Interoperability Constraints in Service Selection Algorithms

Paweł L. Kaczmarek

Faculty of Electronics, Telecommunications and Informatics, Gda´nsk University of Technology,

Narutowicza 11/12 Str., 80-233 Gda´nsk, Poland

Keywords:

Service Oriented Architecture, Interoperability, Service Integration, Business Process Management, Quality

of Service.

Abstract:

In Service Oriented Architecture, composite applications are developed by integration of existing, atomic

services that may be available in alternative versions realizing the same functionality but having different

Quality of Service (QoS) attributes. The development process requires effective service selection algorithms

that balance proﬁts and constraints of QoS attributes. Additionally, services operate in a heterogeneous en-

vironment, which requires resolution of interoperability issues during integration. In this paper, the author

proposes a methodology that introduces interoperability analysis into existing service selection algorithms.

Algorithm data structures are extended with additional constraints that represent interoperability for the two

considered computational models: the graph-based model and the combinatorial model based on integer linear

programming. The extensions enable a straightforward application of a wide range of existing algorithms as

the general structure of input data is preserved. As a part of the research, a system that supports development

of SOA-based applications was implemented. Chosen service selection algorithms together with appropriate

extensions for interoperability analysis were implemented in the system.

1 INTRODUCTION

Service Oriented Architecture (SOA) allows us to re-

duce development cost and time by extensive reuse

of existing service components and competition be-

tween service suppliers. Alternative services realizing

the same functionality differ in non-functional, Qual-

ity of Service (QoS) attributes such as performance,

reliability and price. Typically, application develop-

ment intends to optimize the QoS, which means max-

imization of desired attributes, such as performance,

while simultaneously meeting constraints imposed on

the ﬁnal application, such as maximum price or min-

imum reliability (Yu et al., 2007) (Cao et al., 2007a).

It is required that services with optimal QoS values

are selected, while constraints are satisﬁed.

Existing methods and algorithms of service se-

lection, however, focus on the formal model of the

complex service refraining from interoperability is-

sues that may affect the integration process as ser-

vices are deployed in heterogeneous runtime en-

vironments. Technical interoperability limits the

choice of alternative services despite theoretical con-

formance of functionality and correctness of QoS

analysis. Although services typically use the Web

services standards (WS*) to exchange information,

there are many threats for effective integration. The

runtime platforms that host services support only

selected standards and versions from the wide set

of WS* standards. Even if runtime platforms are

compatible in supported standards, interoperability

is not guaranteed because of standard inconsisten-

cies and vendor-speciﬁc extensions (Fisher et al.,

2006) (Egyedi, 2007). A service selection may prove

to be technically infeasible if services prove to be

non-interoperable. Service selection methods addi-

tionally require mechanisms to represent and guaran-

tee interoperability in the ﬁnal application.

Considering the problem, the author proposes a

methodology that includes interoperability analysis in

service selection algorithms. The data structures of

service selection algorithms are extended with ded-

icated constraints that represent interoperability be-

tween services. The constraints result from interoper-

ability rates that consider runtime environments that

host the services and WS* standards that are used

by the services. In the methodology, two computa-

tional representations are analyzed: the combinato-

rial model together with algorithms based on the inte-

ger linear programming (ILP) problem and the graph-

based model together with algorithms based on single

source shortest path selection. The proposed exten-

L. Kaczmarek P..

Interoperability Constraints in Service Selection Algorithms.

DOI: 10.5220/0003980500230032

In Proceedings of the 7th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE-2012), pages 23-32

ISBN: 978-989-8565-13-6

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

sions enable a straightforward application of a wide

range of existing algorithms as the extensions pre-

serve the general structure of data models.

The proposed solution was implemented in a web-

based system that supports service selection in de-

velopment of workﬂow applications as a representa-

tive case of the composite service concept. The sys-

tem maintains a knowledge base of runtime environ-

ments interoperability, which includes a catalog of

WS* standards, versions and conﬁguration options.

Chosen service selection algorithms together with ap-

propriate extensions for interoperability analysis were

implemented in the system.

The rest of the paper is organized as follows. The

next section presents and analyses backgroundknowl-

edge used in the methodology. Sect. 3 describes in-

teroperability notation adjusted for service selection

purposes. Sect. 4 presents extensions for selection al-

gorithms in the graph-based and combinatorial mod-

els. System implementation is presented in Sect. 5.

Finally, Sect. 6 presents related work and Sect. 7 con-

cludes the paper.

2 SYSTEM MODEL AND

BACKGROUND

Service selection during development of composite

services is a mature research discipline that has al-

ready developed models and solutions for various ap-

plication areas. In this research, existing models are

extended with issues speciﬁc for interoperabilityanal-

ysis, considering that interoperability may limit the

choice in concrete situations.

The formal model of a composite service may be

applied in concrete application areas such as: devel-

opment of WS*-based services using the SOA ap-

proach, development of cloud-based applications and

business process modeling (BPM) and execution us-

ing workﬂow management systems.

2.1 Composite Service Model

Typically, the model of a composite service (CS) takes

the following assumptions (Yu et al., 2007) (Alrifai

et al., 2009) (Cao et al., 2007a):

• A composite service consists of N atomic op-

erations composed using composition structures

(e.g.: loops, conditions, forks, joins). Each oper-

ation is realized by a service from a service class

, ..., S

), a service class is also called an ab-

stract service.

• For each service class S

, there exist one or more

atomic services (s

) that realize the same func-

tionality, but differ in non-functional attributes.

Each service s

is associated with a QoS vector

= [q

, ..., q

] of non-functionalattributes, e.g.:

service price, performance and security.

• There is a deﬁned utility function (F) that is

the evaluation criteria of service selection on its

QoS attributes. The utility function depends on

user preferences considering the non-functional

attributes. The user intends to minimize attributes

such as price and maximize performance and reli-

ability with given weights for each attribute.

The QoS value of the ﬁnal CS depends on ser-

vice selection and the structure of the CS. The cal-

culation considers probability of execution in multi-

ple execution paths, loop unfolding and others. Con-

sidering that QoS values are expressed in different

metric types, a form of normalization is performed

to calculate the utility function F. Attributes receive

positive and negative weights depending on whether

they are desired or undesired as QoS of the service as

described in detail in (Zeng et al., 2003) (Yu et al.,

2007).

Additionally, it is required that the overall QoS

value of the composite service does not exceed a spec-

iﬁed limit (e.g. a price constraint), which is known as

the QoS constraint and deﬁned as:

= [Q

, Q

, ..., Q

] (1)

Service selection process intends to select services

such that

1. The utility function F of the composite service is

maximized

2. Q

constraints are satisﬁed for the composite ser-

vice (Q

≤ Q

, ∀

∈Q

)

2.2 Existing Approaches to Service

Selection

The service selection problem with QoS constraints

has exponential computational complexity in the gen-

eral case (Alrifai et al., 2009) (Bradley et al., 1977).

Different solutions have been proposed that either

narrow service structure to make it feasible for opti-

mal calculations or apply a heuristic algorithm. Exist-

ing solutions, however, do not integrate the concept of

interoperability analysis with service selection based

on QoS attributes.

This work intends to introduce interoperability

constraints to existing algorithms without causing the

necessity of major changes in algorithm structures. In

order to cover a possibly wide range of algorithms,

the following features are considered:

ENASE2012-7thInternationalConferenceonEvaluationofNovelSoftwareApproachestoSoftwareEngineering

Table 1: Selected selection algorithms and their characteristics.

Algorithm Computational Optimization

model scope and accuracy

BBLP, WS IP (Yu et al., 2007) Combinatorial Global optimal

MCSP (Yu et al., 2007) Graph Global optimal

RWSCS KP (Cao et al., 2007a) Combinatorial Global heuristic

WS HEU, WFlow (Yu et al., 2007) Combinatorial Global heuristic

MCSP-K (Yu et al., 2007) Graph Global heuristic

SEWSCP (Hong and Hu, 2009) Combinatorial Local optimal

ACO4WS (Wang et al., 2001) Graph Global heuristic (AI)

PSO (Xia et al., 2009) Graph Global heuristic (AI)

DaC (Yu et al., 2006) Combinatorial Local optimal

LOSS/GAIN (Sakellariou et al., 2007) Graph Global heuristic

GA (Cao et al., 2007b) Graph Global heuristic (AI)

FastHeu (Czarnul, 2010) Graph Local heuristic (DYN)

DIST HEU (Alrifai et al., 2009) Combinatorial Local optimal

QCDSS(Chun-hua et al., 2009) Graph Global heuristic (AI)

• Computational Model. Two well-known mod-

els are used in service selection: the combina-

torial model - 0-1 multidimensional multichoice

knapsack problem (MMKP), e.g.: (Hong and Hu,

2009) (Cao et al., 2007a), and the graph model

- multiconstraint optimal path selection (MCOP),

e.g.: (Cao et al., 2007b) (Wang et al., 2001). The

work (Yu et al., 2007) analyzes both approaches.

• Accuracy. Optimal algorithms are applicable for

small services, e.g. (Yu et al., 2007) proposes an

algorithm based on the branch-and-bound linear

programming. For large services, heuristic al-

gorithms are proposed, such as the shortest-path

based MCSP-K algorithm (Yu et al., 2007) or the

combinatorial-based RWSCS

RP algorithm (Cao

et al., 2007a). Artiﬁcial intelligence (AI) heuristic

methods are also used, e.g. the ant-colony based

ACO4WS algorithm (Wang et al., 2001) and par-

ticle swarm optimization (Xia et al., 2009) (Chun-

hua et al., 2009). Some solutions propose dy-

namic (DYN) optimization (Czarnul, 2010).

• Optimization Scope. The reduction of optimiza-

tion scope is another method of solving the prob-

lem. In this approach, the composite service

is divided into subservices that are analyzed lo-

cally, typically using an optimal algorithm, e.g.:

SEWSCP (Hong and Hu, 2009), DIST

HEU (Al-

rifai et al., 2009) and DaC (Yu et al., 2006).

Table 1 shows selected algorithms and their char-

acteristics.

In the MMKP/combinatorial model (Cao et al.,

2007a) (Yu et al., 2007) (Martello and Toth, 1987),

service classes are mapped to item groups, while

atomic services are mapped to items in the groups.

Each atomic service (s

) requires resources q

and

supplies proﬁt F

. The Q

constraints are treated as

available resource in the knapsack. The problem is

formulated formally as:

Max

∑

i=1

∑

j∈S

subject to

∑

i=1

∑

j∈S

∗ x

≤ Q

(a = 1, ...m)

∑

j∈S

= 1

∈ {0, 1} i = 1, ..., N, j ∈ S

(2)

where x

= 1 if service s

is selected for the so-

lution and x

= 0 otherwise. The problem may be

solved using integer linear programming (ILP) meth-

ods (Bradley et al., 1977).

In the graph-based model (Yu et al., 2007), every

atomic service is a node in the graph and potential

transitions between services are considered as links.

If a service class S

sends data to a service class S

then there is a link between every service from S

every service from S

. The node that represents a ser-

vice s is assigned with QoS attributes of the service

and consequently with service utility function F. QoS

attributes of a service are added to every link incom-

ing to the node that represents the service. The multi-

constraint optimal path problem intends to ﬁnd a path

such that the utility function F is maximized while

meeting the Q

constraints.

If QoS constraints are removed from the selection

problem, a simpliﬁed model is created, in which ser-

vices are selected considering solely the utility func-

tion F. In this case, local optimization for each ser-

vice class S

may be used to select the service that

supplies the highest utility value. Despite the sim-

InteroperabilityConstraintsinServiceSelectionAlgorithms

pliﬁcation, interoperability needs to be considered in

this case too, as the communication model of the ﬁnal

composite service remains unchanged.

2.3 Interoperability in Service

Integration

Service integration in SOA requires resolution of

interoperability issues. Web services standards

(WS*) (Booth et al., 2004) were proposed and ac-

cepted as the main solution for achieving the task.

Currently, there exist approximately ﬁfty standards

in different versions apart from the basic SOAP and

WSDL. Standards constitute the WS* stack (WS-I,

2004), in which standards for an extended functional-

ity rely on lower level ones. Main functional groups

include the areas of: addressing (WS-Addressing),

security (e.g. WS-Security), reliable messaging (e.g

WS-Reliablility), transactions (e.g WS-Coordination,

WS-AtomicTransactions) and others. Business pro-

cess modeling and execution standards (e.g. Business

Process Execution Language (Oasis, 2007)) lever-

age existing WS* standards assuming that lower

layer standards may be used for communication with

atomic services. Despite the general success of WS*,

effective service integration using WS* is still a chal-

lenging task (Egyedi, 2007) (Fisher et al., 2006).

General purpose interoperability metrics have

been proposed as universal means of interoperability

rating. Typically, the metrics specify interoperabil-

ity levels that are associated with a required scope

of data exchange (Ford et al., 2007). For example,

Layers of Coalition Interoperability (LCI) deﬁne nine

layers: Physical, Protocol, Data/Object Model, Infor-

mation, Knowledge/Awareness, Aligned Procedures,

Aligned Operations, Harmonized/Strategy Doctrines,

and Political Objectives. The LISI (Levels of Infor-

mation Systems Interoperability) metric deﬁnes ﬁve

levels: Isolated (manual), Connected (peer-to-peer),

Functional (distributed), Domain (integrated), Enter-

prise (universal). Sect. 6 presents more detailed dis-

cussion concerning existing solutions and methods.

3 INTEROPERABILITY

NOTATION FOR SERVICE

SELECTION

In our work, interoperability is analyzed on the

communication protocol level, which corresponds to

Level 1 (Connected) in the LISI metric or Level 2

(Data/Object) in the LCI metric (Ford et al., 2007).

We assume that if services are interoperable on that

level, it is possible to exchange data between them in

the context of CS construction.

For the purpose of this work, we make the follow-

ing assumptions:

• There exist different runtime environments (de-

noted R = {r

, ..., r

} that may host services.

Each service s is hosted in a runtime environment

r(s) ∈ R that is unambiguously determined by the

atomic service s.

• Each runtime environment supports a known sub-

set of existing WS* standards and their versions.

• Each service s requires some WS* standards (in

speciﬁed versions) to operate correctly. The stan-

dards are supported by r(s).

The number of runtime environments is limited,

considering that they are application servers supplied

by leading industrial companies or communities. Ex-

amples of runtime environments include: IBM Web-

Sphere AS, Microsoft IIS, Apache Tomcat/Axis and

Oracle Glassﬁsh.

If a service s communicates remotely with a ser-

vice s

′

than it is required that s

′

can interoperate with

s. We rate interoperability binary as either interop-

erable (if data can be transfered correctly) or non-

interoperable (if data can not be transfered). Assum-

ing that s sends data to s

′

, let q

comm

(s, s

′

) denote inter-

operability modeled as an attribute of communication

cost between s and s

′

, deﬁned as follows:

comm

(s, s

′

) = 0 if s, s

′

can interoperate

comm

(s, s

′

) = 1 if s, s

′

can not interoperate

(3)

In this notation, interoperability cost 0 denotes

that services are interoperable while interoperability

cost 1 denotes that they are not interoperable.

The q

comm

value is given a priori and depends on

conditions encountered in the real environments that

host the services. Information about service interop-

erability is taken from two complementary sources:

• Experimental veriﬁcation. A test connection is es-

tablished between s and r(s

′

) to check if data can

be transfered correctly within required standards.

• Theoretical calculation. The calculation compares

standards required by s and supported by r(s

′

). r

′

must support standards required by s if s and s

′

are

to be interoperable. The method is easier to ap-

ply but less reliable than the ﬁrst one, as declared

standard compliance does not inferr compatibil-

ity (Egyedi, 2007).

The ﬂow of data between services depends on

the applied communication model. Two well-known

models are considered (Tanenbaum and van Steen,

2002) as shown in Fig. 1:

ENASE2012-7thInternationalConferenceonEvaluationofNovelSoftwareApproachestoSoftwareEngineering

Figure 1: Differences between message passing model (a) and remote method invocation model (b).

• remote method invocation model (RMI)

• message-passing model (MPM)

In the RMI model, there is a central runtime envi-

ronment that hosts the composite service, denoted

(r(CS)), and the runtime manages data exchange dur-

ing invocation of remote atomic services. The model

is typically used in the BPM technology by Business

Process Execution Language (BPEL) (Oasis, 2007)

and Windows Workﬂow Foundation (the XPDL for-

mat). In the message-passing model, services send

data directly from one to another without a central

coordination point. The Business Process Modeling

Notation standard (OMG, 2009) uses this model to

describe the logic of a business process.

4 ALGORITHM EXTENSIONS

The proposed extensions of existing models with in-

teroperability constraints enable a possibly straight-

forward application of existing selection algorithms.

We consider both RMI and MPM, as well as graph-

based and combinatorial-based algorithms.

4.1 Extensions for Remote Method

Invocation Model

In the RMI model, we assume that CS may be hosted

on one of alternative runtime environments. Let R

, ..., r

} denote the set of alternativeenvironments

for CS. Each service runtime environment r(s) must

interoperate with the chosen runtime environment for

the CS. The presented algorithm is applicable if a

workﬂow is deﬁned using the BPEL or XPDL stan-

dards.

Let q

comm,cs

(s, cs, r

) be deﬁned analogously to

comm

that is

comm,cs

(s, cs, r

) = 0 if s can interoperate with

cs hosted on r

comm,cs

(s, cs, r

) = 1 if s can not interoperate with

cs hosted or r

(4)

Let Q

comm,cs

be the set of interoperability informa-

tion of q

comm,cs

, cs, r

) for each s

i = 1..N, j ∈ S

∈ R

The processing is done as shown in Algorithm 1.

The algorithm analyzes in a loop each alternative run-

time environment of CS. For each r

, it performs a

pre-ﬁltering of services that are interoperable with the

. Then, one optimal solution is calculated for each

using an algorithm (SEL

ALG) chosen from exist-

ing selection algorithms. Both combinatorial-based

and graph-based selection algorithms are applicable

in this invocation model. After processing of all r

the optimal solution for the whole set R

is selected

Interoperability constraints introduce a relatively

low computational complexity in the RMI model.

Selection of interoperable services depends on

O(sizeof(S) ∗ sizeof(R

)). In practice, the size of

= T may be considered as constant because it

does not depend on CS size and there is a lim-

ited number of industrially-runningenvironments that

may host services. Generation of tmpSelection takes

T ∗ O(SEL

ALG), which does not exceed the com-

putational complexity of SEL

ALG. The ﬁnal step

(selection of the best solution from R

) takes less

than T ∗ timeof (F), which is certainly lower that

SEL

ALG, as the algorithms invokeF for each service

and for the whole workﬂow. Therefore, O(SEL

ALG)

is the factor that determines the ﬁnal computational

complexity of Algorithm 1.

InteroperabilityConstraintsinServiceSelectionAlgorithms

Algorithm 1: Service selection using an existing selection

algorithm and interoperability constraints in the RMI com-

munication model.

input: R

, S

, ...S

input: Q

comm,cs

, F - utility function

input: SEL ALG - an existing selection algorithm

output: [s

, ..., s

], a

∈ S

//optimal selection

tmpSelection = ∅ //list of optimal selections for

each r

∈ R

for all (r

∈ R

) do

for all (S

, i = 1, ..., N) do

= select all s

from S

such that

comm,cs

, r

) = 0

if (S

== ∅) then

//there are no services in S

that can

//interoperate with r

, no feasible

//solution for runtime environment r

continue with next r

end if

end for

tmpSelection+ = SEL

ALG ((S

, i = 1..N))

end for

maxTmpUtility = 0

for all (sel ∈ tmpSelection) do

tmpUtility = F(sel)

if (tmpUtility > maxTmpUtility) then

maxTmpUtility = tmpUtility

result = sel

end if

end for

return result

4.2 Extensions for Message Passing,

Combinatorial Model

In this model, if a service s communicates with a ser-

vice s

′

, then r(s) must interoperate with r(s

′

). The

model is applicable for workﬂows deﬁned using the

BPMN standard. As stated in Sect. 3 q

comm

, s

)

denotes interoperability between s

, s

∈ S

, s

∈

, i, k = 1, ..., N, j ∈ S

, l ∈ S

. We extend the ex-

isting combinatorial model and its corresponding in-

teger linear programming representation with addi-

tional constraint equations that represent interoper-

ability:

∑

j∈S

∑

l∈S

comm

∗ (x

∗ x

) = 0 (5)

for all pairs S

, S

such that S

sends data to S

the structure of CS. It is required that if two services

are selected (x

= 1 and x

= 1), then their interoper-

ability communication cost q

comm

= 0. If two service

classes do not pass data, the q

comm

value is insigniﬁ-

cant and may be ignored.

The number of added equations is equivalent to

the number of edges in the composite service graph,

which depends linearly on the number of services (N).

The maximum number of elements in each equation is

Max(sizeof(S

)) ∗ Max(sizeof(S

)) for i = 1...N. We

assume that the maximum number of alternative ser-

vices for one service class does not exceed a constant

(MaxS

Equation 5, however, introduces non-linearity, so

the problem is changed from ILP to mixed integer

nonlinear programming (MINLP), which makes it

signiﬁcantly more difﬁcult to solve. We transform

the equation into a separable programming problem,

which is a special class of MINLP that is relatively

easily solvable by existing methods (Bradley et al.,

1977). In order to achieve the separable programming

form, we perform the following transformation:

∗ x

= y

1,ijkl

− y

2,ijkl

where y

1,ijkl

= 0.5(x

+ x

)

2,ijkl

= 0.5(x

− x

)

(6)

for each j ∈ S

, l ∈ S

. After the transformation,

additional y variables are added to problem formu-

lation. The number of y variables equals the total

number of elements in Equation 5 and has the com-

plexity of O(N ∗ MaxS

∗ MaxS

). The newly created

model is more complex than the original one, but it

remains solvable by existing methods such as branch-

and-bound or implicit enumeration (Bradley et al.,

1977). The model is applicable for service selection

methods that leverage (N)LP to ﬁnd an optimal solu-

tion, that is for methods based on local optimization

(DaC (Yu et al., 2006)) or applied to small composite

services.

4.3 Extensions for Message Passing,

Graph-based Model

In this model, we extend the composite service graph

with additional information regarding interoperabil-

ity constraints. Analogously to the combinatorial ap-

proach, the model is applicable for workﬂows deﬁned

using the BPMN standard. We use the previously

deﬁned q

comm

attribute to represent interoperability

communication cost. The processing is done as fol-

lows:

• Assuming that a service class S

communicates

with a service class S

, the q

comm

, s

) attribute

is assigned to each link from a service s

∈ S

a service s

∈ S

. The attribute has the value 0

if services are interoperable and the value 1 in the

other case.

ENASE2012-7thInternationalConferenceonEvaluationofNovelSoftwareApproachestoSoftwareEngineering

Figure 2: A screenshot of the WorkﬂowIntegrator system with summary results of an exemplary service selection.

• The Q

vector is extended with an additional el-

ement Q

comm

that represents interoperability, that

is:

′

= [Q

, Q

, ..., Q

, Q

comm

]

comm

= 0

(7)

where Q

comm

for a service selection (a path in the

graph) is calculated as follows:

comm

(path) =

∑

link∈path

comm

link

(8)

that is: every edge on the selected path must have

the communication cost 0.

The proposed model does not change the graph

structure and the structure of Q

constraints. There-

fore, it is possible to apply both optimal and heuris-

tic existing algorithms that are based on graph

structure processing, such as MSCP, MSCP

K or

ACO4WS (Yu et al., 2007) (Wang et al., 2001).

In this model, interoperability analysis requires

much lower computational cost as compared with the

combinatorial approach. One additional attribute is

added to the QoS vector on the service level and on the

constraints level. This requires additional computa-

tions during graph preparation and during service se-

lection, but does not change the computational com-

plexity of algorithms. Therefore, the graph-based al-

gorithms are more adequate for interoperability anal-

ysis in the message passing model.

5 SYSTEM IMPLEMENTATION

As a part of the research, we implemented the Work-

ﬂowIntegrator system that enables selection of ser-

vices during composite service development. The aim

of the system is to support developers in the selec-

tion process depending on given requirements on util-

ity function, QoS attributes, and interoperability con-

straints. The system is available online at:

http://kask.eti.pg.gda.pl/WorkﬂowFTI

Fig. 2 shows a screenshot of the WorkﬂowIntegra-

tor system with summary results of an exemplary ser-

vice selection using cost and availability as selection

attributes.

We use workﬂow applications as a concrete im-

plementation of the composite service concept, which

enables us to leverage existing solutions in service de-

scription and invocation.

In the workﬂow methodology, a workﬂow appli-

cation corresponds to a complex services, while a

service available through Web services corresponds

to an atomic service. Concrete runtime environ-

ments are Workﬂow Management Systems (WfMS)

for the workﬂow application, and Application Servers

for atomic services. Three main standards are used

for service description: Business Process Execution

Language (BPEL), Business Process Modeling Nota-

tion (BPMN) and the Windows Workﬂow Foundation

(XPDL format).

The system supplies the ASInteroperability mod-

ule that stores general purpose interoperability rat-

ings of runtime environments (Kaczmarek and

Nowakowski, 2011) available also at:

InteroperabilityConstraintsinServiceSelectionAlgorithms

http://kask.eti.pg.gda.pl/ASInteroperability

The ASInteroperability module contains a sys-

tematic description of existing WS* standards, their

versions, conﬁguration options and acceptable values

for the options. For example WS-AtomicTransaction

anticipate the Commit Protocol option with values:

Completion, Two-Phase Commit (Volataile), Two-

Phase Commit (Durable). Popular existing runtime

environments have been described in the system in-

cluding: name, vendor, version and used libraries.

The runtime description model enables dynamic asso-

ciation between runtime environments and dependant

libraries.

Interoperability rates are given to concrete conﬁg-

urations in which integration is attempted. Each con-

ﬁguration covers speciﬁcation of: the two integrated

runtime environments, used WS* standards, standard

versions, signiﬁcant conﬁguration options and values

settled for the options. Rates are given by develop-

ers who have already attempted to establish a con-

crete integration and share the knowledge with the

community. The developer that gives a rate speciﬁes

the conﬁguration, the interoperability rate and the re-

quired development complexity to establish the inte-

gration. The complexity ranges from simple GUI op-

erations, through modiﬁcation of conﬁguration ﬁles,

to changes of undocumented features, and indicates

what is the required expertese level to establish the

integration.

Additionally, a service registry is deﬁned that con-

tains speciﬁcation of services together with their lo-

cation, QoS attributes, the runtime environment that

hosts the service, and used WS* standards. Addition-

ally, it is possible to specify groups of alternative ser-

vices, which is the main function of the registry in

the context of service selection during service compo-

sition. Groups of alternative services are conﬁgured

manually by developers of the composite service.

Using ASInteroperability and Service Registry,

the module of Workﬂow Fault Tolerant Integrator

(WorkﬂowFTI) supports design of optimal workﬂow

applications depending on the following information

acquired from the developer:

1. The developer deﬁnes groups of alternative ser-

vices that correspond to service classes in the CS

model. Each service contains description of QoS

attributes, used WS* standards and the application

server that hosts the service.

2. The developer speciﬁes the utility function that

should be optimized in the form of weights for

QoS attributes and the method for attribute aggre-

gation (average, sum, max, min and others).

3. The developer speciﬁes which selection algorithm

should be used.

4. The user supplies a deﬁned workﬂow application

together with initially assigned services for tasks.

The assignment aims at specifying which service

group should be used for processing each task.

After the conﬁguration, WorkﬂowFTI uses the

chosen algorithm to select appropriate services and

returns the result. During the selection, it considers

the relevant information such as: interoperability con-

straints, QoS attributes, utility function and others.

The system contains prototypical implementa-

tions of representative existing algorithms extending

them with relevant interoperability processing. Both

RMI and message passing models are analyzed during

the work. The BPEL standard was used as a represen-

tation of the RMI model, while the BPMN standard

(using the XPDL format) was used as a representa-

tion of the message passing model. In the message

passing model, interoperability between runtime en-

vironments of individual services is analyzed during

the selection process. The proposed methodology was

implemented using both optimal and approximate ex-

isting algorithms. Optimal algorithms include: ex-

plicit enumeration based on ILP and a simpliﬁed ver-

sion of the MSCP algorithm. Additionally, we im-

plemented a greedy algorithm as an example of the

approximate approach. In the RMI model, services

are pre-ﬁltered considering their interoperability with

each WfMS, and services supplying the best QoS are

selected. We veriﬁed interoperability analysis using

an optimal algorithm based on explicit enumeration,

and an approximate algorithm based on local opti-

mization.

The system is implemented in the .NET 3.5 en-

vironment and hosted on Microsoft Internet Infor-

mation Service. We used the MSSQL database for

storing data regarding interoperability description and

service registry. A web interface is supplied for users

intending to design a workﬂow application or verify

interoperability between application servers. Inter-

nally, the system uses Web services to integrate AS-

Interoperability, Service Registry and WorkﬂowFTI.

6 RELATED WORK

Sect. 2 presented core concepts regarding the problem

addressed in this paper. Apart from the background,

various detailed solutions have been proposed. (Fang

et al., 2004) presents an interoperability assessment

model for service composition using ﬁve interoper-

ability levels: signature, protocol, semantic, quality

and context. The assessment is based on a formal

evaluation metric that assigns a rate in the range [0, 1]

for each level depending on deﬁned conditions. The

ENASE2012-7thInternationalConferenceonEvaluationofNovelSoftwareApproachestoSoftwareEngineering

work (Bhuta and Boehm, 2007) addresses a similar

issue assuming that components-off-the-shelf are in-

tegrated. The work speciﬁes groups of attributes that

are used for interoperability analysis, for example:

control input technique, data communication proto-

cols, encapsulation of behavior and others. Using the

model, authors of that paper propose an interoperabil-

ity assessment tool. Both solutions do not analyze

QoS attributes and constraints, such as price, perfor-

mance, and reliability in service composition.

The Web Services Interoperability (WS-I) Orga-

nization (WS-I, 2004) has been established to reﬁne

existing standards and promote integration of hetero-

geneous environments. The organization issues WS-

Proﬁles that impose additional constraints on commu-

nication formats, which enables higher interoperabil-

ity. (Lampathaki et al., 2009) overviews XML-based

data transfer standards addressed for B2B systems.

The formats concern different layers of application

development as compared to Web services, focusing

on integration of business-oriented data rather than

protocol compatibility. (van der Aalst et al., 2003)

surveys standards and solutions related to business

process modeling and execution. The work discusses

correlation between standards related to business pro-

cess modeling and formal methods of representation.

(Czarnul, 2010) presents the BeesyCluster execution

environment that enables deﬁnition and execution of

business processes with service replacement and dy-

namic optimization.

The concept of Functional Quality of Service

(FQoS) is introduced in (Jeong et al., 2009). FQoS

describes functional attributes of Web services us-

ing mathematical methods to manipulate and quan-

tify those attributes. Authors analyze FQoS on three

levels: term similarity, tree similarity and text simi-

larity. (Tan et al., 2009) proposes to use Petri nets for

compatibility analysis during composition of BPEL

applications. Authors present a detailed association

between Petri net concepts and BPEL control con-

structs and deﬁne a formal procedure of Web ser-

vices composition. The papers focus mainly on ser-

vice compatibility analysis rather than selection based

on non-functional QoS attributes.

(Ullberg et al., 2008) and (Tsalgatidou et al.,

2008) present frameworks that enable interoperabil-

ity rating and analysis in Web services-based systems.

Both papers propose service metamodels that cover

core service description concepts applicable on the

enterprise or on the P2P level. Using the metamodels,

interoperability of services is rated in the considered

scope. (Fu et al., 2005) is another work that rates

interoperability in Web services-based solutions. The

work proposes a formal description model that covers

peers and operations, such as send, receive and tran-

sition. Results achieved using the proposed methods

may be beneﬁcial in our work as an additional source

of information about service interoperability.

7 CONCLUSIONS

The presented methodology enables application of

existing service selection algorithms in cases where

interoperability issues must be considered. The re-

mote method invocation model suits best to the pro-

posed solution allowing us to use any of the exist-

ing algorithms with minor development and compu-

tational effort. The message passing model favors

graph-based selection algorithms (both optimal and

heuristic) allowing us to preserve the general struc-

ture of algorithm data model. The application of

the methodology in the message passing model for

combinatorial algorithms faces difﬁculties because of

non-linearity of constraint equations. The problem is

solvable by existing methods, but requires additional

computational effort. As a part of the research, im-

plementation work was performed for a representa-

tive set of composite service models and algorithm

approaches, which veriﬁed the presented approach.

Extension of heuristic algorithms based on the

combinatorial model is an interesting area of future

work, considering that the algorithms may use propri-

etary data processing that does not rely directly on the

deﬁned ILP representation of the problem. Applica-

tion of the methodology in industrial projects is an-

other area of future work. The current work covered

implementation of the methodology in representative

algorithms, but the use of it in industrial projects

may give further guidelines for extensions and adjust-

ments. The projects may further supply interesting

statistical data, such as the ratio of interoperable ser-

vices, market share of runtime environments, and dif-

ferences in QoS attributes of services, which will be

beneﬁcial in further improvements of service selec-

tion methods.

ACKNOWLEDGEMENTS

The work was supported in part by the Polish Min-

istry of Science and Higher Education under research

projects N N519 172337. The author would like to

thank students of the Faculty of ETI for implementa-

tion and testing work regarding the system for service

selection.

InteroperabilityConstraintsinServiceSelectionAlgorithms

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