320 360 400 450

C 90 100 180 200 270 300 360 400 450 500

D 35 45 70 90 105 135 140 180 175 225

E 70 80 140 160 210 240 280 320 350 400

F 100 115 200 230 300 345 400 460 500 575

6.2 QoS Value of a Composite Service Graph

6.2.1 The Results of Comprehensive QoS

The 30 Web service repositories are classified into twogroups to evaluate the QoS value of a composite servicegenerated by our approach and the IP-based approach[4]. The first group of 15 Web service repositories arefrom the workflow models A, B, and C. They are usedto test our proposed approach using Metric-FF (calledMetric-FF) and the IP-based approach (called IP-Linear),where each composition request in a service repositoryhas three global QoS constraints without temporal andaverage-based features. The second group of 15 Webservice repositories from the workflows D, E, and F areapplied to our approach using SCP solver (called SCPSolver) and the IP-based approach (called IP-SAT) withfive global QoS constraints in each request.

Table 5 shows global QoS constraints of the 30 compo-sition requests for these 30 Web service repositories. Onthe one hand, we take the 15 composition requests on thefirst group of 15 Web service repositories from workflowmodels A, B, and C. Each of these composition requestscorresponds to a Web service repository and has threeglobal QoS constraints in order on execution price, prob-ability of success, and availability. On the other hand,we take another 15 composition requests on the secondgroup of 15 Web service repositories from the workflowmodels D, E, and F. Each of them has five global QoSconstraints in the order of execution price, executiontime, probability of success, availability, and reputation.We set QoS preferences P = (0.2, 0.2, 0.2, 0.2, 0.2) on thefive QoS criteria. After Q-WSC planning formulation,each Q-WSC problem is translated to a CSTE or numericplanning problem that is fed to Metric-FF or SCP solverto find a composite service graph. Also, we transform theQ-WSC problem to an IP problem with an AMPL modeland AMPL data. Then, we use an IP solver CPLEX tofind a composite service graph. Table 6 summarizes thecomprehensive QoS of each composite service generatedby Metric-FF, SCP solver, and the IP-based approach.

6.2.2 Comparison and AnalysisBased on the above experimental results on comprehen-sive QoS, we compare our approach against the IP-basedapproach and analyze the results.

(1) Our proposed approach using planning does notdepend on a predefined workflow model for QoS-awareservice composition problem. However, the IP-basedapproach requires a predefined workflow and selectsa sequence of services for the tasks defined in thatworkflow. Although these approaches under predefinedworkflows can efficiently find a composite service graph,they cannot ensure global satisfiability and optimality fora Q-WSC problem.

(2) Our approach using Metric-FF planner can gener-ate a linear solution plan with a globally suboptimal QoSvalue. Experimental results show that the comprehensiveQoS of a composite service found by our approach usingMetric-FF outperforms that generated by the IP-basedapproach on all the test cases.

(3) In order to deal with global QoS constraints withtemporal and average-based features, we use our SCPsolver to handle planning problems with temporal ex-pressiveness and numeric reasoning. As shown in theexperimental results, the QoS value of a compositedependency graph found by our approach using SCPsolver is always better than that of a composite servicefound by the IP-based approach.

6.3 Time of Generating a Composite ServiceWe compare the computational time of our approachusing Metric-FF and SCP to the IP-based approach. Theexperiments are tested on 30 composition requests, asshown in Table 5. Our approach using Metric-FF andSCP finds a linear solution plan and a composite depen-dency graph for a composition request, respectively. TheIP-based approach using an IP solver finds a compositeservice graph for a composition request. Fig. 8 and 9show computational time of each request.

From Fig. 8 and 9, we can see that, for the IP-basedapproach, its computing time of each composition re-quest is in no more than 0.421 seconds. Our approachusing Metric-FF can find a Q-WSC solution within 0.81seconds for any composition request. For more complexproblems with temporal numeric planning, our approachusing SCP finds the optimal solution in no more than3.55 seconds for all of the 30 composition requests.

Our approach is slower than the IP-based approach.The reason is that the IP-based approach only optimizesthe QoS value under a given predefined workflow model(and hence suboptimal), while our approach using theSCP solver finds the optimal Q-WSC solution under allpossible workflows, a service composition task with amuch larger search space.

When the problem size is within the range specified byour experiments, the developed SCP planner can be in-tegrated by corporations to help workflow creators buildtheir operational business processes in real applications,

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TABLE 5Global QoS constraints within a composition request for each repository. In the first group of 15 composition requests for the

workflows A, B, and C, each composition request has three global QoS constraints in the order of execution price, probability ofsuccess, and availability. In the second group of 15 composition requests for the workflows D, E, and F, it has five global QoSconstraints in each request in the order of execution price, execution time, probability of success, availability, and reputation.

Tasks

No.

Global QoS constraints

5 10 15 20 25

A {209,0.228,0.274} {176,0.275,0.255} {183,0.289,0.240} {192,0.295,0.281} {163,0.225,0.299}B {416,0.032,0.038} {372,0.047,0.042} {359,0.042,0.078} {406,0.191,0.074} {348,0.179,0.102}C {409,0.052,0.063} {362,0.049,0.136} {417,0.055,0.087} {348,0.122,0.079} {394,0.063,0.087}

D {146,58,0.196,0.291,4.15} {127,39,0.209,0.253,4.15} {90,64,0.257,0.206,3.9} {138,43,0.291,0.235,4.4} {105,82,0.248,0.289,4.35}E {378,90,0.099,0.090,4.2} {350,102,0.079,0.148,4.3} {279,116,0.128,0.115,4.05} {248,109,0.104,0.074,4.25} {215,80,0.147,0.141,4.26}F {459,178,0.067,0.074,4.25} {364,161,0.073,0.052,4.10} {462,170,0.078,0.084,3.95} {397,150,0.075,0.052,4.0} {482,162,0.072,0.081,4.17}

TABLE 6Experimental results on the comprehensive QoS value (the lower the better) found by Metric-FF, SCP solver and the IP-based

approach. Column ‘Constraint’ represents global QoS constraints specified in a composition request, as shown in Table 5. For acomposition request on each Web service repository, it corresponds to a set of global QoS constraints Cm

p , where p is theworkflow model and m is the number of candidate operations for each task in the model. For example, C5

A represents the globalQoS constraints {209,0.228,0.274} in Table 5. Column ‘FF’ represents Metric-FF. Column ‘SCP’ represents SCP solver.

Tasks

No.

5 10 15 20 25

ConstraintQoS value

ConstraintQoS value

ConstraintQoS value

ConstraintQoS value

ConstraintQoS value

FF IP-Linear FF IP-Linear FF IP-Linear FF IP-Linear FF IP-Linear

A C5A 4.326 5.155 C10

A 3.930 5.263 C15A 4.017 5.061 C20

A 3.763 5.022 C25A 3.946 4.718

B C5B 7.892 10.260 C10

B 7.377 8.743 C15B 7.286 8.515 C20

B 6.864 8.703 C25B 6.812 8.423

C C5C 9.036 10.863 C10

C 8.423 10.007 C15C 8.869 10.103 C20

C 7.810 9.499 C25C 8.122 9.747

No. Constraint SCP IP-SAT Constraint SCP IP-SAT Constraint SCP IP-SAT Constraint SCP IP-SAT Constraint SCP IP-SAT

D C5D 3.011 4.486 C10

D 2.892 4.077 C15D 2.613 3.725 C20

D 3.312 3.839 C25D 2.618 3.777

E C5E 6.478 8.299 C10

E 6.401 8.193 C15E 7.092 7.724 C20

E 5.932 7.811 C25E 7.377 7.492

F C5F 10.130 12.000 C10

F 10.193 11.983 C15F 9.674 11.278 C20

F 9.722 10.926 C25F 9.549 11.012

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Fig. 8. Experimental results on the computational time of Q-WSC of our approach using Metric-FF planner and the IP-basedmethod IP-Linear. There are 15 composition requests tested on 15 Web service repositories.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

IEEE TRANSACTIONS ON SERVICES COMPUTING, VOL. 5, NO. 3, JULY-SEPTEMBER 2012 13

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such as airline ticket ordering, electronic commerce, andenterprise business workflow management.

From the above experimental results on computationaltime, we conclude that our approaches with Metric-FFand SCP, although slower than the IP-based approach,are still fast enough for practical use. Since our approachdelivers solutions with the optimal QoS, it should bepreferred by users who concern about QoS values suchas execution price and time.

7 CONCLUSIONS

This paper presents an AI planning-based method forQ-WSC. The method first compiles a Q-WSC probleminto a CSTE planning problem. Then, the method appliesour recently developed SCP planner to handle the CSTEplanning problem using temporal planning and numer-ical optimization and find a composite service graphwith the optimal QoS. For a restricted class of Q-WSCproblems, we transform them into a numeric planningproblem, which can be solved by a metric-based plannerMetric-FF. Our approach significantly extends the capa-bility of prior work by ensuring global satisfiability andoptimality without assuming a predefined workflow.The experimental results demonstrate that our proposedapproach is fast enough for practical deployment, thanksto the highly efficient automated planners.

ACKNOWLEDGMENTS

This work was partially supported by an NSF grant IIS-0713109, a Microsoft Research New Faculty Fellowship,a Shanghai Leading Academic Discipline Project grantJ50103, and an Innovation Program of Shanghai Munic-ipal Education Commission grant 11ZZ85.

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Guobing Zou is an assistant professor in theschool of computer engineering and science atShanghai University from 2012. He received ajoint Ph.D. degree in computer science fromTongji University and Washington University inSt. Louis in 2012. His research interests includeWeb service composition, automated planning,semantic Web and information retrieval. He haspublished 17 papers on international journalsand conferences, including AAAI-12, Soft Com-puting, Journal of Tongji University, FSKD-09,

and CCV-10. He currently serves as a program committee member onCIT-12 and UUMA-12. He worked as a reviewer for Journal of ArtificialIntelligence Research, KDD-09, AAAI-10, and ICDM-10.

Qiang Lu is currently a postdoctoral researcherin the school of computer science and technolo-gy at the University of Science and Technologyof China (USTC). He received the B.E. degreeand the Ph.D. degree in school of computerscience and technology from USTC in 2007 and2012. His research interests include automatedplanning and scheduling, parallel and distributedcomputing, and cloud computing. He receivedthe Joint Ph.D. Training Scholarship from theChina Scholarship Council (2009). Lu is a stu-

dent member of ACM and CCF. He has published more than 10 paperson journals and conferences, including ACM Transactions on IntelligentSystems and Technology, ICAPS-11, CloudCom-11, and IPC-11.

Yixin Chen is an associate professor of com-puter science at the Washington University inSt. Louis. He received the Ph.D. degree in com-puter science from the University of Illinois atUrbana-Champaign in 2005. His research inter-ests include nonlinear optimization, constrainedsearch, planning and scheduling, data mining,and data warehousing. His work on planning haswon First-Class Prizes in the International Plan-ning Competitions (2004 and 2006). He has wonthe Best Paper Award in AAAI (2010) and ICTAI

(2005), and Best Paper nomination at KDD (2009). He has receivedan Early Career Principal Investigator Award from the Departmentof Energy (2006) and a Microsoft Research New Faculty Fellowship(2007). Dr. Chen is a senior member of IEEE. He serves as an associateeditor on the IEEE Transactions on Knowledge and Data Engineering,and ACM Transactions on Intelligent Systems and Technology.

Ruoyun Huang is currently a software engineerat Google, working in large scale intelligent sys-tems. He received the Ph.D degree in computerscience from Washington University in St. Louisin August, 2011. His research interests includeautomated planning, large scale intelligent sys-tems, Web service composition, and probabilis-tic inference. He has published more than 12 pa-pers on top journals and conferences, includingJournal of Artificial Intelligence Research (JAIR-12), Artificial Intelligence (AIJ-09), AAAI (2008,

2010, 2012) and ICAPS-09. He won AAAI’10 Outstanding Paper Award.Before his Ph.D study, he also worked in Bearingpoint consulting.

You Xu is currently a Ph.D. candidate in thedepartment of computer science and engineer-ing at Washington University in St. Louis. Hereceived the B.Sc. degree in mathematics fromNanjing University in 2006, and the M.Sc. de-gree in computer science from the WashingtonUniversity in St. Louis in 2009. He is now work-ing for Google in Chicago. His research inter-ests include large-scale nonlinear optimization,constrained search, and partial order reductionfor planning, and automated planning in cloud

computing. He has published 12 papers, including RTAS-12, RTAS-11,MobiHoc-10, RTSS-10, and IJCAI-09.

Yang Xiang is a professor in the departmentof computer science and technology at TongjiUniversity. He received the Ph.D. degree in man-agement science and engineering from HarbinInstitute of Technology in 1999. His research in-terests include data warehousing and data min-ing, intelligent decision support system, servicecomputing and e-commerce. He has publishedmore than 100 papers on the international jour-nals and conferences, including Expert System-s with Applications, Science China Information

Sciences, and Chinese Journal of Electronics. He has published 4 bookson intelligent decision support system in complex problems.

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.