Optimizing the Dynamic Composition of Web Service Components

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1 Optimizing the Dynamic Composition of Web Service Components Wei-Chun Chang* Department and Graduate School of Information Management, Shu-Te University, Taiwan 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 momentum 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 individual developers who can deliver such functional entities can register and publish their service components to a Universal Description, Discovery, and Integration (UDDI) registry for public use Accordingly, different web services provide certain software functionalities on the WWW Web services can be as simple as a single transaction, eg the querying of a bank account balance, or more complex multi-services, eg 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, eg a service-oriented model and architecture [1, 2] Other techniques have also been applied to promote the use of web services, eg 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, ie 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, eg 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 Information-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 * Corresponding author 227

2 Publishing WS Components Service Providers UDDI Registry Service Directory (IBM, Microsoft, SAP, NTT) Agent (Composite Applications) Deliver WS components Locate Services Define Interface (WSDL, SOAP) Requests WS Compositions Service Requesters Figure 1 Communication diagram of a web service request 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 specified 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 regarding 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 (ie, the quality of services (QoS) such as reliability, functionality, and execution 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 (ie 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, eg web service composition Business Process Execution Language for Web Services (BPEL4WS 1 ) [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 228 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 applied as the searching algorithms to search the optimal solutions for combinatorial problems Survival 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 principles 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 chromosomes 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 identified 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 descendants is handled by the search operators 2, crossover and mutation; which explore variations in species information in order to generate offspring Crossover operators exchange information between 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

3 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 performance of solutions in the objective space A general global optimization problem can be defined as follows f* ( x ) min f ( x ) x subject to c(x) where 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 feasible 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 END 1 gen=0 2 P(gen) = Initialize ( N ) 3 Transformation (O(gen)) 4 F(gen) = Evaluate (O(gen)) 5 V Pareto [ ] = 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 (V Pareto [], P(gen+1) ) 14 gen = gen END while 229 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 V Pareto [] is used to collect the Pareto optimal set and is initialized in line 5 by the initial population The evolutionary process then enters an iteration phase to search for optimal solutions Termination depends on the generation number (line 6) The new generation is selected using an elite policy 3 To maintain the Pareto optimal set (line 12), V Pareto [] is updated after the next generation has been selected using the definition of Pareto dominance (line 13) The size of V Pareto [] is varied dynamically in order to accommodate an unknown number of solutions if the solutions 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] Web Service Task sequence The population of Next Gen Searching Operators: Crossover,Mutation No Termination criteria The fundamental designs of an EC-based process in this study were focused on the definition of search space, chromosome structure design, objective function definitions, and fitness assessment algorithm In general, to apply the process in web service composition, the major steps of the process are defined as follows 1) Collecting the information of component registrants: the size of searching space is decided by the number of component registrants collected from available UDDI registries Therefore, it is very important 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 library as specified in [12] The communication protocol is based on a set of API message (ie, UDDI 30 and up) 3 The elite policy selects the top N chromosomes as survivors based on their fitness index Yes Output Results The process of collecting all available service components Initial population Performance Interpretation Surviving Chromosomes Component Library Chromosome Mapping Population + Performance Fitness Assessor Survival Criteria Figure 2 Evolutionary process used to optimize the composition of web services

4 2) Modeling component resources from different providers: service components are classified and constructed into database tables based on the functionalities and characteristics of service applications requested The work flow of the service applications can be modeled by using a scenario-based method that is used to describe the task steps required to accomplish the completion of web service applications 3) Applying the sequence of web service composition and chromosome encoding/decoding: the task sequence of web services that are needed to be optimized is defined A sub-task service in a task sequence can be defined as componentji, subtask j where it is assumed that one sub-task can be completed by a service component By utilizing the collected information of component registrants, a web service task sequence is transformed into a binary string, ie encoding a solution into a chromosome The chromosome mapping mechanism utilizes a hierarchical structure (see Figure 3) for an encoding/decoding task sequence and chromosome To facilitate the design of more effective testing According to these principles, several suitable evaluation metrics are discussed (eg reliability, time, and cost) in this paper To associate these measurements 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 measurements may distract the sensitivity of each objective in the optimization process Therefore, several commonly used metrics were selected for the evaluation of multi-objectives in the design architecture for simplicity The sensitivity and dependency analysis between different metrics was beyond the research objectives of this study The fitness assessment consisted 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 directly As specified in [14], software reliability is defined in statistical terms as the probability of failure-free operation of a computer program in a specified environment for a specified time For the simplicity, a simple measure of reliability for the measure of each service component was adopted as defined in [13] The measure is defined as follows: Population (N) 1 2 N 1 st level a service task sequence Task-1 Task-2 Task-3 2 nd level a task node Service Component Task x Example: Component (COMP00) is used to complete task (ST0) Phenotype: {COMP00, ST0} Genotype : MTBF MTTF MTTR (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 summation of all service components organized for a service request was considered The summation equation is defined as follows: Figure 3 Chromosome structure of a service task sequence 4) Fitness assessment: To evaluate the design quality of software applications, multi-parameters or attributes are used in the metrics to evaluate performance and quality The metric measurement focuses on different aspects based on what criteria customers require Such measurement is a key element of evaluating the performance and quality of software applications 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 C Service_ Cost Ci (2) for all componenti 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 request The summation equation is defined as follows: T Service_ Time Ti (3) for all componenti 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 230

5 I can assess up to n objectives ( n ) Therefore, the dimension of objectives is not limited to the fitness assessment [16] This is very important especially when many critical factors, ie 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 41 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 (ie, 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 42 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 components 5 associated with three component properties, eg time, reliability, and cost The simulation parameters are described in the following sub-sections 421 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 COMP, ST COMP, ST COMP ST 0 i 0 1 i 1 9 i,, is the symbol representing components that can be allocated to implement task (ST j ) A set of virtue service components used to test the EC-based process were generated by Where, COMP ji, j 0,,9, i 0,,9 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 9 Figure 4 Search process through UDDI registry Figure 5 Search process through Perl-CGI program Table2 Configurations of EA operators applied in the experiments Operators Parameters Policy Population Crossover Size 64 Initialization Random Mating rate 09 Selection Roulette Wheel Mutation Probability 0001 Selection Next Gen Elite Termination Criterion 500 generations a computational generator Parts of the simulated components are listed in Table 1, where 3 properties, eg 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 components for 10 sub-tasks were generated in the simulation service task sequence 231

6 422 EC-Based Process Parameters Table 1: Components randomly generated by computational model Component Task time Reliability Cost COMP00 ST COMP10 ST COMP20 ST COMP30 ST COMP40 ST 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 COMP90 ST Figure 7 Fitness of optimal service composition versus generations Figure 6 The experimental results illustrated in 3-D solution space 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 2The 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, eg 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 ie, justifying the applicability of EAs in the web service composition problem domain In the Figure, the optimization solution is converged 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 combinatorial 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 systems (2) A trade-off analysis of multiple objectives, the analysis studied the conflicting between objectives in composing better web services, ie costing vs reliability The optimal results fitted the principle of natural evolution, which looks for the fittest survivor 232

7 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 provides 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 limitations in this approach making further study necessary 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, environmental factors need to be considered in the designs that are currently used [17] The dependency between 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 relationship 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 solutions The optimization process for using EAs in a problem domain has been demonstrated, ie optimizing a task sequence for web service composition utilized a set of service components randomly generated by a simulation program Based on the generalization of this experience, the optimization procedures can be applied in web service composition to search optimal components References [1] "World Wide Web Consortium," 2004 [2] "Web Services Organization," 2004 [3] B Benatallah, M Dumas, M-C Fauvet, F A Rabhi, and Q Z Sheng, "Overview of some patterns for architecting and managing composite 233 web services," ACM SIGecom Exchanges, vol 3, pp 9-16, 2002 [4] I-Y Ko and R Neches, "Composing Web Services for Large-Scale Tasks," IEEE Internet Computing, vol 7, pp 52-59, 2003 [5] D A Menasce, "QoS-Aware Software Components," IEEE Internet Computing, vol 8, pp 91-93, 2004 [6] D A Menasce, "Web Server Software Architectures," IEEE Internet Computing, vol 7, pp 78-81, 2003 [7] "Business Process Execution Language for Web Services Importer/Exporter Technology," 2004 [8] T Bäck, D B Fogel, and T Michalewicz, Evolutionary Computation 1, Basic algorithms and operators Bristol: Institute of Physics Publishing, 2000 [9] T Bäck, U Hammel, and H-P Schwefel, "Evolutionary Computation: comments on the history and current state," IEEE Transactions on Evolutionary Computation, vol 1, pp 3-17, 1997 [10] T Bäck, "Introduction to evolutionary algorithms," in Evolutionary Computation 1, Basic algorithms and operators Bristol: Institute of Physics Publishing, 2000a, pp [11] W C Chang, "Optimising system requirements with evolutionary algorithms," in Department of Computation Manchester: UMIST, The University of Manchester Institute of of Science and Technology, 2004, pp 165 [12] J Yang, "Web service componentization," in Communications of the ACM, vol 46, 2003, pp [13] J D Musa, A Iannino, and K Okumoto, Engineering and managing software with reliability measures: McGraw-Hill, 1987 [14] R S Pressman, Software Engineering: A Practitioner's Approach: McGraw-Hill Science/Engineering/Math, 2004 [15] W C Chang, A Sutcliffe, and R Neville, "A Distance Function-Based Multi-Objective Evolutionary Algorithm (DFBMOEA)," presented at Proceedings of the Genetic and Evolutionary Computation Conference, LBP (GECCO 2003), Chicago, Illinois, 2003 [16] C A Coello Coello, D A Van Veldhuizen, and G B Lamont, Evolutionary algorithms for solving multi-objective problems (genetic algorithms and evolutionary computation): Plenum Pub Corp, 2002 [17] J McEachern, "Emerging technologies," presented at The 2002 IEEE World Congress on Computer Intelligence, Congress on Evolutionary Computation, Hilton Hawaii Village Hotel, Honolulu, Hawaii, USA, 2002

8 Biographies Wei-Chung Chang received the MS degree from Duke University, USA in 1997, and PhD degree from the University of Manchester, Manchester, UK in 2004 His PhD 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, ROC Chingseh Wu received the MS degree from US Air Force Institute 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 & Engineering, Oakland University, Michigan, USA His current research interests include Software Engineering, Software Validation and Testing, and distributed computing 234