Collaborative reliability prediction of service-oriented systems

Service-oriented architecture (SOA) is becoming a major software framework for building complex distributed systems. Reliability of the service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet. Designing effective and accurate reliability prediction a...

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Veröffentlicht in:2010 ACM/IEEE 32nd International Conference on Software Engineering Jg. 1; S. 35 - 44
Hauptverfasser: Zheng, Zibin, Lyu, Michael R.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 01.05.2010
IEEE
Schriftenreihe:ACM Conferences
Schlagworte:
Collaboration
Equations
General and reference > Cross-computing tools and techniques > Reliability
Probability
reliability composition
Reliability engineering
reliability prediction
Software and its engineering > Software organization and properties > Extra-functional properties > Software reliability
Software reliability
user-collaboration
web service
Web services
reliability prediction
reliability composition
web service
user-collaboration
ISBN:9781605587196, 1605587192
ISSN:0270-5257
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Service-oriented architecture (SOA) is becoming a major software framework for building complex distributed systems. Reliability of the service-oriented systems heavily depends on the remote Web services as well as the unpredictable Internet. Designing effective and accurate reliability prediction approaches for the service-oriented systems has become an important research issue. In this paper, we propose a collaborative reliability prediction approach, which employs the past failure data of other similar users to predict the Web service reliability for the current user, without requiring real-world Web service invocations. We also present a user-collaborative failure data sharing mechanism and a reliability composition model for the service-oriented systems. Large-scale real-world experiments are conducted and the experimental results show that our collaborative reliability prediction approach obtains better reliability prediction accuracy than other approaches.
ISBN:9781605587196
1605587192
ISSN:0270-5257
DOI:10.1145/1806799.1806809