Conceptual model of web service reputation
ACM SIGMOD Record
QoS computation and policing in dynamic web service selection
Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Toward autonomic web services trust and selection
Proceedings of the 2nd international conference on Service oriented computing
Flexible and Efficient Matchmaking and Ranking in Service Directories
ICWS '05 Proceedings of the IEEE International Conference on Web Services
Reliable QoS monitoring based on client feedback
Proceedings of the 16th international conference on World Wide Web
Transparent Runtime Adaptability for BPEL Processes
ICSOC '08 Proceedings of the 6th International Conference on Service-Oriented Computing
Context-Driven Autonomic Adaptation of SLA
ICSOC '08 Proceedings of the 6th International Conference on Service-Oriented Computing
Towards Correctness Assurance in Adaptive Service-Based Applications
ServiceWave '08 Proceedings of the 1st European Conference on Towards a Service-Based Internet
Contract-based cloud architecture
CloudDB '10 Proceedings of the second international workshop on Cloud data management
Adaptation of service-based systems
Service research challenges and solutions for the future internet
LFTM, linguistic fuzzy trust mechanism for distributed networks
Concurrency and Computation: Practice & Experience
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Service-oriented computing promotes the construction of applications by composing distributed services that are advertised in an open service market. In such an environment, individual services may change and evolve dynamically, requiring composite services to adapt to such changes. The prevailing strategy is to react on failures and replace the defective component of the composite service. However, this reactive approach does not fully exploit the opportunities of a dynamic market where older services may be replaced by better ones.In this paper we promote a novel architecture for automated, dynamic, pro-active, and transparent maintenance and improvement of composite services. We leverage fine-grained client-side monitoring techniques to generate information regarding functional and non-functional properties of service behavior. A reputation manager is responsible for collecting and aggregating this information, and provides economical incentives for honest sharing of feedback. Composite services can thus use reliable reputation information to pro-actively improve their aggregate performance.