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Yu Dai, Lei Yang, Bin Zhang. QoS-Driven Self-Healing Web Service Composition Based on Performance Prediction[J]. Journal of Computer Science and Technology, 2009, 24(2): 250-261.
Citation: Yu Dai, Lei Yang, Bin Zhang. QoS-Driven Self-Healing Web Service Composition Based on Performance Prediction[J]. Journal of Computer Science and Technology, 2009, 24(2): 250-261.

QoS-Driven Self-Healing Web Service Composition Based on Performance Prediction

Funds: This work is supported by the National Natural Science Foundation of China under Grant No. 60773218.
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  • Author Bio:

    Yu Dai receives her Ph.D. degree from the College of InformationScience and Technology at Northeastern University, Shenyang, China in2008. She is a lecturer of college of software, Northeastern University.Her main research interests include service composition andservice oriented computing.

    Lei Yang is a lecturer in the College of Information Science andTechnology at Northeastern University, Shenyang, China. He received hisPh.D. degree from Northeastern University in 2007. His researchinterests include service composition and service oriented computing.

    Bin Zhang is a professor in the College of Information Scienceand Technology at Northeastern University, Shenyang, China. He is amember of CCF. He received his Ph.D. degree from NortheasternUniversity in 1997. His current research interests are serviceoriented computing and information retrieval.

  • Received Date: March 16, 2008
  • Revised Date: November 26, 2008
  • Published Date: March 04, 2009
  • Web services run in a highly dynamic environment, as a result, the QoS of which will change relatively frequently. In order to make the composite service adapt to such dynamic property of Web services, we propose a self-healing approach for web service composition. Such an approach is an integration of backing up in selection and reselecting in execution. In order to make the composite service heal itself as quickly as possible and minimize the number of reselections, a way of performance prediction is proposed in this paper. On this basis, the self-healing approach is presented including framework, the triggering algorithm of the reselection and the reliability model of the service. Experiments show that the proposed solutions have better performance in supporting the self-healing Web service composition.
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