Research on a Kind of QoS-Sensitive Semantic Web Services Composition Method Based on Genetic Algorithm

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1 roceedings of the 7th International Conference on Innovation & Manageent 893 Research on a Kind of QoS-Sensitive Seantic Web Services Coposition Method Based on Genetic Algorith Cao Hongjiang, Nie Guihua, Chen Donglin School of Econoics, Wuhan University of Technology, Wuhan,.R.China, (E-l: chjnewfree@yahoo.co.cn) Abstract In the process of QoS-sensitive business, the user not only cares about whether the function of the process will be finished successfully, but also pays great attention to the QoS of the whole process. Thus, how to ake a choice aong the candidate web services so that the selected ones can not only finish the assigned task and confor to the local restriction, but also can cooperate with other web services and optiize the QoS of the process, becoes a key proble. In order to solve this proble, an ontology odel of the web service quality attributes and an approach based on the generic algorith were proposed. The experiental results show the effectiveness of this ethod. Key words Seantic web services; Ontology odel; Qos; Generic algorith Introduction With the continuous developent of seantic web services, dync service coposition which based on web services as an iportant ethod to achieving a flexible, rapid integration becoe a new hotspot. At present, as the nuber of service coponent growth rapidly, how to select the results to eet the needs of the user QoS requireents quickly in assive result is one of the n objectives of the developent of web service coposition. In the Web service selection, in addition to functional attributes, the user can also in soe non-functional properties of set constrnts to coplete, naely, by constrnt QoS (quality of service) property to choose. QoS contn the attributes are: price, response tie, avlability, and reliability, and so on [,2] and the Web service providers will often set a range of QoS property value, so as to provide reference for potential users. In the process of QoS-sensitive business, the user not only cares about whether the function of the process will be finished successfully, but also pays great attention to the QoS of the whole process. Thus, how to ake a choice aong the candidate web services so that the selected ones can not only finish the assigned task and confor to the local restriction, but also can cooperate with other web services and optiize the QoS of the process, becoes a key proble. We know that QoS query essage forat provided by the different service providers and service users is likely to be a variety of In order to provide QoS achine-readable inforation, the establishent of a QoS ontology odel to standardize the description of the inforation QoS inforation is iperative. This paper presents an ontology odel of web service quality attributes and genetic algoriths ethods to select Web services effectively, and through experients proved the effectiveness of the ethod. Copared with existing ethods, this paper presents the ethod has a better universal. 2 Related Work With the popularity of Web services in recent years, there has been a lot of research work in the area of use of Web service coposition to obtn business processes. L Aversano [3] et al. proposed to use existing Web services, through the dync cobination of the to be business processes As the Internet environent and dync nature of the inherent randoness, people tend to deand through a cobination of Web services have been in business processes to eet certn service level agreeent SLA (service level agree-ent), which satisfy the QoS constrnts. Therefore, how to solve the QoS-sensitive issue of Web service coposition, the researchers ade a lot of discussion. One of typical odel is used eta-process and eta-services concept, a eta-service representative a group of siilar business functions of the Web services. Thus, concrete services that corresponding to the sae eta- service can be replaced with each other. Meta-process is coposed by the eta services to an abstract process odel. In such paradig, soe researchers have proposed ethods based on integer progrng [4, 5] ; this ethod assues that the constrnt conditions and objective function has a linear character. Zeng s research [4] focus on overhead response tie, effectiveness and reliability QoS attributes. And in the course of calculation the objective function used the logarithic strn. Although, Zeng think their odel can extend the other siilar behavior property, in reality, the process required to

2 894 roceedings of the 7th International Conference on Innovation & Manageent eet the constrnts ore coplex, often do not have the linear characteristics. Such as service dependencies and user preferences. Therefore, Zeng proposed odel does not have a good universal. Although Aggarwal [5] referred to the needs of such non-linear constrnt, but did not give the appropriate solutions. Copared with such ethods based on integer progrng, the ethod proposed in this article based on genetic algoriths that can handle ore fors of restrnt, with a better universal. Such paradig is used in another ethod of artificial intelligence query strategy. This ethods fro artificial intelligence constrnt logic CL (constrnt logic progrng), it has the advantage of strong odeling capabilities; Yu T and Berbner R s [6][7] article is the study of this approach. As the CL has the characteristics of transission constrnts, therefore, these ethods are ore efficient query, but the cost of its own is relatively large. In the QoS-sensitive Web service coposition process, but also due to fewer constrnts in general are relatively siple. Therefore, this article considers that the adoption cost of a relatively sall. Genetic algorith is ore suitable. As the genetic algorith itself does not provide echaniss to describe the binding, so a lot of research work to achieve through the penalty function ethod. For QoS- sensitive Web service coposition proble [8, 9, 0]. 3 The Qos Ontology Model Different service providers and service users with the QoS query essage forat is likely to be varied. In order to provide QOS achine-readable inforation, the establishent of a QoS ontology odel to regulate the QOS inforation description is necessary [].This paper presents an ontology odel of the web service quality attributes, to a unified description of the various service quality attributes. Ontology is structured as follows: Top-level ontology Top-level ontology nly definitions soe of the advanced concepts related to QOS odels.for each concept and the relationship between these concepts will be further explanation in this article, Qosrofile is the ost iportant concept, which is the n body of Qos odel. QosAttribute: used to indicate Qos paraeter, which is the super class of all Qos paraeters,in practical situations, it contns the description of the price of Qos paraeters describing the response tie of the Qos paraeters, describing the reliability of the Qos paraeters, etc. Qosrofile through its hasqosatrribute attributes can define all the Qos paraeters of a service Figure QoS Ontology Model In order to eet the needs of Web services atching and choice, QosAirribute derived for the three subclasses: CoonQosAttribute, DonQosAttribute, and SpecialQosAttribute. CoonQosAttribute refers to all WebServiee need to possess Qos paraeters, such as price, response tie, reliability, reputation and so on. DonQosAttribute refers to the Qos paraeters relevant to the don, such as the packaging of the books bought is hardcover" or stripped-down" and so on. SpecialQosAttribute used to describe service-specific QoS paraeters. For service providers in the publication of the QoS inforation or Service requestor proposed QoS requireents. Accordingly, Qosrofile through the three subattributes of hasqosattribute: hascoonqos Attribute, hasdonqosattribute, hasspecialqosattribute link with three subclass.qosattribute is the super class used to describe all the paraeters of QoS. Qos paraeters of the class can b e expressed as etrics, onotonicity, and share of weight in the Qosrofile and so on. If it is

3 roceedings of the 7th International Conference on Innovation & Manageent 895 coposite Qos paraeters, this odel can also describe it with other Qos paraeters. QoS calculation of business processes In order to coplete the Web services choices according to QoS, we need to describe how fro a single Web service QoS attribute value calculating business processes. For a given specific Web services, business processes, Table shows the calculation of the entire process of the QoS rules [2], and lists the calculation function corresponds to the variety of coon processes structure and coon QoS properties. Meanwhile, in the last line of Table also gives the process the calculation of user-defined (such as the don dependent properties) property rules. In this article, due to space liitations, only by tie, cost, QoS, etc. these coon attributes, to deonstrate, validate the ethod proposed in the text. Table The Model for Coputing the QoS of Services Copositon Qos Attribute Sequence Switch arallel Loop Tie Cost Avlability Reliability T(t ) i C(t ) i T(ti) ax{t(t i) i (...p) } kt(t) p C(ti) A(t i) A(ti) R(t ) i R(ti) C(t i) kc(t) A(t i) A(T) k R(t ) i R(t) k User-defined fs(f(t i )) i{...} f R ((,F(t i ))) i{...n} f F (F(t i )) i{...p} f L (k,f(t)) 4 A Method Based on Genetic Algorith Genetic algorith is based on the echanis of natural selection and natural genetic search algorith, which is an effective way to solve the optiization proble. Using genetic algorith for solving optiization probles, we first need to select soe individuals in the feasible don is encoded as a chroosoe to for the first-generation groups, and calculate the fitness of each chroosoe. Next, the algorith uses the selection echanis randoly selected fro the group of chroosoes as the saples before reproductive process. The probability of a chroosoe being selected is proportional to its fitness. In the next reproduction process, Genetic algorith uses crossover and utation operator to the selection of chroosoes after the reproduction processing. Genetic algorith does not require QoS cobination operations (also including the objective function and constrnts) have linear characteristics. This akes our approach ore suitable for (even user-defined) QoS attributes, without the need for linearization. In the QoS-sensitive Web service coposition probles, the chroosoes described as an integer array of S = (S, S 2,... S n ). The S i array corresponds to each eleent of business processes a sub-task. Each eleent contns an index, the index pointing to the eleents in a group with the sae or siilar functionality as Web services in the selected Web services. 4. Dync fitness function According to characteristics of QoS-sensitive Web service coposition proble, the fitness function of chroosoes requires soe QoS attribute value to the axiu, while leaving other parts of the QoS property value of the iniu. Siilarly, if the user-defined QoS properties of a specific area, then the fitness function would also like to ake further adjustents under the circustances. In addition, the fitness function should be able to suppress the chroosoe does not eet the constrnts to guide the direction of the to evolve to eet the constrnts, as well as violations of the different QoS constrnts given different weights: Vl i (g) 0, i =,2...n () Where represents the violation of section i of constrnts on chroosoes brought about the penalty value of fitness. D (g) is defined as: the ipact of current chroosoe breach of the various types of constrnt

4 896 roceedings of the 7th International Conference on Innovation & Manageent n D(g) = Vl i (g)y (2) i = In this forula, if chroosoe violates the constrnt, then y i is, otherwise y i is 0. Because different etrics of its range is usually not the sae. Values are often differences in the direction, if the raw etric data directly to calculate, could ake between the results are not coparable. Therefore, the need for data pre-processing, apping the all to the sae range, and a unified direction for all etrics values. For exaple, etrics M's range is [in, ax], an instance of the raw easureent data is v, If the etrics values upward direction, that is, the greater the value of v, sd the better the quality of service(such as Web service's credibility), then according to the forula (3) to be noralized it v - in,in ax - in V' = (3), v ax 0, v in Conversely, if the etrics values downward direction, that is, the saller the value of v, sd the better the quality of services(such as the Web service operation of the response tie and delay tie), then according to the forula (4) to be noralized it. ax - v,in v ax ax - in V' = (4), v in 0, v ax After standardized processing, with all etrics of the data fall into [0,] interval, and the direction of their values are upward. These values, in the QOS ontology odel presented in this paper have a corresponding classes and attributes to represent According to coon QoS properties in the Table,the fitness function is defined as follows: A(g) + 2R(g) F(g) = + 5 D (5) 3C(g) + 4 T(g) In this forula,,... 5 are positive real nuber, as a paraeter.,... 4 on behalf of the user for a specific QoS attributes to set the weights, 5 is the overall weight of violating the constrnts. Usually, if a penalty factor with a high weight, In the evolutionary process, could ake chroosoe which close to the optial solution but violate the constrnt discarded in the early evolution. To address this issue, this paper presents dync punishent strategies: the penalty weight increases along with the descendant nuber increases. To ake such a chroosoe could be retned, through several generations of evolution; the entire group will eet constrnts. Therefore, dync fitness function can be defined as: F(g) = A(g) C(g) R(g) T(g) 4 i 5 v + D(g) ax gen gen Where gen represents Current evolutionary ties, gen ax represents Maxiu nuber of evolutionary 4.2 Terination conditions of genetic algoriths Specify a axiu nuber of evolutions are a ore efficient way. Therefore, in QoS-sensitive issue of Web service coposition, using the terination condition as follows: set the axiu nuber of offspring gen ax, iterate until the constrnt (D (g) = 0) have been satisfied. If have already reached the axiu nuber of generations, but the constrnts have not been et. Then through the algorith to deterine, we think that there is no corresponding solution. Or until the optiization of the chroosoe in the offspring is not changed so far. ax (6)

5 roceedings of the 7th International Conference on Innovation & Manageent 897 In this way, QoS-sensitive Web service coposition proble can be described by eans of chroosoes. And through repeated chroosoe selection, crossover and utation operators to continuously optiize the results; until the terination condition of the algorith is true, we can get the optial solution of Web service coposition. 5 Experient This section will be through experients analyze and validate the effectiveness of the ethod proposed in this paper. And through analysis of the offspring produced by genetic algorith is to observe the changes in chroosoe fitness. Thus Coparison of static and dync fitness functions. We use elite genetic algorith, in which each evolved to retn only the best two chroosoes. Crossover probability 0.7, utation probability of 0.0, population size of 00, the selection echanis used switch wheel ethod. The ipleentation of genetic algoriths for each experient 50 ties and record the average of the results. In order to observe the different fitness function, to require the evolution to achieve preset nuber of ties, if the descendant has not iproved, then stops the algorith. Business processes used in the experient included 25 service calls, of which there are 6 different tasks, For each task. Were observed when the nuber of optional web services are 5,0,5,20,25 situation at the tie Figure 2 shows, the constrnt is to require the cost and service response tie to reach the optiu, the figure shows the genetic algorith can find solutions that satisfy the constrnt. Figure 2 Coparison of Different Fitness Functions for Different araeter 6 Conclusion This paper presents a ethod based on genetic algoriths to solve the QoS-sensitive Web service coposition proble. Through the use of genetic algoriths for business processes in the task select the appropriate Web services, and binding, So that business processes can not only satisfy the constrnts also ake specified QoS attribute to achieve optial. Experiental data is to prove the effectiveness of this ethod. Meanwhile because of genetic algorith is not subject to linear constrnts, therefore has a better universality and the extendibility. resented in this paper using dync fitness function is to reduce the influence of Constrnts to fine chroosoe, increasing the copetitiveness of fine chroosoes. In addition, the proposed QOS ontology odel enables QOS attributes to get a unified description. In future work, will further optiize the fitness function; and consider the adoption of genetic algorith to solve the autoatic generation of eta-process in seantic web service coposition process.

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