STRATEGIES FOR SERVICE COMPOSITION IN P2P NETWORKS

Jan Gerke

Swiss Federal Institute of Technology ETH Zurich, Computer Engineering and Networks Lab TIK

Gloriastrasse 35, CH-8092 Zurich, Switzerland

Peter Reichl

Telecommunications Research Center FTW Vienna

Donau City Strasse 1, A-1220 Vienna, Austria

Burkhard Stiller

University of Zurich, Department of Informatics IFI and ETH Zrich, TIK

Winterthurerstrasse 190, CH-8057 Zurich, Switzerland

Keywords:

Service Composition, Peer-to-Peer Network, Service-oriented Architecture.

Abstract:

Recently, the advance of service-oriented architectures and peer-to-peer networks has lead to the creation of

service-oriented peer-to-peer networks, which enable a distributed and decentralized services market. Apart

from the usage of single services, this market supports the merging of services into new services, a process

called service composition. However, it is argued that for the time being this process can only be carried out

by specialized peers, called service composers. This paper describes the new market created by these service

composers, and models mathematically building blocks required for such a service composition. A general

algorithm for service composition developed can be used independently of solutions for semantic difﬁculties

and interface adaption problems of service composition. In a scenario for buying a distributed computing

service, simulated strategies are evaluated according to their scalability and the market welfare they create.

1 INTRODUCTION

In recent years the development of distributed systems

especially the Internet has been inﬂuenced heavily

by two paradigms: Service-orientation and peer-to-

peer (P2P) (A. Oram (Editor), 2001). The beneﬁt

of service-oriented architectures (SOA) is their sup-

port of loose coupling of software components, i.e.,

providing a high degree of interoperability and reuse

(He, 2003) by standardizing a small set of ubiquitous

and self-descriptive interfaces, e.g., the standardiza-

tion of web services (World Wide Web Consortium,

2004) by the world wide web consortium, and its im-

plementations like .NET (Microsoft Developer Net-

work, 2004). Additionally, P2P ﬁle sharing systems

like BitTorrent have proven to be scalable content

distribution networks (Izal et al., 2004). This scal-

ability is achieved by the distributed nature of P2P

networks. Rather than using centralized server in-

frastructure, they rely on distributed hosts, the peers,

and their self-organization abilities. Lately, P2P net-

works have also been put to commercial use. For

instance, Blizzard uses the BitTorrent technology to

distribute program updates to their customer base of

several hundred thousands, thus reducing the need for

a centralized server infrastructure (Blizzard Corpora-

tion, 2005).

In order to combine the beneﬁts of the two par-

adigms and to achieve a decentralized platform for

loosely coupled software components, an architecture

of a service-oriented middleware was proposed by

(Gerke et al., 2003). In order to implement this archi-

tecture, (Gerke and Stiller, 2005) presented a JXTA-

based middleware. This middleware enables a fully

distributed service market, in which peers provide ser-

vices to other peers. This middleware supports this

market by providing mechanisms to search within the

underlying distributed P2P network for services, to

negotiate the terms of service usage, and ﬁnally to

charge for service usage. The market itself offers low

entrance hurdles, as any Internet host can enter the

market by running this middleware implementation.

In addition to the usage of a single service by a

single peer, the middleware-enabled service market

makes it possible to combine services into new value-

added services. This process, called service compo-

sition, allows for the maximum beneﬁt from service

reusability when creating new services. Thus, new

Gerke J., Reichl P. and Stiller B. (2005).

STRATEGIES FOR SERVICE COMPOSITION IN P2P NETWORKS.

In Proceedings of the Second International Conference on e-Business and Telecommunication Networks, pages 16-24

DOI: 10.5220/0001416100160024

 SciTePress

services can be deployed much quicker and the expert

knowledge of service developers can be used in new

services.

Service composition consists of two main sub prob-

lems: (a) understanding what functionality is pro-

vided by services and (b) understanding how services

communicate through their interfaces. The ﬁrst prob-

lem has been tackled through the development of se-

mantic service descriptions (Hendler et al., 2002),

as well as through the application of artiﬁcial intel-

ligence (Carman et al., 2003). The second prob-

lem is addressed mainly through interface description

languages like WSDL (Web Services Descriptions

Working Group, 2005). Other interface standards like

CORBA do not offer the self-description capabilities

of web services, thus reducing the reusability of soft-

ware components.

The highest beneﬁt from service composition

would be achieved, if it could be carried out by any

peer in a completely automated manner, at high speed,

and costs approaching zero. However, both sub prob-

lems described above have not yet been completely

solved. Therefore, for the time being, service com-

position can only be carried out by specialized peers,

called service composers at a certain cost.

Therefore, this paper investigates composition

strategies for service composers, while abstracting

from the detailed technique used. To this end, a gen-

eral algorithm for service composition is deﬁned and

its evaluation is performed in a scenario, where com-

puting power is bought from various peers. Thus,

general inﬂuences of parameters onto this algorithm

are evaluated, and the welfare a service composer can

create is deﬁned. The overall goal is to ﬁnd a strategy

for service composition, which scales well while cre-

ating high welfare for service consumers and service

composers. How this welfare is distributed fairly be-

tween the two is out of the scope of this paper, but will

addressed elsewhere with the help of public auctions

(Varian, 2003).

The remainder of this paper is structured as fol-

lows. Section 2 gives an overview of the distributed

service market and the underlying P2P architecture.

Section 3 contains deﬁnitions of services, their prop-

erties and descriptions. Section 4 introduces the gen-

eral service composition algorithm proposed. While

Section 5 presents the evaluation of this algorithm

with different parameters, Section 6 concludes the

work and gives an overview over future work.

2 P2P SERVICE MARKET

Before investigating service composition, it is nec-

essary to describe the environment in which it takes

place. Thus, the underlying service-oriented P2P net-

work and roles being part of the system are outlined.

Additionally, the service market enabled by the un-

derlying P2P network and the markets for composed

services are discussed.

2.1 Underlying Technology

The P2P network is not a physical network, but is

built on top of the Internet (A. Oram (Editor), 2001).

This implies that peers are Internet hosts and links

of the P2P network are end-to-end (e2e) connections

through the Internet between such hosts. Thus, the set

of hosts taking part in the P2P network and the e2e

connections between them form an overlay network

on top of the Internet, consisting of peers and links.

In turn, the notion of terms like ’link’ or ’neighbor’ is

different in the Internet and in the overlay network. It

is assumed that connections provide an e2e quality of

service guarantees when required by services, regard-

less of the technology used to provide these guaran-

tees, e.g., IntServ or DiffServ.

Every peer inside the network can provide and re-

quest services to and from other peers. The term

service is deﬁned as functionality which is offered

by a peer to other peers, and which can be accessed

through input and output interfaces. Services are

described through documents called service descrip-

tions, including service functionality and interfaces,

but also characteristics such as terms of usage, e.g.,

prices and methods of payment. Services can be used

by applications, which are programs running on peers

which are not provided as services themselves and of-

fer user interfaces.

A peer providing a service to another peer is acting

as a service provider, while a peer which is using a

service from another peer is acting as a service con-

sumer. A single peer can take over both roles succes-

sively or even at the same time, if he provides a set of

services to a set of peers and uses a set of other ser-

vices from another set of peers. The service usage is

always initiated by the service consumer, thus a ser-

vice provider can not supply unrequested services to

consumers or even demand payment for services de-

livered in such a fashion. Due to the dynamic nature

of P2P networks, the duration of a service usage is

restricted, i.e., it is not possible to rely on the avail-

ability of a certain service for weeks or months.

Service usage follows a one-to-one relationship be-

tween service consumer and provider, i.e., neither do

several service consumers use the same service in-

stance, nor do several service providers together pro-

vide a service to a consumer directly. Several ser-

vice consumers can still use the same service at the

same time, but several service instances are created by

the service provider and service usage takes place in-

dependently. Furthermore, service providers can use

services from other service providers, in order to pro-

STRATEGIES FOR SERVICE COMPOSITION IN P2P NETWORKS

vide a new value-added service to a service consumer.

This process of combining services is called service

composition. A peer carrying out this process is said

to play the role of a service composer. There is no di-

rect relation between additional service providers and

the service consumer. Examples of such service us-

ages are shown in Figure 1.

Peer 1

Peer 2

Peer 3 Peer 4

Application

Service Instance

Service

Service Provision

Figure 1: The use model (Gerke and Stiller, 2005)

2.2 Market Model

The service market consists of the peers which offer

services and use them, without composing new ser-

vices on demand. This service market is assumed to

be a global market with low entrance barriers, due

to its open P2P nature. This market is character-

ized through perfect competition between its partici-

pants since the number of market participants is large.

Therefore, if a peer was offering a service for a price

much higher than his own costs, another peer would

offer the same service at a lower price. Thus, prices

for services are set by the market itself through com-

petition, service prices are given and not negotiable.

Service providers do not have to price their services.

Either they are able to offer a service for the market

price or they do not offer the service at all.

Service

Consumer

(one)

Service

Composers

(a few)

Service

Suppliers

(many)

Services

Composed

Services

Service Market (with

Perfect Competition)

Composed Service

Market (Oligopoly)

Figure 2: The two markets model

However, the market model is clearly different

Table 1: Comparison of the Two Markets

Service Mar-

ket

Composed

Service

Market

No. of Sellers Many Some

No. of Buyers Many One

Market Form Perfect com-

petition

Oligopoly

Traded Good Services Composed

Services

Persistence Constant Created by re-

quest

Prices Set by market Pricing re-

quired

when service composition is considered. Service

composition is a complex process. Therefore, it is

assume that only a small number of peers decide to

take on the role of service composer. Each of them

carries out his own variant of service composition,

using his own business secrets. Service composers

act as brokers between service consumers and service

providers. They take part in the service market as buy-

ers and in the composed service market as sellers, as

shown in Figure 2.

Because of the high specialization required, only a

small number of overall peers will act as service com-

poser. Therefore, the market for composed services

will not have perfect competition but will be domi-

nated by an oligopoly of service composers. In fact,

there is a separate market for every new composed

service. It is created whenever a service consumer

contacts an oligopoly of service composers with the

request to compose a new service. Thus, pricing is

an important issue in the composed services market,

as a price has to be found for a previously inexistent

service. Table 1 covers key properties of these two

markets.

3 SERVICE PROPERTIES

Due to the market-based view and discussion above,

goods traded need a formal deﬁnition. Therefore, ser-

vices must be described by specifying their properties

as well as the implications of these properties are ex-

plained.

(American Heritage Dictionaries (Editor), 2000)

deﬁnes a service as an act or a variety of work

done for others, especially for pay. In the scope of

this paper, this deﬁnition is applied only to the en-

visioned technical system and its participants (the

peers). Thus, a service is a piece of software or a

software component operated by one of the systems

ICETE 2005 - GLOBAL COMMUNICATION INFORMATION SYSTEMS AND SERVICES

participants. It fulﬁlls one or several tasks on behalf

of another participant of the system, thus carrying out

work for him, for which he is paid.

Every service S has properties S

′

, ..., S

′

), n ∈ N which describe the char-

acteristics of the service (Dumas et al., 2001),

especially the task which is carried out by the ser-

vice. These properties may include parameters and

information from four different groups:

• Functionality: A formal description of what the

service should do (if speciﬁed by the service con-

sumer) or what it can do (if speciﬁed by the service

provider)

• Quality: A list of QoS parameters which describe

with which quality the service should be provided

(if speciﬁed by the service consumer) or can be pro-

vided (if speciﬁed by the service provider). All pa-

rameters can have ﬁxed values or can be described

with value ranges.

• Cost: A tariff (Reichl and Stiller, 2001) used to

charge the consumption of the service (if speciﬁed

by the service provider, the actual cost is calculated

by applying the tariff to the functionality and qual-

ity properties measured during the service delivery)

or the willingness-to-pay function (if speciﬁed by

the service consumer) which describes how much

the consumer is willing to pay for a service depend-

ing on its functionality and quality parameters.

• Others: Who is providing/requesting the service.

Based on these deﬁnitions and explanations of ser-

vices and their properties the following formalized

approach is taken.

Service properties are speciﬁed in service descrip-

tions, where every property S

′

consists of a for-

mal property description S

′′

and a property range

∗

= [S

′

]. The property description describes

the meaning of properties according to a common se-

mantic standard shared by all peers (Hendler et al.,

2002). The property range describes the degree of

fulﬁllment of this property in numerical values. Met-

rics generating these values are part of the property

description.

Two property descriptions match when they are se-

mantically equal according to the common semantic

standard. Thus, a property A

′

is said to fulﬁll an-

other property B

′

, if and only if, A

′′

matches B

′′

and

their property ranges overlap, i.e., B

′

≤ A

′

≤

′

or B

′

≤

′

≤

′

. Analogously, a property A

′

said to exactly fulﬁll another property B

′

, if and only

if, A

′′

matches B

′′

and A

′

= B

′

and

′

Then, a service S with properties S

′

, ..., S

′

), n ∈ N is said to implement a service de-

scription D with properties D

′

= (D

′

, ..., D

′

), m ∈

N, if and only if, all its properties fulﬁll the proper-

ties speciﬁed in the service description, i.e., n = m

and ∀i ∈ [1, n] : S

′

fulﬁlls D

′

. Analogously, a ser-

vice S with properties S

′

= (S

′

, ..., S

′

), n ∈ N is

said to exactly implement a service description D with

properties D

′

= (D

′

, ..., D

′

), m ∈ N, if and only

if, n = m and ∀i ∈ [1, n] : S

′

exactly fulﬁlls D

′

Thus, a service S is called an (exact) implementation

of a service description D, if and only if, S (exactly)

implements D. Vice versa, a service description D

is said to (exactly) describe a service S, if and only

if, S (exactly) implements D. It is assumed that ex-

act service descriptions exist and have been published

within the P2P network for all services offered by

peers acting as service providers. Of course, service

descriptions can exist without corresponding imple-

mentations.

Even if a consumer speciﬁes ﬁxed property val-

ues in a service description, an inﬁnite number of

different services can implement his service descrip-

tion. Thus, a service class is deﬁned as a set of

services which has speciﬁc common properties. Let

S = {S

, ..., S

}, n ∈ N be the set containing all

services and let D be a service description with prop-

erties D

′

= (D

′

, ..., D

′

), m ∈ N. Then,

D is called

a service class for a service description D, if and only

if,

D = {S

implements D}, S

∈ S. All ser-

vices which have these properties are called members

of the service class. Let C be the service class for

a service description D. Then, a service S is called

a member of C, if and only if, S implements D.

Thus, every implementation of a service description

is a member of the service class created by the de-

scription. Furthermore, every service description au-

tomatically creates a service class, though this class

does not need to have any members.

4 SERVICE COMPOSITION

From the service composers point of view, the service

composition process starts when he receives a service

description from a service consumer. The service con-

sumer does not have to state ﬁxed service properties

within this description, but can make use of property

ranges as deﬁned in Section 3.

After receiving the consumers service description,

the composer searches the P2P network for member

services of the service class described by this descrip-

tion. If he can not ﬁnd such a service, he starts com-

posing the service by combining other services. In

order to do this, he must obtain or create a building

plan (Gerke, 2004), which describes how to achieve

the properties of the original service class by combin-

STRATEGIES FOR SERVICE COMPOSITION IN P2P NETWORKS

ing members from other service classes. Generally,

a building plan describes how members of a speciﬁc

service class can be combined with members of other

speciﬁc service classes to create a new composed ser-

vice. In particular, it describes properties of the com-

posed service and how these properties relate to the

properties of its component services. This process is

supported by the mapping m in such a way that if

D is a service with properties D

′

, then B is called a

building plan for D, if and only if, B contains service

descriptions {D

, ..., D

}, n ∈ (N ) and a mapping

m : D

′

× ... × D

′

→ D

′

which describes the re-

lation between properties of component services (i.e.,

speciﬁc services fulﬁlling the service descriptions) on

the properties of the composed service (i.e., the ser-

vice created by composing the component service as

also described in the building plan).

When the composer has obtained one or several

building plans for the service class described by the

consumers service description, he searches the P2P

network for implementations of the service classes

described within them. If he is unable to ﬁnd any of

these service classes, he recursively creates building

plans for this service class. Thus, he creates a service

dependency graph, as shown in Figure 3.

Initial

Service Class

Service

Class 4

Service

Class 3

Implementation 6

Implementation 7

Implementation 5

Implementation 1

Building Plan 1

Service

Class 2

Service

Class 1

Implementation 4

Implementation 2

Building Plan 3

Cover

“and” node

“or” node

Service Class

Building Plan

Service Implementation

Building Plan 2

Figure 3: An example service dependence tree

The original service class (described by the con-

sumers service description) is the root of this tree.

Several ways to implement this service (one imple-

mentation and two building plans) are connected to

the root node via an or node, while service classes are

connected to the building plan via and nodes. Com-

posed services can be found by traversing the tree

from its root node. At each or node exactly one link

must be chosen for traversal, while at each and node

all links have to be traversed. A sub tree found by

applying this algorithm models a composed service

when only services form this trees leaves. Such a tree

is called a cover of the original service dependency

tree and is also shown in Figure 3. The algorithm does

rate the quality of a composed service in any way.

Building plans only ensure that every composed ser-

vice fulﬁlls the consumers initial service description

(i.e., the functionality is the same and all properties

overlap with described properties).

The following pseudo code deﬁned a recursive al-

gorithm to build a service dependence tree.

buildDependenceTree(ServDescr SC) {

while (ServDescr SI =

searchNewServiceImplementation (SC)

!= nil) {

buildDependenceTree (SI);

SC.addChild (SI);

}

while (BuildingPlan BP =

searchNewBuildingPlan (SC) != nil) {

for (int i=0; i <

BP.getChildren().length; i++)

buildDependenceTree

(BP.getChildren()[i];

SC.addChild (BP);

}

In order to achieve this presentation, slightly sim-

pliﬁed, the following assumptions have been made:

• The classes ServiceDescription and BuildingPlan

are tree nodes.

• The and and or nodes are neglected. All children of

a ServiceDescription are inherently connected via

an or node and all children of a BuildingPlan are

inherently connected via an and node.

• Each new BuildingPlan already comes with Ser-

viceClass children (instead of connecting them by

hand according to service descriptions).

The algorithm describes a general way to build ser-

vice dependence trees. By using it, any kind of ser-

vice dependence tree can be created. The decision

about what tree is created is made by the two func-

tions searchNewServiceImplementation and search-

NewBuildingPlan. These methods include individual

secrets of each service composer, namely:

• How to ﬁnd a new building plan or service imple-

mentation for a given service description.

• When to stop searching for a new building plan or

service implementation.

5 EVALUATION

The general service algorithm described in Section 4

has been evaluated by simulations. The service re-

quested in these simulations is a computing service

with a computing power measured in GFLOPS (bil-

lions of ﬂoating point operations per second). Dif-

ferent strategies for when to stop searching for more

building plans are evaluated. The criteria for evalua-

tion is the achieved average welfare W (S) of a com-

posed service S. The welfare of a composed service

depends on the utility U(S) the consumer receives

ICETE 2005 - GLOBAL COMMUNICATION INFORMATION SYSTEMS AND SERVICES

from the service, the cost of the service C(S), and

the cost for composing the service B(S) in such a

way that W (S) = U(S) − C(S) − B(S) holds. The

cost of the composed service is equal to the sum of

the costs of the services needed to create it. I.e., let

...S

, i ∈ [1, m] be the services used to create the

composed service S, then C(S) =

i=1

C(S

5.1 General Assumptions

The simulations make the following assumptions:

• The code to be executed with the computing power

can be parallelized to a granularity of 1 GFLOPS,

which is also the smallest amount which can be

bought as a single service.

• The parallelization incurs no extra effort. Espe-

cially, no additional synchronization or signalling

overhead must be taken into account.

• Any amount of required GFLOPS can be bought

directly as a service implementation from at least

one service provider. This assumption is reason-

able due to the perfect competition existing in the

service market.

• For any amount of GFLOPS ﬁve building plans ex-

ist, which describe how to obtain the GFLOPS by

buying two GFLOPS shares instead. This assump-

tion is not stringent, as there are not many dif-

ferences between ﬁnding a bad building plan and

ﬁnding none at all. Plans describe 0.9/0.1 (worst),

0.8/0.2, 0.7/0.3, 0.6/0.4 and 0.5/0.5 (best) ratios of

the original amount of GFLOPS.

• The cost of ﬁnding a new building plan or ﬁnding

out that no more building plan exist is ﬁxed and

is equal to . However, it is also assumed that the

service composer has no previous knowledge of the

cost, i.e., only after every search for building plan

does he know what this search did cost.

• The cost of ﬁnding service implementations is neg-

ligible, as this functionality is provided by the ser-

vice search of the underlying P2P network. Thus,

let n be the number of searched building plans, then

B(S) = n · Z.

• The utility function of the service consumer is in-

creasing monotonously and it is convex, based on

the amount of GFLOPS received. Thus, additional

utility created by additional amounts of GFLOPS

decreases monotonously.

• Since there is perfect competition in the service

market, all service providers use a similar cost

function. This cost function is increasing monoto-

nously and it is concave, based on the amount of

GFLOPS received. Thus, the cost of additional

GFLOPS is monotonously increasing.

• Properties are ﬁxed. This means, that the service

consumer does not specify a range of GFLOPS, but

rather a precise amount of GFLOPS. Since build-

ing plans deﬁne exact ratios for their two respec-

tive component services, the amount of GFLOPS

in service classes and service implementations also

becomes ﬁxed.

5.2 Simulation Setup

The service composition algorithm was implemented

in Java and executed with different search strategies

and parameters. Simulations were carried out on a

PC with 512 MB memory and a 1.8 GHz CPU. For all

settings, the average welfare was calculated as the av-

erage over 100 simulation runs. In all simulations the

amount of the requested computing power has been

set to 1024 GFLOPS. The cost function used in those

simulations is C(x) =

10000

, the utility function is

U(x) = log x · 10 (x being the number of GFLOPS),

as shown in Figure 4.

0 100 200 300 400 500 600 700 800 900 1000

100

GFLOPS

Utility and Cost

cost

utility

Figure 4: Utility and cost functions

5.3 Balanced Search

The algorithm used to build the service dependence

tree is constructed from the root as a balanced tree.

This means that for every service class in the tree

the same amount of searches for building plans is

carried out until the previously deﬁned search depth

is reached. Figure 5 depicts the welfare for differ-

ent costs of ﬁnding building plans, when building

plans are only searched once, i.e., no recursion takes

place (strategy #1). Vice versa, Figure 6 depicts the

welfare for different costs of ﬁnding building plans,

when recursion takes place until a certain tree depth

is reached. At all steps only a single building plan is

searched (strategy #2).

Both strategies produce good results for the aver-

age cost for searching building plans of 3. Thus, this

STRATEGIES FOR SERVICE COMPOSITION IN P2P NETWORKS

−15

−10

−5

No. of Building Plans

Building Plan Cost

Welfare

Figure 5: Welfare for different numbers of building plan

searches and different building plan costs

cost was chosen for strategy #3, which investigated,

whether the welfare could be increased by varying the

number of building plans searched at every step, as

well as the tree depth. However, the results depicted

in Figure 7 clearly show, that the increased beneﬁt

achieved by building a wide and deep service depen-

dency tree is by far outweighed by the increased cost

for building this tree. In order to optimize the welfare,

more sophisticated heuristics for inﬂuencing the trees

width and depth are required.

−300

−200

−100

100

Tree Depth

Building Plan Cost

Welfare

Figure 6: Welfare for different service dependency tree

depths and different building plan search costs

5.4 Search Until Welfare Decreases

In order to avoid search cost explosions (cf. Figure

7), the service composition algorithm was extended

with strategy #4: The service dependency tree is built

recursively without a predeﬁned search depth. The

recursion is stopped when the previous search for a

building plan has created less additional beneﬁt than

the search cost, i.e., the welfare decreased

1.5

2.5

3.5

−7000

−6000

−5000

−4000

−3000

−2000

−1000

1000

Tree Depth

No. of Building Plans

Welfare

Figure 7: Average welfare for different numbers of building

plan searches and different service dependency tree depths,

with ﬁxed building plan cost of 3

Strategy #4 is clearly not optimal, as the next search

for a building plan could still provide a higher beneﬁt.

However, results depicted in Figure 8 clearly show

that this strategy makes the algorithm resistant against

high building plan search costs. Still, the algorithm

produces bad results for higher numbers of building

plans searched.

1.5

2.5

−8000

−6000

−4000

−2000

2000

No. of Building Plans

BP search cost

Welfare

Figure 8: Average welfare for different numbers of building

plan searches and building plan search costs, using the local

welfare decrease heuristic

5.5 Tree Width Decreases with

Increasing Tree Depth

Strategy #5 makes the following extension to the

previous one: If the search continues (i.e., the last

search step produced additional welfare), the number

of building plans searched will be decreased by one in

comparison to the previous search step. This strategy

outperforms the previous one in all cases except one:

When initially only a single building plan is searched

ICETE 2005 - GLOBAL COMMUNICATION INFORMATION SYSTEMS AND SERVICES

the tree depth will only be 1 and the resulting welfare

will be equal to the welfare as depicted in Figure 9 for

the search of a single building plan.

−6000

−4000

−2000

2000

No. of Building Plans

BP search cost

Welfare

Figure 9: Average welfare for strategy #5

5.6 Tree Width Decreasing with

Welfare Decrease

Finally, strategy #6 computes the number of building

plans depending on the size of the additional welfare

created in the last search step. The welfare divided

by the cost for searching the building plan equals the

number of building plans to be searched next. This

strategy performs worse than the previous one for

higher numbers of initially searched building plans,

but always creates higher welfare for a maximum

number of searched building plans of 1.

−2.5

−2

−1.5

−1

−0.5

0.5

x 10

No. of Building Plans

BP search cost

Welfare

Figure 10: Average welfare for strategy #6

5.7 Discussion

The set of strategies show a number of different

heuristics. However, measuring scalability requires

to count the number of searches for building plans

carried out, which have been omitted in detail for

space reasons, but they are part of the calculated wel-

fare in the form of the cost for building the service

dependence tree. Thus, the depicted welfare func-

tions clearly show the inﬂuence of bad scalability in

the rapid drops of welfare due to a high number of

searches for building plans.

1 2 3 4 5 6 7 8 9 10 11

−20

−10

Building plan cost

Welfare

strategy #1, best results

strategy #2, best results

strategy #4, 1 BP

strategy #5, 2 BPs

strategy #6, 1 BP

Figure 11: Comparison of optimal sub-strategies

On one hand, simulation results show that all strate-

gies produce bad results when building a wide ser-

vice dependence tree, i.e., when searching for several

building plans for a single service class. On the other

hand, results indicate that controlling the depth of the

tree, i.e., deciding when to stop the recursion, can be

handled very well by strategies. For each of theses

strategies an optimal sub-strategy exists. Strategy #4

and #6, produce their highest welfare when at each

algorithm step a single building plan at the most is

searched. Strategy #5, produces its highest welfare

when initially searching for two building plans. Wel-

fare of these sub strategies are compared in Figure 11.

It can be clearly seen, that strategy #4 outperforms

all other strategies. Furthermore, it produces higher

welfare than the theoretical hulls of strategies #1 and

#2, which were created by manually selecting the best

sub strategy for each building plan search cost. Thus,

for any building plan search cost investigated the de-

scribed sub strategy #4 is the best choice. Further-

more, the results depicted in Figure 11 indicate that

for high building plan search costs strategy #4 pro-

duces the same welfare as the hulls of strategy #1 and

#2. This is logical, since for high building plan search

costs no proﬁt can be made, in which case all three

strategies will stop after a single search for a building

plan.

STRATEGIES FOR SERVICE COMPOSITION IN P2P NETWORKS

6 SUMMARY

Different strategies for service composition in P2P

networks have been investigated. Based on the dis-

tributed and decentralized service market together

with its underlying P2P network, the service market

model was extended by the composed service market.

Driven by models of all building blocks for the service

composition process the generic service composition

algorithm was speciﬁed, resulting in service depen-

dence trees. Different strategies for building this tree

were developed and evaluated in simulations of com-

posing a distributed computing service.

Such strategies for building the service dependence

tree can differ in the width of the tree (i.e., how many

building plans are searched for each service class)

and the depth of the tree (i.e., when to stop the re-

cursion of the tree building algorithm). Those results

show that extending the tree and the width at the same

time quickly leads to a dropping welfare of several or-

ders of magnitude. However, improved heuristically

strategies control the width and depth of the tree. All

strategies proved to be very sensitive to the number

of building plans searched at each step. Not a single

heuristic was able to produce a high (or even posi-

tive) welfare, when a larger number of building plans

could be searched for. In fact, these results indicate

that at the most two building plans should be searched

for within any service class. If those strategies were

used in this way, they produced positive welfare for

most of the investigated spectrum of building plan

search costs. One strategy produced positive welfare

for the whole spectrum, and continually outperformed

all other strategies.

Thus, it has been shown that service composition

can be economically successful, when a generic algo-

rithm is used with the proposed strategies and a small

number of building plans searched for at each step.

Future work includes the development of public auc-

tions for composed services, thus enabling the service

consumer to publish his utility function, which in turn

enables the maximization of the welfare of the com-

posed service market.

This work has been partially performed in the EU-

projects MMAPPS and DAIDALOS, and the Austrian

Kplus competence center programme.

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