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ABSTRACT: With increasing presence and adoption of Web services on the World Wide Web, Quality-of-Service (QoS) is becoming important for describing nonfunctional characteristics of Web services. In this paper, we present a collaborative filtering approach for predicting QoS values of Web services and making Web service recommendation by taking advantages of past usage experiences of service users. We first propose a user-collaborative mechanism for past Web service QoS information collection from different service users. Then, based on the collected QoS data, a collaborative filtering approach is designed to predict Web service QoS values. Finally, a prototype called WSRec is implemented by Java language and deployed to the Internet for conducting real-world experiments. To study the QoS value prediction accuracy of our approach, 1.5 millions Web service invocation results are collected from 150 service users in 24 countries on 100 real-world Web services in 22 countries. The experimental results show that our algorithm achieves better prediction accuracy than other approaches. Our Web service QoS data set is publicly released for future research.
IEEE Transactions on Services Computing 07/2011; · 1.47 Impact Factor
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IEEE International Conference on Cloud Computing, CLOUD 2011, Washington, DC, USA, 4-9 July, 2011; 01/2011
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IEEE 22nd International Symposium on Software Reliability Engineering, ISSRE 2011, Hiroshima, Japan, November 29 - December 2, 2011; 01/2011
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30th IEEE Symposium on Reliable Distributed Systems (SRDS 2011), Madrid, Spain, October 4-7, 2011; 01/2011
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IEEE International Conference on Web Services, ICWS 2011, Washington, DC, USA, July 4-9, 2011; 01/2011
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IEEE International Conference on Cloud Computing, CLOUD 2011, Washington, DC, USA, 4-9 July, 2011; 01/2011
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IEEE T. Services Computing. 01/2011; 4:140-152.
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IEEE International Conference on Cloud Computing, CLOUD 2011, Washington, DC, USA, 4-9 July, 2011; 01/2011
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ABSTRACT: Cloud computing is becoming a mainstream aspect of information technology. The cloud applications are usually large-scale, complex, and include a lot of distributed components. Providing highly reliable cloud applications is a challenging and critical research problem. To attack this challenge, we propose FTCloud which is a component ranking based framework for building fault-tolerant cloud applications. FTCloud employs the component invocation structures and the invocation frequencies to identify the significant components in a cloud application. An algorithm is proposed to automatically determine optimal fault tolerance strategy for these significant components. The experimental results show that by tolerating faults of a small part of the most significant components, the reliability of cloud application can be greatly improved.
Software Reliability Engineering (ISSRE), 2010 IEEE 21st International Symposium on; 12/2010
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ABSTRACT: The rising popularity of cloud computing makes building high quality cloud applications a critical and urgently required research problem. Component quality ranking approaches are crucial for making optimal component selection from a set of functionally equivalent component candidates. Moreover, quality ranking of cloud components helps the application designers detect the poor performing components in the complex cloud applications, which usually include huge number of distributed components. To provide personalized cloud component ranking for different designers of cloud applications, this paper proposes a QoS-driven component ranking framework for cloud applications by taking advantage of the past component usage experiences of different component users. Our approach requires no additional invocations of the cloud components on behalf of the application designers. The extensive experimental results show that our approach outperforms the competing approaches.
Reliable Distributed Systems, 2010 29th IEEE Symposium on; 12/2010
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ABSTRACT: Quality-of-Service (QoS) is widely employed for describing non-functional characteristics of Web services. Although QoS of Web services has been investigated in a lot of previous works, there is a lack of real-world Web service QoS datasets for validating new QoS based techniques and models of Web services. To study the performance of real-world Web services as well as provide reusable research datasets for promoting the research of QoS-driven Web services, we conduct several large-scale evaluations on real-world Web services. Firstly, addresses of 21,358 Web services are obtained from the Internet. Then, invocation failure probability performance of 150 Web services is assessed by 100 distributed service users. After that, response time and throughput performance of 5,825 Web services are evaluated by 339 distributed service users. Detailed experimental results are presented in this paper and comprehensive Web service QoS datasets are publicly released for future research.
Web Services (ICWS), 2010 IEEE International Conference on; 08/2010
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IEEE 21st International Symposium on Software Reliability Engineering, ISSRE 2010, San Jose, CA, USA, 1-4 November 2010; 01/2010
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29th IEEE Symposium on Reliable Distributed Systems (SRDS 2010), New Delhi, Punjab, India, October 31 - November 3, 2010; 01/2010
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Int. J. Web Service Res. 01/2010; 7:21-40.
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IEEE International Conference on Web Services, ICWS 2010, Miami, Florida, USA, July 5-10, 2010; 01/2010
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IEEE International Conference on Web Services, ICWS 2010, Miami, Florida, USA, July 5-10, 2010; 01/2010
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Empirical Software Engineering. 01/2010; 15:323-345.
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Proceedings of the 32nd ACM/IEEE International Conference on Software Engineering - Volume 1, ICSE 2010, Cape Town, South Africa, 1-8 May 2010; 01/2010
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Proceedings of the 2009 IEEE/IFIP International Conference on Dependable Systems and Networks, DSN 2009, Estoril, Lisbon, Portugal, June 29 - July 2, 2009; 01/2009
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IEEE International Conference on Web Services, ICWS 2009, Los Angeles, CA, USA, 6-10 July 2009; 01/2009
