Optimizing Web Service Composition in Parallel

Transcription

1 , pp Optimizing Web Service Composition in arallel Chang Li, Dongjin Yu, Yuyu Yin, Youwei Yuan and Wanqing Li School of Computer, Hangzhou Dianzi University, Hangzhou, China {yudj, yinyuyu, yyw, Abstract. Traditional functional-centric Web services cannot meet the requirements of data processing which merely provide the functionality required by users. Data-intensive Web services focus on providing and updating data with large amounts of data operation and exchange. In this paper, we put forward the parallel schema for data-intensive Web services and describe the method used to determine the degree of parallelism (DO) to balance the cost and throughput. The experiment results demonstrate that our proposed parallel schema for data-intensive Web services could effectively improve the efficiency of the composite Web service. Keywords: data-intensive Web services, degree of parallelism, parallel, Web service composition 1 Introduction As service-oriented computing and Web service achieved success in encapsulation and data integration, the academia and industry have started with adopting the Web service and SOA (Service Oriented Architecture) to achieve data management on the Internet, which promotes the data-intensive applications development, operation and management [1][2][3]. However, the traditional functional centric Web services can t meet the requirement of data processing with merely providing the functionality required by the users, which makes the Web services that support data-intensive application become a new form of Web service. Such Web services focus on providing and updating data with large amount of data operation and exchange, which is referred to data-intensive Web service. Efficient parallel and concurrent technology is crucial in the analysis of large-scale data [4]. It means a lot by introducing the parallelism programming schema into composite services to deal with large-scale data. Moreover, the user usually concerns the quality of the composite service [5]. As a result, the composite service should also guarantee user the acceptable quality of the whole process. In the composite service, the client needs to invoke Web services provided by different supplier via the Internet and the Web services may locate in different area of the network, which makes the QoS problem receive extensive concern. Most of the current researches in QoS optimization mainly discuss how to select appropriate Web services in terms of non-functional properties and the quality of Web service [6]. ISSN: ASTL Copyright 2014 SERSC

2 To solve the above problems, in terms of large-scale data, we deduce the calculating of the DO (Degree of arallelism), and propose an algorithm of obtain the DO. Our paper is organized as follows. Section 2 presents the case in detail. In section 3, we show how to determine an appropriate DO while considering the cost and throughput. After Section 4 presents the experiment results, last section concludes the paper and outlines our future work. 2 Case Description Fig.1 shows an example process with four basic structures applying where the circle represents the elementary services and the box encircles the nodes in parallel. The curving line represents the loop execution of service. As shown in Fig.1, service is deployed with replicas on multiple nodes and execute in parallel. Although parallel execution improves the efficiency of the composite service and users get the optimal response time, while the user have to also pay for the service replicas. In the user s perspective, the goal is the minimum cost and the maximum benefit. In other word, it s not necessarily the higher DO the better. Of course, if the DO is too low, the ability of dealing with large-scale data will be limited. Therefore, it s essential to conclude an appropriate DO. S2 S1 S5 S3 Fig. 1. Composite service includes multi-structures To be clear, we list the notations used in our paper and their definitions in Table I. Table 1. Related Symbols And Definition Notation C T Q C Q Q T C i T i i m Definition The highest fee users can pay The maximum throughput of users requirement The maximum response time of users requirement The total cost of the composite service The total throughput of the composite service The total response time of the composite service The price of Web service S i The response time of Web service S i The throughput of the Web service S i The number of tasks in the composite service Copyright 2014 SERSC 71

3 n The number of parallel services A A constant greater than 0 B A constant greater than 0 3 Optimizing Web Service Composition in arallel What we are going to do is to optimize the composite service in terms of cost and throughput for the case in Fig. 1. The compositional services have been selected and what we concern is the decision of DO. Finding a suitable DO is equal to solving the following constrained optimization problem: Q Max C Q T T Q C subject to C Q Q We can obtain the possible range where a suitable DO lie in:. In actual situation, we should also take another factor into consideration, the whole time specified by user, which also affects the DO. Although the practical situation may be different, our analysis mainly covers the general cases. The actual composite service can take our case as reference in the optimization. 4 Experiment And Result We develop the Microblog searching service composed by three elementary services as shown in Fig.2. The Data-preprocessing service does the parsing of the Microblog data. The Search-for-Microblog service searches for the Microblogs in terms of a given user Id. The Collect-the-results service will collect all the microblogs output from the Search-for-Microblog service. We take 250,000 microblog items published 72 Copyright 2014 SERSC

4 by Sina ( as testing data and measure the runtime of the whole process by varying the number of data nodes. Collect-theresults Search-for- Microblog Datapreprocessing Output Fig. 2. The composite service composed with elementary services According to our above algorithm, we derive the minimum value of n to be 1, the maximum value of n to be 6. Since the user specify the acceptable response time to be 400s, the minimum value of n must be greater than 1, namely 4 is appropriate. So the range of n is from 4 to 6. From the perspective of saving cost, n is better to be 4. Compared with the /C in Table 2, we conclude the result computed with our algorithm is approximate to the physical truth. Table 2. The value of DO and /C Number of nodes /C Conclusion In this paper, we put forward the parallel schema for data-intensive Web services. Afterwards, we consider the QoS optimization of the composite service based on the services in parallel. We establish the model to balance the throughput and cost. Applying this model on a composite service based on the parallel mode and deciding the DO to optimize the whole process to make the user obtain the optimal benefit, namely the most data processed in the lowest cost. As it s time-consuming when transferring data between services, our future work will include integrating specific transition tools. In addition, we will consider more property, such as reliability and availability. Acknowledgments. The work is supported by Natural Science Foundation of China (No ), Natural Science Foundation of Zhejiang (No.LY12F02003), the Key Science and Technology roject of Zhejiang (No. 2012C ) and the open project foundation of Zhejiang rovincial Key Laboratory of Network Technology and Information Security. The authors would also like to thank anonymous reviewers who made valuable suggestions to improve the quality of the paper. Copyright 2014 SERSC 73

5 References 1. Spiros Koulouzis, Reginald Cushing, Konstantinos A. Karasavvas, Adam Belloum, Marian Bubak: Enabling Web Services to Consume and roduce Large Datasets. IEEE Internet Computing, vol. 16, no. 1, pp (2012) 2. Murakami Y, Tanaka M, Bramantoro A, Zettsu K.: Data-Centered Service Composition for Information Analysis IEEE Ninth International Conference on Services Computing (SCC), pp (2012) 3. Tanaka M, Murakami Y, Zettsu K.: Data-Intensive Services for Large-Scale Archive Access. IEEE Ninth International Conference on Services Computing (SCC), pp (2012) 4. Fei X, Lu S, Lin C.: A MapReduce-enabled scientific workflow composition framework. IEEE International Conference on Web Services, pp (2009) 5. Yin Li, Chuang Lin: QoS-Aware Service Composition for Workflow-Based Data-Intensive Applications. IEEE International Conference on Web Services, pp (2011) 6. Qiqing F, Xiaoming, Qinghua L, Yahui H.: A global QoS optimizing web services selection algorithm based on MOACO for dynamic web service composition. International Forum on Information Technology and Applications, IFITA'09, pp (2009) 74 Copyright 2014 SERSC