Web Service adaptation and evolution is receiving huge interest in the service oriented architecture community due to dynamic and volatile web service environment. Regarding quality of service changes, Web Services need to be able to adapt dynamically to respond to such changes. However, formulating quality of service parameters and their relationship with adaptation behaviour of a service based system is a difficult task. In this paper, a Fuzzy Inference System (FIS) is adopted for capturing overall QoS and selecting adaptation strategies using fuzzy rules. The overall QoS is inferred by QoS parameters, while selection of adaptation strategies is inferred by the overall QoS, importance of QoS and cost of service substitution. In particular, hierarchical fuzzy systems were used to reduce the number of rules. Our approach is able to efficiently select adaptation strategies with respect to QoS changes. We test and compare our fuzzy adaptation with a naive adaptation approach that works based on precise measurement of QoS in order to show the performance of the approach in reducing the number of service substitutions and adaptation cost.
Selection of Service Adaptation Strategies Based on Fuzzy Logic
PERNICI, BARBARA;SIADAT, SEYED HOSSEIN
2011-01-01
Abstract
Web Service adaptation and evolution is receiving huge interest in the service oriented architecture community due to dynamic and volatile web service environment. Regarding quality of service changes, Web Services need to be able to adapt dynamically to respond to such changes. However, formulating quality of service parameters and their relationship with adaptation behaviour of a service based system is a difficult task. In this paper, a Fuzzy Inference System (FIS) is adopted for capturing overall QoS and selecting adaptation strategies using fuzzy rules. The overall QoS is inferred by QoS parameters, while selection of adaptation strategies is inferred by the overall QoS, importance of QoS and cost of service substitution. In particular, hierarchical fuzzy systems were used to reduce the number of rules. Our approach is able to efficiently select adaptation strategies with respect to QoS changes. We test and compare our fuzzy adaptation with a naive adaptation approach that works based on precise measurement of QoS in order to show the performance of the approach in reducing the number of service substitutions and adaptation cost.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
