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dc.date.accessioned2015-04-28T07:48:17Z
dc.date.available2015-04-28T07:48:17Z
dc.date.issued2015-04-28
dc.identifier.uriurn:nbn:de:hebis:34-2015042848197
dc.identifier.urihttp://hdl.handle.net/123456789/2015042848197
dc.language.isoeng
dc.subjectWeb Serviceeng
dc.subjectWeb Service Compositionseng
dc.subjectQuality of Serviceeng
dc.subjectBPELeng
dc.subjectService Selectioneng
dc.subjectContext-Awarenesseng
dc.subjectService Recommendationeng
dc.subjectBusiness Processeng
dc.subjectManagement Frameworkeng
dc.subject.ddc004
dc.titleManaging Quality Properties of Web Service Compositionseng
dc.typeDissertation
dcterms.abstractWeb services from different partners can be combined to applications that realize a more complex business goal. Such applications built as Web service compositions define how interactions between Web services take place in order to implement the business logic. Web service compositions not only have to provide the desired functionality but also have to comply with certain Quality of Service (QoS) levels. Maximizing the users' satisfaction, also reflected as Quality of Experience (QoE), is a primary goal to be achieved in a Service-Oriented Architecture (SOA). Unfortunately, in a dynamic environment like SOA unforeseen situations might appear like services not being available or not responding in the desired time frame. In such situations, appropriate actions need to be triggered in order to avoid the violation of QoS and QoE constraints. In this thesis, proper solutions are developed to manage Web services and Web service compositions with regard to QoS and QoE requirements. The Business Process Rules Language (BPRules) was developed to manage Web service compositions when undesired QoS or QoE values are detected. BPRules provides a rich set of management actions that may be triggered for controlling the service composition and for improving its quality behavior. Regarding the quality properties, BPRules allows to distinguish between the QoS values as they are promised by the service providers, QoE values that were assigned by end-users, the monitored QoS as measured by our BPR framework, and the predicted QoS and QoE values. BPRules facilitates the specification of certain user groups characterized by different context properties and allows triggering a personalized, context-aware service selection tailored for the specified user groups. In a service market where a multitude of services with the same functionality and different quality values are available, the right services need to be selected for realizing the service composition. We developed new and efficient heuristic algorithms that are applied to choose high quality services for the composition. BPRules offers the possibility to integrate multiple service selection algorithms. The selection algorithms are applicable also for non-linear objective functions and constraints. The BPR framework includes new approaches for context-aware service selection and quality property predictions. We consider the location information of users and services as context dimension for the prediction of response time and throughput. The BPR framework combines all new features and contributions to a comprehensive management solution. Furthermore, it facilitates flexible monitoring of QoS properties without having to modify the description of the service composition. We show how the different modules of the BPR framework work together in order to execute the management rules. We evaluate how our selection algorithms outperform a genetic algorithm from related research. The evaluation reveals how context data can be used for a personalized prediction of response time and throughput.eng
dcterms.accessRightsopen access
dcterms.creatorReichle, Diana-Elena
dc.contributor.corporatenameKassel, Universität Kassel, Fachbereich Elektrotechnik / Informatik
dc.contributor.refereeGeihs, Kurt (Prof. Dr.)
dc.contributor.refereeKönig-Ries, Birgitta (Prof. Dr.)
dc.contributor.refereeZündorf, Albert (Prof. Dr.)
dc.contributor.refereeWacker, Arno (Prof. Dr.)
dc.subject.ccsC.2.4 Distributed Systems
dc.subject.ccsG.1.6 Optimization - Global Optimization
dc.subject.ccsG.3 PROBABILITY AND STATISTICS - Correlation and regression analysis
dc.subject.ccsD.4.8 Performance - Monitors
dc.subject.ccsD.4.8 Performance - Modeling and prediction
dc.subject.mscLinear Regressioneng
dc.subject.swdWeb Servicesger
dc.subject.swdDienstekompositionger
dc.subject.swdDienstgüteger
dc.subject.swdBusiness Process Execution Languageger
dc.subject.swdProzessmanagementger
dc.subject.swdKontextbezogenes Systemger
dc.date.examination2015-02-09


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