Combining Global Optimization with Local Selection for Efﬁcient...

Combining Global Optimization with Local Selection forEfficient QoS-aware Service Composition

Mohammad AlrifaiL3S Research CenterUniversity of Hannover

[email protected]

Thomass RisseL3S Research CenterUniversity of Hannover

[email protected]

ABSTRACT

The run-time binding of web services has been recently putforward in order to support rapid and dynamic web ser-vice compositions. With the growing number of alternativeweb services that provide the same functionality but differin quality parameters, the service composition becomes adecision problem on which component services should be se-lected such that user’s end-to-end QoS requirements (e.g.availability, response time) and preferences (e.g. price) aresatisfied. Although very efficient, local selection strategyfails short in handling global QoS requirements. Solutionsbased on global optimization, on the other hand, can han-dle global constraints, but their poor performance rendersthem inappropriate for applications with dynamic and real-time requirements. In this paper we address this problemand propose a solution that combines global optimizationwith local selection techniques to benefit from the advan-tages of both worlds. The proposed solution consists of twosteps: first, we use mixed integer programming (MIP) tofind the optimal decomposition of global QoS constraintsinto local constraints. Second, we use distributed local se-lection to find the best web services that satisfy these localconstraints. The results of experimental evaluation indicatethat our approach significantly outperforms existing solu-tions in terms of computation time while achieving close-to-optimal results.

Categories and Subject Descriptors

H.3.5 [On-line Information Services]: Web-based ser-vices; H.3.4 [Systems and Software]: Distributed systems

General Terms

Management, Performance, Measurement

Keywords

Web Services, QoS, Optimization, Service Composition

1. INTRODUCTIONThe service-oriented computing paradigm and its realiza-

tion through standardized web service technologies providea promising solution for the seamless integration of businessapplications to create new value-added services. Industrial

Copyright is held by the International World Wide Web Conference Com-mittee (IW3C2). Distribution of these papers is limited to classroom use,and personal use by others.WWW 2009, April 20–24, 2009, Madrid, Spain.ACM 978-1-60558-487-4/09/04.

practice witnesses a growing interest in the ad-hoc servicecomposition in the areas of supply chain management, ac-counting, finances, eScience as well as in multimedia applica-tions. With the growing number of alternative web servicesthat provide the same functionality but differ in quality pa-rameters, the composition problem becomes a decision prob-lem on the selection of component services with regards tofunctional and non-functional requirements.

Figure 1: Web Service Composition Example

Consider for example the personalized multimedia deliv-ery scenario (from [21]) in Figure 1. A smartphone userrequests the latest news from a service provider. Availablemultimedia content includes a news ticker and topical videosavailable in MPEG 2 only. The news provider has no adapta-tion capabilities, so additional services are required to servethe user’s request: a transcoding service for the multime-dia content to fit the target format, a compression serviceto adapt the content to the wireless link, a text translationservice for the ticker, and also a merging service to inte-grate the ticker with the video stream for the limited smart-phone display. The user request can be associated with someend-to-end QoS requirements (like bandwidth, latency andprice). The service composer has to ensure that the aggre-gated QoS values of the selected services match the userrequirements at the start of the execution as well as duringthe execution. However, dynamic changes due to changesin the QoS requirements (e.g. the user switched to a net-work with lower bandwidth) or failure of some services (e.g.some of the selected services become unavailable) can occurat run-time. Therefore, a quick response to adaptation re-quests is important in such applications. The performanceof the service selection middleware can have a great impacton the overall performance of the composition system.

Figure 2 gives a conceptual overview of the QoS-aware ser-vice composition problem. Given an abstract compositionrequest, which can be stated in a workflow-like language (e.g.BPEL [19]), the discovery engine uses existing infrastructure(e.g. UDDI) to locate available web services for each task inthe workflow using syntactic (and probably semantic) func-tional matching between the tasks and service descriptions.As a result, a list of candidate web services is obtained foreach task. The goal of QoS-aware service selection middel-

WWW 2009 MADRID! Track: Web Engineering / Session: Service Oriented Development

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Figure 2: Conceptual Overview

ware is to select one component service from each list suchthat the aggregated QoS values satisfy the user’s end-to-endQoS requirements. In service oriented environments, wheredeviations from the QoS estimates occur and decisions uponreplacing some services has to be taken at run-time (e.g.in the multimedia application above), the efficiency of theapplied selection mechanism becomes crucial. The focus ofthis paper is on the selection of web services based on theirnon-functional properties and the performance of the ap-plied techniques.

Local Selection vs. Global Optimization

Two general approaches exist for the QoS-aware service com-position: local selection and global optimization.

Local Selection: The local selection approach is especiallyuseful for distributed environments where central QoSmanagement is not desirable and groups of candidateweb services are managed by distributed service bro-kers [8, 14]. The idea is to select one service from eachgroup of service candidates independently on the othergroups. Using a given utility function, the values ofthe different QoS criteria are mapped to a single util-ity value and the service with maximum utility valueis selected. This approach is very efficient in terms ofcomputation time as the time complexity of the localoptimization approach is O(l), where l is the number ofservice candidates in each group. Even if the approachis useful in decentralized environments, local selectionstrategy is not suitable for QoS-based service composi-tion, with end-to-end constraints (e.g. maximum totalprice), since such global constraints cannot be verifiedlocally.

Global Optimization: The global optimization approachwas recently put forward as a solution to the QoS-aware service composition problem [24, 25, 5, 6]. Thisapproach aims at solving the problem on the com-posite service level. The aggregated QoS values ofall possible service combinations are computed, andthe service combination that maximizes the aggregated

utility value, while satisfying global constraints is se-lected. The global selection problem can be modeledas a Multi-Choice Multidimensional Knapsack problem(MMKP), which is known to be NP-hard in the strongsense [20]. Consequently, it can be expected that anoptimal solution may not be found in a reasonableamount of time [16]. The exponential time complex-ity of the proposed solutions [24, 25, 5, 6] is only ac-ceptable if the number of service candidates is verylimited. Already in larger enterprises and even morein open service infrastructures with a few thousandsof services the response time for a service compositionrequest could already be out of the real-time require-ments.

Contribution of the Paper

Since the QoS requirements (e.g. response times, through-put or availability) are only approximate, we argue that find-ing a “reasonable” set of services that avoid obvious viola-tions of constraints at acceptable costs is more importantthan finding ”‘the optimal”’ set of services with a very highcost. In addition, we advocate that the selection of compo-nent services should be carried out in a distributed fashion,which fits well to the open web service environment, wherecentral management is not feasible.

The contribution of this paper can be stated as follows:

• A distributed QoS computation model for web services.Unlike existing solutions that model the QoS-awareservice composition problem as a conventional globaloptimization problem, we exploit the special structureof the web service composition problem to reduce thecost of QoS optimization. The QoS optimization inour model is carried out by a set of distributed servicebrokers. The idea is to decompose QoS global con-straints into a set of local constraints that will serve asa conservative upper/lower bounds, such that the sat-isfaction of local constraints by a local service brokerguarantees the satisfaction of the global constraints.

• An efficient QoS-aware service selection approach. Wepropose an efficient and scalable mechanism for se-lecting web services for a given composition requestfrom a collection of service candidates, such that thefulfillment of user’s end-to-end QoS requirements andpreferences can be ensured. By combining global op-timization with local selection our approach is able toefficiently solve the selection problem in a distributedmanner. Experimental evaluations show that our hy-brid approach is able to reach close-to-optimal resultsmuch faster than existing ’pure’ global optimizationapproaches.

The rest of the papers is organized as follows. In the nextsection we discuss related work. Section 3 introduces the sys-tem model and gives a problem statement. Our approach forefficient and distributed QoS-aware service selection is pre-sented in Section 4. Performance analysis and experimentalevaluations for comparing our solution against existing so-lutions are presented in Section 5. Finally, Section 6 givesconclusions and an outlook on possible continuations of ourwork.


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2. RELATED WORKThe requirements for composition of web services can be

stated in a workflow language such as Business Process Exe-cution Language (BPEL) [19]. In [26, 9] ontology-based rep-resentations for describing QoS properties and requests wereproposed to support semantic and dynamic QoS-based dis-covery of web services. Quality of service management hasbeen widely discussed in the area of middleware systems [7,11, 12, 13]. Most of these works focus on QoS specificationand management. Recently, the QoS-based web service se-lection and composition in service-oriented applications hasgained the attention of many researchers [24, 25, 5, 6, 15,23]. In [15] the authors propose an extensible QoS compu-tation model that supports open and fair management ofQoS data. The problem of QoS-based composition is notaddressed by this work. The work of Zeng at al. [24, 25]focuses on dynamic and quality-driven selection of services.The authors use global planning to find the best service com-ponents for the composition. They use (mixed) linear pro-gramming techniques [18] to find the optimal selection ofcomponent services. Similar to this approach Ardagna etal. [5, 6] extends the linear programming model to includelocal constraints. Linear programming methods are very ef-fective when the size of the problem is small. However, thesemethods suffer from poor scalability due to the exponen-tial time complexity of the applied search algorithms [16].In [23] the authors propose heuristic algorithms that can beused to find a near-to-optimal solution more efficiently thanexact solutions. The authors propose two models for theQoS-based service composition problem: 1) a combinatorialmodel and 2) a graph model. A heuristic algorithm is intro-duced for each model. The time complexity of the heuristicalgorithm for the combinatorial model (WS HEU) is polyno-mial, whereas the complexity of the heuristic algorithm forthe graph model (MCSP-K) is exponential. Despite the sig-nificant improvement of these algorithms compared to exactsolutions, both algorithms do not scale with respect to anincreasing number of web services and remain out of the real-time requirements. Any distributed implementation of thesealgorithms would raise a very high communication cost. TheWS HEU for example, is an improvement of the originalheuristic algorithm for solving general Multi-Choice Multi-dimensional Knapsack problems named M-HEU [1]. TheWS HEU algorithm starts with a pre-processing step forfinding an initial feasible solution, i.e. a service combinationthat satisfies all constraints but not necessarily is the bestsolution. A post-processing step improves the total utilityvalue of the solution with one upgrade followed by one ormore downgrades of one of the selected component services.Applying this algorithm in a distributed setting where theQoS data of the different service classes is managed by dis-tributed service brokers would raise very high communica-tion cost among these brokers to find the best composition.In this paper, we propose a heuristic algorithm that solvesthe composition problem more efficiently and fits well to thedistributed environment of web services.

3. SYSTEM MODELIn our model we assume that we have a universe of web

services S which is defined as a union of abstract serviceclasses. Each abstract service class Sj ∈ S (e.g. flightbooking services) is used to describe a set of functionally-

equivalent web services (e.g. Lufthansa and Qantas flightbooking web services). In this paper we assume that infor-mation about service classes is managed by a set of servicebrokers as described in [15, 14]. Web services can join andleave service classes at any time by means of a subscriptionmechanism.

3.1 Abstract vs. Concrete Composite ServicesAs shown in Figure 2 we distinguish in the composition

process between the following two concepts:

• An abstract composite service, which can be definedas an abstract representation of a composition requestCSabstract = {S1, . . . , Sn}. CSabstract refers to therequired service classes (e.g. flight booking) withoutreferring to any concrete web service (e.g. Lufthansaflight booking web service).

• A concrete composite service, which can be defined asan instantiation of an abstract composite service. Thiscan be obtained by binding each abstract service classin CSabstract to a concrete web service sj , such thatsj ∈ Sj . We use CS to denote a concrete compositeservice.

3.2 QoS CriteriaIn our study we consider quantitative non-functional prop-

erties of web services, which can be used to describe thequality criteria of a web service [24, 15]. These can includegeneric QoS attributes like response time, availability, price,reputation etc, as well as domain-specific QoS attributes likebandwidth for multimedia web services as long as these at-tributes can be quantified and represented by real numbers.We use the vector Qs = {q1(s), . . . , qr(s)} to represent theQoS attributes of service s, where the function qi(s) de-termines the value of the i-th quality attribute of s. Thevalues of these QoS attributes can be either collected fromservice providers directly (e.g. price), recorded from previ-ous execution monitoring (e.g. response time) or from userfeedbacks (e.g. reputation) [15]. The set of QoS attributescan be divided into two subsets: positive and negative QoSattributes. The values of positive attributes need to be maxi-mized (e.g. throughput and availability), whereas the valuesof negative attributes need to be minimized (e.g. price andresponse time). For the sake of simplicity, in this paper weconsider only negative attributes (positive attributes can beeasily transformed into negative attributes by multiplyingtheir values by -1).

3.3 QoS Computation of Composite ServicesThe QoS value of a composite service is decided by the

QoS values of its component services as well as the composi-tion model used (e.g. sequential, parallel, conditional and/orloops). In this paper, we focus on the sequential composi-tion model. Other models may be reduced or transformedto the sequential model. Techniques for handling multipleexecution paths and unfolding loops from [10], can be usedfor this purpose.

The QoS vector for a composite service CS is defined asQCS = {q′1(CS), . . . , q′r(CS)}. q′i(CS) represents the esti-mated value of the i-th QoS attribute of CS and can beaggregated from the expected QoS values of its component


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services. In our model we consider three types of QoS ag-gregation functions: 1) summation, 2) multiplication and 3)minimum relation. Table 1 shows examples of these aggre-gation functions.

Aggregation

type

Examples Function

Summation Responsetime

q′(CS) =∑n

j=1q(sj)

PriceReputation q′(CS) = 1/n

∑n

j=1q(sj)

Multiplication Availability q′(CS) =∏n

j=1q(sj)

ReliabilityMinimum Throughput q′(CS) = minn

j=1 q(sj)

Table 1: Examples of QoS aggregation functions

3.4 Global QoS ConstraintsGlobal QoS constraints represent user’s end-to-end QoS

requirements. These can be expressed in terms of upper(and/or lower ) bounds for the aggregated values of the dif-ferent QoS criteria. As mentioned earlier, we only considernegative QoS criteria. Therefore in our model we only haveupper bound constraints.

Definition 1. (Feasible Selection) Let CSabstract be a givencomposition request and C′ = {c′1, . . . , c

′m}, 0 ≤ m ≤ r, be

a vector of global QoS constraints on CSabstract. Let CSbe an instantiation of CSabstract, in which a concrete webservice is selected for each service class. We consider CSa feasible selection iff q′(CS) ≤ c′,∀c′k ∈ C′, i.e. all globalconstraints are satisfied.

3.5 Utility FunctionIn order to evaluate the multi-dimensional quality of a

given web service a utility function is used. The functionmaps the quality vector Qs into a single real value, to enablesorting and ranking of service candidates. In this paper weuse a Multiple Attribute Decision Making approach for theutility function: i.e. the Simple Additive Weighting (SAW)technique [22]. The utility computation involves scaling theQoS attributes’ values to allow a uniform measurement ofthe multi-dimensional service qualities independent of theirunits and ranges. The scaling process is then followed by aweighting process for representing user priorities and pref-erences. In the scaling process each QoS attribute value istransformed into a value between 0 and 1, by comparing itwith the minimum and maximum possible value accordingto the available QoS information of service candidates. Fora composite service CS = {S1, . . . , Sn}, the aggregated QoSvalues are compared with minimum and maximum possibleaggregated values. The minimum (or maximum) possibleaggregated values can be easily estimated by aggregatingthe minimum (or maximum) value of each service class inCS. For example, the maximum execution price of CS canbe computed by summing up the execution price of the mostexpensive service candidate in each service class in CS. For-mally, the minimum and maximum aggregated values of the

k-th QoS attribute of CS are computed as follows:

Qmin′(k) =n∑

j=1

Qmin(j, k) (1)

Qmax′(k) =n∑

j=1

Qmax(j, k)

with

Qmin(j, k) = min∀sji∈Sj

qk(sji) (2)

Qmax(j, k) = max∀sji∈Sj

qk(sji)

where Qmin(j, k) is the minimum value (e.g. minimumprice) and Qmax(j, k) is the maximum value (e.g. maximumprice) that can be expected for service class Sj according tothe available information about service candidates of thisclass.

Now the utility of a component web service s ∈ Sj iscomputed as

U(s) =

r∑

k=1

Qmax(j, k) − qk(s)

Qmax(j, k) − Qmin(j, k)· wk (3)

and the overall utility of a composite service is computed as

U ′(CS) =r∑

k=1

Qmax′(k) − q′k(CS)

Qmax′(k) − Qmin′(k)· wk (4)

with wk ∈ R+

0 and∑r

k=1wk = 1 being the weight of q′k to

represent user’s priorities.

Definition 2. (Optimal Selection) The optimal selectionfor a given web service composition request CSabstract and agiven vector of global QoS constraints C′ = {c′1, . . . , c

′m}, 0 ≤

m ≤ r, is a feasible selection (according to Definition 1) withthe maximum overall utility value U ′.

However, finding the optimal composition requires enu-merating all possible combinations of service candidates. Fora composition request with n service classes and l servicecandidate per class, there are ln possible combinations tobe examined. Performing exhaustive search can be very ex-pensive in terms of computation time and, therefore, inap-propriate for run-time service selection in applications withmany services and dynamic needs.

3.6 Problem StatementThe problem of finding the best service composition with-

out enumerating all possible combinations is considered asan optimization problem, in which the overall utility valuehas to be maximized while satisfying all global constraints.Formally, the optimization problem we are addressing canbe stated as follows:

For a given composition request CSabstract = {S1, . . . , Sn}and a given set of m global QoS constraints C′ = {c′1, . . . , c

′m},

find an implementation CS = {s1, . . . , sn} by binding eachSj to a concrete service sj ∈ Sj such that:

1. The overall utility U ′(CS) is maximized, and

2. The aggregated QoS satisfy: q′k(CS) ≤ c′k,∀c′k ∈ C′


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Figure 3: Distributed QoS-aware Service Selection

4. QOS-AWARE SERVICE COMPOSITIONThe use of mixed integer programming [18] to solve the

QoS-aware service composition problem has been recentlyproposed by several researchers [24, 25, 5, 6]. Binary deci-sion variables are used in the model to represent the servicecandidates. A service candidate sij is selected in the opti-mal composition if its corresponding variable xij is set to 1in the solution of the model and discarded otherwise. Byre-writing (4) to include the decision variables, the problemof solving the model can be formulated as a maximizationproblem of the overall utility value given by

r∑

k=1

Qmax′(k) −∑n

j=1

∑l

i=1qk(sji) · xji


subject to the global QoS constraints

n∑

j=1

l∑

i=1

qk(sji) · xji ≤ c′k, 1 ≤ k ≤ m · wk (6)

while satisfying the allocation constraints on the decisionvariables as

l∑

i=1

xji = 1, 1 ≤ j ≤ n. (7)

Because the number of variables in this model dependson the number of service candidates (number of variables =n·l), this MIP model may not be solved satisfactorily, exceptfor small instances. Another disadvantage of this approachis that it requires that the QoS data of available web servicesbe imported from the service broker into the MIP model ofthe service composer, which raises high communication.

To cope with these limitations, we divide the QoS-awareservice composition problem into two sub-problems that canbe solved more efficiently in two subsequent phases. Figure 3gives an overview on our approach. In the first phase, the

service composer decomposes each global QoS constraintsinto local constraints on the component services level andsends these constraints to the involved service brokers. Theseconstraints also include user’s preferences, which are ex-pressed in terms of weights of the QoS attributes. In thesecond phase, each service broker performs local selectionto find the best component services that satisfy these localconstraints. The two phases of our approach are describedin the next subsections in more details.

4.1 Decomposition of Global QoS ConstraintsTo ensure the fulfillment of global QoS constraints in a

service composition problem without enumerating all possi-ble combinations of component web service, we decomposeeach QoS global constraint c′ into a set of n local constraintsc1, . . . , cn (n is the number of abstract service classes in thecomposition request). The local constraints serve as a con-servative upper bounds, such that the satisfaction of localconstraints guarantees the satisfaction of global constraints.

A naive decomposition algorithm would be to divide eachglobal constraint c′ into n equal local constraints such that:cj = c′/n, 1 ≤ j ≤ n. However, as different service classescan have different QoS value ranges, a more sophisticateddecomposition algorithm is required. Furthermore, in or-der to avoid discarding any service candidates that mightbe part of a feasible composition, the decomposition algo-rithm needs to ensure that the local constraints are relaxedas much as possible while meeting global constraints. Wesolve this problem by modeling the QoS constraint decom-position problem as an optimization problem. The goal ofthis optimization problem is to find a set of local constraintsfor each service class that cover as many as possible servicecandidates, while their aggregation does not violate any ofthe global constraints. To this end, we divide the qualityrange of each QoS attribute into a set of discrete quality val-ues, which we call quality levels. We then map each global


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QoS constraint into a set of these quality levels, which willbe used as local constraints by the local service selection al-gorithm. For example, given a set of candidate web servicesand their execution prices, we create a list of price levels forthat service class (the following subsection describes howlevels are determined). The global constraint on total ex-ecution price is then mapped to the price levels of serviceclasses. We use mixed integer program (MIP) [18] solvingtechniques to find the best mapping of global constraints tolocal quality levels. Unlike the MIP model in [24, 25, 5, 6],our MIP model has much less number of variables (i.e. thequality levels instead of actual service candidates) and canbe, therefore, solved much faster.

4.1.1 Determining Quality Levels

Quality levels are initialized for each service class Sj bydividing the value ranges of each QoS attribute qk into a setof d discrete quality values as depicted in figure 4

Qmin(j, k) ≤ q1jk ≤ . . . ≤ qd

jk ≤ Qmax(j, k).

The quality levels are determined such that they representthe data collection of each service class. We first dividethe range of attribute values into d sub-ranges. From eachsub-range we randomly select one sample value. The morefrequent a given value is, the higher the probability that itis selected as a quality level. We then assign each qualitylevel qz

jk a value pzjk between 0 and 1, which estimates the

benefit of using this quality level as a local constraint. Thisvalue is determined as follows. First, we compute h(qz

jk),i.e. the number of candidate services that would qualify ifthis level was used as local constraint. Second, we calculatethe utility value of each service candidate in the service classusing the utility function (3) and determine u(qz

jk), i.e. thehighest utility value that can be obtained by consideringthese qualified services. Finally, pz

jk can be calculated as

pzjk =

h(qzjk)

l·u(qz

jk)

umax

(8)

where l is the total number of service candidates of serviceclass Sj , and umax is the highest utility value that can beobtained for this class by considering all service candidates.The value pz

jk indicates how many web services would qualifyif the z-th level was used as local constraint for the k-thQoS attribute in service class Sj , and estimates the highestobtainable utility value for that class.

Figure 4: Quality Level Selection

4.1.2 Formulating the MIP Model

We use MIP model to find the best decomposition of QoSconstraints into local constraints. Therefore, we use a binarydecision variable xz

jk for each local quality level qzjk such that

xzjk = 1 if qz

jk is selected as a local constraint for the QoSattribute qk at the service class Sj , and xz

jk = 0 otherwise.Therefor, we use the following allocation constraints in

the model:

∀j,∀k :

d∑

z=1

xzjk = 1 , 1 ≤ j ≤ n , 1 ≤ k ≤ m (9)

Note that the total number of variables in the modelequals to n · m · d, i.e. it is independent of the numberof service candidates. If the number of quality levels d sat-isfies m ·d ≤ l we can ensure that the size of our MIP modelis smaller than the size of the model used in [24, 25, 5, 6](where the number of decision variables is n · l), thus can besolved much faster.

The objective function of our MIP model is to maximizethe p value (as defined in 8) of the selected local constraintsto minimize the number of discarded feasible selections. There-fore, the objective function can be expressed as follows:

maximize

n∏

j=1

m∏

k=1

pzjk , 1 ≤ z ≤ d (10)

We use the logarithmic function to linearize (10) in orderto be able to use it in the MIP model:

maximizen∑

j=1

m∑

k=1

d∑

z=1

ln(pzjk) ∗ xz

jk (11)

The selection of the local constraints must ensure thatglobal constraints are still satisfied. Therefore, we add thefollowing set of constraints to the model:

∀k :

n∑

j=1

d∑

z=1

qzjk · xz

jk ≤ c′k , 1 ≤ k ≤ m (12)

By solving this model using any MIP solver methods, weget a set of local quality levels. These quality levels arethen sent to the distributed set of involved service brokersto perform local selection.

4.2 Local SelectionAfter decomposing global QoS constraints into local ones,

the second step of our solution is to perform local selec-tion for each service class independently. Upon the receiptof local constraints and user’ preferences from the servicecomposer, each service broker performs the local selectionand returns the best web service candidate to the servicecomposer. The received local constraints are used as upperbounds for the QoS values of component services. Web ser-vices that violate these upper bounds are skipped from theselection. A list of qualified services is created and sortedby their utility values.

The use of (3) for this purpose is not appropriate forthe following reason. This utility function compares thedistance Qmax(j, k) − qk(sji) between the quality value ofa service candidate sji and the local maximum value inits class Sj with the distance Qmax(j, k) − Qmin(j, k) be-tween the local minimum and maximum values. This scal-ing approach can be biased by local properties leading tolocal optima instead of global optima. Therefore, we com-pare the distance Qmax(j, k) − qk(sji) with the distance


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between the maximum and minimum overall quality values:Qmax′(k) − Qmin′(k). This scaling method ensures thatthe evaluation of service candidates is globally valid, whichis important for guiding local selection in order to avoid lo-cal optimums. The scaling process is then followed by aweighting process for representing user’s over the differentQoS attributes. We compute the utility U(sji) of the i-thservice candidate in class Sj as

U(sji) =r∑

k=1

Qmax(j, k) − qk(sji)


with wk ∈ R+

0 and∑r

k=1wk = 1 being the weight of qk to

represent user’s priorities.

5. PERFORMANCE STUDYThe aim of this evaluation is to validate our hypothesis

that our approach achieves close-to-optimal results with amuch lower computation time compared to “pure” globaloptimization approach as proposed by [15, 25, 6]. In the fol-lowing we use the label “hybrid” to refer to our solution andthe label “global” to refer to the “pure” global optimizationapproach.

5.1 Performance AnalysisThe scalability of QoS-based service composition systems

is affected by the time complexity of the applied algorithm.There are three factors that determine the size of the compo-sition problem: the number of required service classes n, thenumber of service candidates per class l, which we assumeto be equal for all classes, and the number of global QoSconstraints m. As the problem can be modeled as a Multi-Choice Multidimensional Knapsack problem (MMKP), whichis known to be NP-hard [20], the time complexity of any ex-act solution is expected to be exponential. Existing globaloptimization solutions model the service selection problemas a standard mixed integer program (MIP). The worst casetime complexity of MIP solvers using the simplex methodis an exponential function O(2n·l) [16], which restricts theapplicability of these solutions to small size compositionproblems, where the number of service candidates l is verylimited. Returning back to the given service compositionscenario in section 1, already with a few hundreds of webservice candidates that provide the same functionality (e.g.transcoding web services), the response time of this ap-proach to QoS-optimized service selection (or replacement)requests can be out of the run-time requirements.

In our Hybrid approach, we use mixed integer program-ming to solve part of the problem, namely, the decomposi-tion of the global QoS constraints into local ones. The actualselection of services, however, is done using distributed lo-cal selection strategy, which is very efficient and scalable.The local utility computation for service candidates has alinear complexity with respect to the number of service can-didates, i.e. O(l). As service brokers can perform the localselection in parallel, the total time complexity of this stepis not affected by the number of service classes, hence, thecomplexity of the second step remains O(l).

The time complexity of our approach is dominated by thetime complexity of the constraint decomposition part. Thenumber of decision variables in our MIP model is n · m · d,where n is the number of service classes, m is the numberof global QoS constraints and d is the number of quality

levels. Consequently, the time complexity of our approach isindependent on the number of available web services, whichmakes it more scalable than existing solutions that rely on“pure” global optimization. By selecting a low number ofquality levels d with 1 < d << l

mwe ensure that the size of

the MIP is much smaller compared to the MIP model usedin the global optimization approaches in [15, 25, 6].

5.2 Experimental EvaluationWe have conducted extensive simulations to evaluate the

performance of the proposed QoS-aware service selection ap-proach, which we describe in this section.

Evaluation Methodology

We have created several test cases of the QoS-based servicecomposition problem. Each test case consists of a servicecomposition request with n service classes, l service candi-dates per class and m global QoS constraints. By varyingthese numbers we created a collection of test cases, whereeach unique combination of these parameters represents onetest case. We first solved each test case using the globaloptimization approach to find the optimal selection of com-ponent services that satisfy all global QoS constraints, whilemaximizing the overall utility value. We recorded the re-quired computation time tglobal and the obtained utilityvalue uglobal by this method for each test case. We thenprovided the same test cases to our hybrid service selec-tion method and compared its computation time thybrid andthe aggregated utility value of the returned selection uhybrid

with tglobal and uglobal for the same test case respectively.In order to study the effect of the chosen number of qual-ity levels in the hybrid approach, we solved each test caseseveral times with different number of quality levels. Thenumber of quality levels in this experiment was set to 10,20, 30, 40 and 50 levels.

The Dataset

In our evaluation we experimented with two QoS datasets.The first dataset is the QWS real dataset from [2, 3, 4]. Thisdataset includes measurements of 9 QoS attributes for 2500real web services. Table 2 lists the QoS attributes in thisdataset and gives a brief description of each attribute. Thedataset was measured using commercial benchmark tools for

Table 2: QoS attributes in the QWS datasetQoS At-

tribute

Description Units Of

Measure-

ment

ResponseTime

Time taken to send a request andreceive a response

millisecond

Availability Number of successful invoca-tions/total invocations

percent

Throughput Total number of invocations for agiven period of time

invocations /second

Likelihood ofsuccess

Number of response/number of re-quest messages

percent

Reliability Ratio of the number of error mes-sages to total messages

percent

Compliance To which extent a WSDL documentfollows the WSDL spec.

percent

Best Practices To which extent a web service fol-lows the Web Services Interoper-ability (WS-I) Basic Profile

percent

Latency Time the server takes to process agiven request

millisecond

Documentation Measure of documentation (i.e. de-scription tags) in WSDL

percent


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Figure 5: Performance comparison w.r.t. the number of web service candidates

Figure 6: Performance comparison w.r.t. the number of web service classes

web services, which were located using public sources on theWeb, including UDDI registries, search engines and serviceportals. For more details about this dataset we refer thereader to [3, 4].

In order to make sure that the results of our experimentsare not biased by the used QWS dataset, we experimentedwith a second dataset. The second dataset was created byassigning arbitrary QoS values to 20000 artificial web ser-vices. The QoS values were normally distributed in therange between 1 and 100.

Experiment Settings

We used the open source (Mixed Integer Programming) Lp-Solve system lpsolve version 5.5 [17] for solving the MIPmodel in both approaches. The experiments were conductedon a HP ProLiant DL380 G3 machine with 2 Intel Xeon2.80GHz processors and 6 GB RAM. The machine is run-ning under Linux (CentOS release 5) and Java 1.6.

Performance Results

In Figure 5 we compare the performance of our hybrid ap-proach and the global optimization approach with respectto the number of service candidates. The graphs show themeasured computation times tglobal and thybrid for each testcase. The number of service candidates per class l variesfrom 50 to 500 for the QWS dataset and from 100 to 2000

services per class. In this experiment, the number of serviceclasses n is fixed to 5 for the QWS dataset and to 10 classesfor the random dataset in all test cases. The number of QoSconstraints in all these test cases was fixed to 3 constraints.

The results indicate that the hybrid approach significantlyoutperforms the global approach for both datasets. By in-creasing the number of service candidates, the required com-putation time of the hybrid approach increases very slowlycompared to the global approach, which makes our solutionmore scalable.

The results also show that increasing the number of qual-ity levels in the hybrid approach increases the computationtime. We also notice that with small number of service can-didates, increasing the number of quality levels “more thannecessary” (i.e. to 50 quality levels in this case) can leadto longer computation time than in the global approach.This is an expected behavior as we already discussed in Sec-tion 5.1. According to our analysis, the number of qual-ity levels d must be less than l/m. In the aforementionedsituation this was not the case (in the random dataset forexample, d = 50, whereas l/m = 33.3).

In the experiment shown in Figure 6 we study the per-formance of both approaches with respect to the number ofservice classes n in the composition. The number of serviceclasses varies from 5 to 25 for the QWS dataset and from10 to 100 classes for the random dataset. The number of


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Figure 7: Optimality w.r.t. the number of web service candidates

Figure 8: Optimality w.r.t. the number of web service classes

service candidates per class l in this experiment is fixed to100 for the QWS dataset and to 500 for the random dataset.The results of this experiment show that our approach stilloutperforms the global approach in all test cases.

Optimality

As our hybrid solution is an approximate solution, we haveevaluated the quality of the results obtained by our solutionby comparing it with the optimal results obtained by theglobal optimization approach. We compute the optimality ofthe results of the hybrid approach by comparing the overallutility value (uhybrid) of the selected services to the overallutility value (uglobal) of the optimal selection obtained bythe global approach, i,e.:

optimality = uhybrid/uglobal

Figure 7 shows the achieved optimality in several testcases with different number of service candidates, while Fig-ure 8 shows the achieved optimality in several test caseswith a varying number of service classes. The results indi-cate that the hybrid approach was able to achieve above 96%optimality in average. The results also show that in aver-age, increasing the chosen number of quality levels improvesthe achieved optimality. This improvement does not comewithout cost as we can see from Figures 5 and 6. There

is a trade-off between optimality and performance. Never-theless, in average, the hybrid approach is able to reach aclose-to-optimal results with very low cost.

6. CONCLUSION AND FUTURE WORKIn this paper we presented an efficient heuristic for the

QoS-based service composition, which is known to be NP-hard. We combine global optimization with local selectionmethods to benefit from the advantaged of both worlds. Ourproposed method allows to dramatically reduce the (worstcase) efforts compared to existing solutions. Our evaluationsshow a significant improvement in terms of computationaltime, while achieving close to optimal results. This is es-pecially useful for applications with dynamic changes andreal-time requirements. In the current approach the numberof service levels is fixed and need to be defined beforehand.Currently we are studying the impact of the applied methodas well as the number of the selected quality levels on theperformance and quality of the obtained results. We alsoaim at developing a self-adaptive approach, which optimizesitself by determining the best number of quality levels atrun-time based on the available QoS information. A proto-col for coordinating the distributed service brokers, whichare involved in a QoS optimization process, is also part ofour future work.


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