Vol. 2, No. 4, pp. 227-234, 2008
Optimizing the Dynamic Composition of Web Service Components
Wei-Chun Chang*
Department and Graduate School of Information Management, Shu-Te University, Taiwan [email protected]
Ching-Seh Wu
Department of Computer Science and Engineering, Oakland University, USA
Abstract
An evolutionary process for the dynamic composition of web service components is proposed. The supporting technology for web services has been widely studied mainly focusing on the standardization of service transactions and running a single web service. In dealing with complex and large-scale web service requests, there is a foreseeable bottleneck of supporting technology. The solution proposed in this paper applies evolutionary computing techniques to automatically select optimal combinations of web service components from available component repositories. This process is illustrated with a computational simulation of component selection.
Keyword: web service; component composition; evolutionary algorithm; multi-objective optimization
1. Introduction
Requesting software services over the World Wide Web (WWW) has gained considerable momen-tum since its founding. The WWW evolved from information communication in business-oriented transactions. The functionalities of WWW also evolved over decades from the document web, to the application web and to the service-oriented web. The objective of a service-oriented web is to provide better quality software service to users. The new service is called web services [1, 2]. According to a document published by the WWW consortium, web services can be described as software entities that are capable of delivering certain functionalities over a network.
Following the definitions and specifications of web service, any organization, company, or even in-dividual developers who can deliver such functional entities can register and publish their service compo-nents to a Universal Description, Discovery, and In-tegration (UDDI) registry for public use. Accor-dingly, different web services provide certain soft-ware functionalities on the WWW. Web services can be as simple as a single transaction, e.g. the que-rying of a bank account balance, or more complex multi-services, e.g. supplying chain management systems from business to business (B2B), and many other [3]. These services have brought about new service-oriented architecture in the development of the WWW [1].
To improve the performance of web services, many researchers and organizations have been trying to define standards and unify frameworks for stakeholders in utilizing web services, e.g. a service-oriented model and architecture [1, 2]. Other techniques have also been applied to promote the use of web services, e.g. eXtensible Markup Language (XML), web service describing language
(WSDL), Simple Object Access Protocol (SOAP), and UDDI. However, current web service developments mostly focus on providing either a single service or at most a few. Focusing on single service without being prepared for complex and large-scale web services cause thechnological bottlenecks to develop. Therefore, in order to enhance service-oriented integration, the collecting and composing of web service components for complex and large-scale web service applications need to be developed and improved. The collecting process involves three important steps, i.e. publishing service components, finding service components, and binding service components into a useful web application (see Figure 1).
In composing web services, both a single service component and a series of service components that can support large-scale tasks need to be found. Ko and Neches also point out that current web service research focuses only on developing mechanisms to describe and locate individual service components in a network environment [4]. They further argue that the use of web services must combine various services to enable large-scale task management, e.g. business to business service transactions. The process involves the integration of service components, which may be provided by different providers. Based on current mechanisms, it is impractical for users to properly use multiple web service tasks for large-scale systems. To solve the problem, a high-level system (Eurasia: Exploring, Understanding, and Recording Analysis Steps in Informa-tion-Management Applications) is proposed to help users quickly explore and test various web services for large-scale systems. In their research, they only focused on how to retrieve and assemble web service components. However, neither the problems in combining many different service
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providers with similar functionality components or the suitability of combinations for the service requestors were addressed. It is vital to develop a composition process that can search available UDDI component repositories and integrate better service components for each request. The process as speci-fied by Ko and Neches has not been satisfactorily studied and developed [4]. Hence, the computing techniques for composing better web services need to be investigated.
In dynamic composition the information regard-ing suitable service components need to be acquired from many service providers whose components are registered in a UDDI registry repository. The next step is to negotiate with different service providers in order to integrate suitable service components. The integration is successful when multi-objectives set by a service requester are met (i.e., the quality of servic-es (QoS) such as reliability, functionality, and execu-tion time [5]). However, such performance measurement is always difficult to assess in software systems. The same applies to a web service field. A quantitative analysis study on several possible software architectural options was done and a classification of web server software architecture is provided in [6]. Aspects of performance (i.e. service time, physical contention, software contention, and the trade-off analysis of these performance factors) are discussed. To evaluate web service composition, several aspects of the quality of service have been proposed, e.g. web service composition – Business Process Execution Language for Web Services (BPEL4WS1) [7], web service coordination, web service transaction, web service security, and web service reliability. These aspects present the critical factors in business processes.
The problems addressed in this research mainly revolved around the search for better web service components. Firstly, current solutions to web service
1
BPEL4WS defines a language for creating service compositions in the form of business processes. It supports a process-oriented form of service composition that interacts with a set of web services to achieve a certain goal.
architecture primarily revolve around the structure of a single service. For multiple service integration, a series of service components need to be integrated. Therefore, good quality and performance service components covering by different service providers are important. Cost-effectiveness also needs to be addressed. The cost for web service is increasing especially with high quality components. Hence, trade-off analysis is one of the key issues for assembling web service components. This involves multi-objective optimization techniques to select a group of optimal solutions to support decision making. To implement dynamic web service composition, suitable service components and organize optimal combinations from different service providers need to be searched. When compounded with the above multi-objective optimization problem above the problem gets more complicated. These issues are addressed in this paper.
2. Solutions for Dynamic Composition
Evolutionary Algorithms (EAs) have been ap-plied as the searching algorithms to search the optim-al solutions for combinatorioptim-al problems. “Survivoptim-al of the fittest” is a principle in the natural environment which is used in the searching algorithm to generate survivors, the optimal solutions, for a given problem. To establish the basis of the evolutionary computing (EC) field, several studies were reviewed. The prin-ciples of the EC theory are based on Darwin’s theory of natural selection to solve real world problems [8]. EAs have been successfully applied in optimizing the solutions for a variety of domains [9]. The strength of EC techniques comes from the stochastic strategy of search operators. The major components in EC are search operators acting on a population of chromo-somes. EC was developed to solve complex problems, which were not easy to solve by existing algorithms [9]. To fully understand the basis of EAs, Bäck iden-tified three characteristics [10]. The method utilized in the algorithm to progress the search from ancestors to offspring is the collective learning process; species information is collected during the evolutionary process, and the offspring that inherit good genes from parents survive the competition. This is the first characteristic of EAs. Next, the generation of des-cendants is handled by the search operators2, cros-sover and mutation; which explore variations in spe-cies information in order to generate offspring.
Crossover operators exchange information be-tween mating partners. On the other hand, a mutation operator, which mutates a single gene with very small probability, is used to change the genetic material in an individual. Finally, the third characteristic that defines EAs is the evaluation scheme, which is used
2
Search operator: The operator that explores new chromosomes in a searching space.
Agent (Composite Applications)
Service
Providers RequestersService
Pub lishi ng W S C ompo nent s UDDI Registry – Service Directory
(IBM, Microsoft, SAP, NTT) Req ue sts Locate Services
Define Interface (WSDL, SOAP)
W S C om positi ons Deliver WS components
Figure 1. Communication diagram of a web service request
Vol. 2, No. 4, pp. 227-234, 2008 to decide who the survivor is. The evaluation scheme
is the most diverse characteristic of the three due to the different objectives used to select the different solutions needed in different domains. The evaluation scheme can be as simple as good or bad, a binary decision; or as complex as nonlinear using multiple mathematical equations to assess trade-offs between multiple objectives.
For this study EC techniques provided stochastic searching techniques aimed at global optimization. Global optimization searches for the best perfor-mance of solutions in the objective space. A general global optimization problem can be defined as fol-lows.
)
x
(
f
min
)
x
(
f
*
x
) (x c to subjectwhere f(x) is the global optimization in objective space when determining the minimum of the function f(x); x is a vector of variables which lies in the feasi-ble region , any x in defines a feasible solution in which x conforms to the constraints c(x). A similar definition can also be applied to the maximization of objective functions.
3. Composing the Fittest Web Service
Composition
The evolutionary algorithm developed in this paper is listed below.
BEGIN
1 gen=0
2 P(gen) = Initialize ( N ) 3 Transformation (O(gen)) 4 F(gen) = Evaluate (O(gen)) 5 VPareto[ ] = P(gen)
6 while (Criterion not met) do { 7 P’(gen) = Crossover (P(gen)) 8 P’(gen) = P’(gen)
+ Mutation (P(gen)) 9 Transformation (O’(gen)) 10 F(gen) = Evaluate (O’(gen)) 11 P”(gen) = P(gen) + P’(gen) 12 P(gen+1)= EliteSelect(P”(gen)) 13 Pareto (VPareto[], P(gen+1) )
14 gen = gen + 1
15 END while
END
After the random initialization of population size N (line 2), the equalization function transforms the values of each objective value into a [0, 1] range for P(gen) (line 3). The fitness evaluation of the initial population takes place before the evolutionary process is entered (line 4). A Pareto vector VPareto[]
is used to collect the Pareto optimal set and is initia-lized in line 5 by the initial population. The
evolu-tionary process then enters an iteration phase to search for optimal solutions. Termination depends on the generation number (line 6). The new genera-tion is selected using an elite policy3. To maintain the Pareto optimal set (line 12), VPareto[] is updated
after the next generation has been selected using the definition of Pareto dominance (line 13). The size of VPareto[] is varied dynamically in order to
accom-modate an unknown number of solutions if the solu-tions survive the Pareto dominance test given.
The design objective of this study was to develop an EC-based process incorporated with a current web service transaction procedure (see Figure 2) to search the solution space. The space was created by collecting information of service components through UDDI registries for the optimization of web service composition. This type of evolutionary process has also been developed and tested in requirements engineering in order to search for the optimal solutions for system specification [11].
The fundamental designs of an EC-based process in this study were focused on the definition of search space, chromosome structure design, objec-tive function definitions, and fitness assessment algo-rithm. In general, to apply the process in web ser-vice composition, the major steps of the process are defined as follows.
1) Collecting the information of component regi-strants: the size of searching space is decided by the number of component registrants collected from available UDDI registries. Therefore, it is very im-portant to obtain the information of all available components from component registration agents. The information regarding the description of service components can be collected from a component li-brary as specified in [12]. The communication pro-tocol is based on a set of API message (i.e., UDDI 3.0 and up).
3
The elite policy selects the top N chromosomes as survivors based on their fitness index.
Population + Performance No Web Service Task sequence Component Library Chromosome Mapping Initial population Performance Interpretation Fitness Assessor Surviving Chromosomes. Yes Output Results The process of collecting all available
service components Termination criteria Searching Operators: Crossover ,Mutation The population of Next Gen. Survival Criteria
Figure 2. Evolutionary process used to optimize the composition of web services
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2) Modeling component resources from different providers: service components are classified and constructed into database tables based on the func-tionalities and characteristics of service applications requested. The work flow of the service applica-tions can be modeled by using a scenario-based me-thod that is used to describe the task steps required to accomplish the completion of web service applica-tions.
3) Applying the sequence of web service composi-tion and chromosome encoding/decoding: the task sequence of web services that are needed to be opti-mized is defined. A sub-task service in a task se-quence can be defined as
component
ji,
sub
task
j
where it is assumed that one sub-task can be com-pleted by a service component. By utilizing the col-lected information of component registrants, a web service task sequence is transformed into a binary string, i.e. encoding a solution into a chromosome. The chromosome mapping mechanism utilizes a hie-rarchical structure (see Figure 3) for an encod-ing/decoding task sequence and chromosome.
4) Fitness assessment: To evaluate the design quality of software applications, multi-parameters or attributes are used in the metrics to evaluate perfor-mance and quality. The metric measurement focuses on different aspects based on what criteria customers require. Such measurement is a key element of eva-luating the performance and quality of software ap-plications. Many metrics have been developed to measure different aspects of software. Based on [13], the principles of the measurement are:
To assist the evaluation of analysis and design models
To provide an indication of the complexity of procedural designs and source code
To facilitate the design of more effective testing According to these principles, several suitable evaluation metrics are discussed (e.g. reliability, time, and cost) in this paper. To associate these mea-surements with EC techniques, the metrics have been called objective evaluation functions. Another reason a limited number of objectives were used to evaluate web service components instead of applying full range of metrics was that too many metric measure-ments may distract the sensitivity of each objective in the optimization process. Therefore, several com-monly used metrics were selected for the evaluation of multi-objectives in the design architecture for sim-plicity. The sensitivity and dependency analysis be-tween different metrics was beyond the research ob-jectives of this study. The fitness assessment con-sisted of two stages. Firstly, the evaluation function of each objective was depicted as follows:
Service component reliability: Unlike other quality factors, software reliability can not be measured di-rectly. As specified in [14], software reliability is defined in statistical terms as “the probability of fail-ure-free operation of a computer program in a speci-fied environment for a specispeci-fied time”. For the sim-plicity, a simple measure of reliability for the meas-ure of each service component was adopted as de-fined in [13]. The measure is dede-fined as follows:
MTTR
MTTF
MTBF
(1)
where, MTBF is mean-time-between-failure; MTTF is mean- time-to-failure; and MTTR is mean-time-to-repair.
Service cost: a service cost objective was adopted in this design. In this study, only the cost summa-tion of all service components organized for a service request was considered. The summation equation is defined as follows:
i component all for i Cost ServiceC
C
_ (2)Service time: a service time objective was adopted in this design. The running time was the summation of all service components organized for a service re-quest. The summation equation is defined as fol-lows:
i component all for i Time Service T T _ (3)Next, a simple, but effective multi-objective optimization algorithm, called DFBMOEA (Distance Function-Based Multi-Objective Evolutionary Algorithm) [15], was adopted as the fitness function applied in the evolutionary process to assess overall evaluation of the objective functions above. The advantage of using DFBMOEA is that the algorithm Example: Component (COMP00) is
used to complete task (ST0) Phenotype: {COMP00, ST0} Genotype : 0001 01
1stlevel – a service task sequence Task-1 Task-2 Task-3 …….. 1 2 . . . . . . . N Population (N)
2ndlevel – a task node Service Component Task – x
Figure 3. Chromosome structure of a service task sequence
Vol. 2, No. 4, pp. 227-234, 2008 can assess up to n objectives ( I
n ). Therefore, the dimension of objectives is not limited to the fitness assessment [16]. This is very important especially when many critical factors, i.e. security, cost, time, reliability, and interoperability, are commonly requested to evaluate different types of web service compositions.
4.
Experiment Delimitation
To illustrate the EC-based process applied for optimizing web service compositions, a component searching case and a computational simulation were implemented.
4.1 A Web Service Component Collection
The collection process of service component information is the key stage in our design. The information is used to create the search space before the EC-based process is applied. To achieve that, several UDDI registries maintained by IBM, Microsoft, and others provided registered service components associated with their service descriptions. The search process was implemented through their web sites (i.e., https://uddi.ibm.com/ubr/registry.html) as illustrated in Figure 4.Alternatively, developers can embed the inquiries into programming codes for inquiring available service components from UDDI registries. Here, a search process was implemented by using Perl-CGI program 4 to acquire web service components from a IBM UDDI registry server. The screen is given in Figure 5.
4.2. Computational Simulation
To test the applicability of the EC-based process (see Figure 2) applied to optimize service component composition, a computational simulation that used a component generator was adopted to create a set of virtual service components5 associated with three component properties, e.g. time, reliability, and cost. The simulation parameters are described in the following sub-sections.
4.2.1 Component Generation
In the simulation, 10 sub-tasks in a web service task sequence were used as the testing case. The task sequence is expressed as follows.
COMP0i,ST0
COMP1i,ST1
...COMP9i,ST9
Where, COMPji,j
0,..,9
,i
0,..,9
, is the symbolrepresenting components that can be allocated to im-plement task (STj). A set of virtue service components
used to test the EC-based process were generated by
4
Perl provides “UDDI::Lite” module to process the inquiry. 5
These virtual components were used for the simulation only. They can be replaced by real service components.
a computational generator. Parts of the simulated components are listed in Table 1, where 3 properties, e.g. time, reliability, and cost, associated with each component were adopted. Here it was assumed that each task had 10 service components as the available choices from a component library. In total, 100 com-ponents for 10 sub-tasks were generated in the simu-lation service task sequence.
Figure 4. Search process through UDDI registry
Figure 5. Search process through Perl-CGI pro-gram
Table2. Configurations of EA operators applied in the experiments
Operators Parameters Policy
Population Size 64
Initialization Random
Crossover Mating rate 0.9
Selection Roulette Wheel
Mutation Probability 0.001
Selection Next Gen. Elite
Termination
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4.2.2 EC-Based Process Parameters
The chromosome structure (see Figure 3) was designed in two levels. The top level coded a cycle of sub-tasks, while the second level described the component–task type combination within a task node. The binary string for a task node was 6 bits long; with the component 4 bits were assigned and with the task 2.The chromosome represents a task sequence containing multiple task nodes. Hence, the length of the chromosome binary string is based on how many nodes in a sequence, e.g. if the length of the chromosome binary string is 10 (sub-tasks) * 6 (bits/task) = 60 bits. The initial population was set to 64 and the combinations of component and tasks were generated randomly within the constraints imposed by the domain. The configurations of the EC parameters for the experiment are given in Table 2.
The configurations of the EA operators were based on the literature survey [8]. These might not have been the optimal configurations for the web service domain; however, it was sufficient to achieve the objective for this design i.e., justifying the appli-cability of EAs in the web service composition prob-lem domain.
4.2.3 Results and Discussions
The experimental results generated by the EC-based procedure are illustrated in Figure 6.
In the Figure, all explored chromosomes (web service compositions) were collected through the evolutionary process and presented in Figure 6, a 3-objectives solution space. Each point represents a combination of service components to complete the web service task sequence. The fittest chromosome is indicated with an arrow pointed being the fittest according to the DFBMOEA. The optimization of fitness is illustrated in Figure 7.
In the Figure, the optimization solution is con-verged within 50 generations. The quick convergence illustrates the applicability and efficiency of the EC algorithms in optimizing web service composition.
Although the simulation was tested through a set of service components randomly generated by computers, the optimal results demonstrated that the component combinations were easily optimized by the EC-based process proposed in this paper.
5. Conclusions
Compared to current solutions, which only focus on single web service design or neglect the combina-torial and multi-criteria optimization problems, the approach in this study considered the possibility of an optimization solution in composing the most suitable web service components for complex and large-scale systems in the allocation of web services. Therefore, the contributions of this paper are as follows:
(1) A new approach is developed to web service composition in order to optimize the search of better service components for complex and large-scale sys-tems.
(2) A trade-off analysis of multiple objectives, the analysis studied the conflicting between objectives in composing better web services, i.e. costing vs. relia-bility. The optimal results fitted the principle of natural evolution, which looks for the fittest survivor Figure 6. The experimental results illustrated in 3-D
solution space
Table 1: Components randomly generated by computational model
Component Task time Reliability Cost
COMP00 ST0 0.56 0.75 2232 COMP10 ST1 0.21 0.53 575 COMP20 ST2 0.35 0.76 866 COMP30 ST3 0.37 0.81 1685 COMP40 ST4 0.12 0.73 1815 COMP90 ST9 0.25 0.89 1840
Figure 7. Fitness of optimal service composition versus generations
Vol. 2, No. 4, pp. 227-234, 2008 in multi-criteria assessment.
In summary, the optimization results illustrated the objectives of this paper: the optimal component combinations of web services from all available components. The multi-objective case defined in the experiments also reflected the real world situation when composing service components over the WWW. Another advantage of using an EC-based process is that if one web service component does not fit after the selection criteria have been changed by the users during the composition process, this approach pro-vides an immediate re-run to re-organize the optimal solutions to satisfy the new criteria.
Although the applicability of EC algorithms to optimize service component combination have been demonstrated, there are several constraints and limi-tations in this approach making further study neces-sary in order to improve its performance. For instance, an integration interface between the current solutions and the present approach is urgently needed. As for the improvement of the EC-based process, environ-mental factors need to be considered in the designs that are currently used [17]. The dependency be-tween the different service components also needs to be considered to improve the fitness assessment. For example,
IF
{Component A and B are selected in a service task sequence Composition} THEN
{The interoperability improves x%}
The example illustrates the dependent relation-ship between components A and B. This type of dependency is closely related to certain objective functions. Therefore, integrating the dependency information with objective functions can improve the accuracy of the results and lead to better optimal so-lutions.
The optimization process for using EAs in a problem domain has been demonstrated, i.e. optimiz-ing a task sequence for web service composition uti-lized a set of service components randomly generated by a simulation program. Based on the generaliza-tion of this experience, the optimizageneraliza-tion procedures can be applied in web service composition to search optimal components.
References
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Biographies
Wei-Chung Chang received
the MS degree from Duke University, USA in 1997, and Ph.D. degree from the Univer-sity of Manchester, Manchester, U.K in 2004. His Ph.D. thesis applied evolutionary computing to requirements engineering and developed the evolutionary requirements analyzer tools. He is currently with the Department and Graduate School of Information Management, Shu-Te University, Taiwan, R.O.C.
Chingseh Wu received the MS
degree from US Air Force In-stitute of Technology in 1993 and PhD degree from Texas A&M University in 2000, both in computer sciences. In 2007, he joined the Department of Computer Science & Engi-neering, Oakland University, Michigan, USA. His current research interests include Software Engineering, Software Validation and Testing, and distributed computing.
