Existing methods used for service discovery assume that the world is static, con- sidering a predetermined set of attributes. As a result, current discovery techniques return many results that are irrelevant. Our approach to high quality service dis- covery improves the results’ relevancy by considering dynamic attributes with values changing over time. Using this approach, we reveal structure from the data to satisfy the consumers’ experiences. Web service quality is a set of dynamic attributes used to rank services with similar functionalities. When picking a service to execute financial transactions effi- ciently, we might consider availability, reliability, response time, and transaction cost as quality indicators. Supporting dynamic attributes is a feature critical to providing exceptional quality service discovery. In addition, effective service discovery requires detailed context models that describe both static and dynamic features. The context takes into consideration the situation of the service, the operating environment, the users’ circumstances, and their preferences. For instance, latency is an important issue in stock trading services with direct impact on revenue. One of the main challenges in enabling dynamic service discovery is developing techniques and models to handle the novel aspects of the web service paradigm. This challenge leads to a variety of research questions related to measuring, monitoring, or querying of dynamic attributes, while guaranteeing integrity and validity. We outline an architecture framework called Static Discovery Dynamic Selection (SDDS) to gather and manage dynamic attributes considering both context and do- main information at discovery time—augmenting static mechanisms. The architec- ture of SDDS defines individual components that collectively satisfy flexible and ac- curate service selection with a robust resource management approach capable of con- sidering high-frequency data. Moreover, we devised a multi-criteria decision making algorithm that considers the knowledge domain and the user context, and accordingly, the algorithm returns a small set of accurate and reliable results. As part of the SDDS framework, autonomic computing adds self-adaptability by taking highly dynamic context information into account. The impact of our method is demonstrated in an implementation of the model. We demonstrate that increasing the adaptability of the web service discovery by including context information provides a noticeable reduction in the number of results returned compared to static web service discovery methods. We extend the proposed infrastructure to ascertain whether a particular service satisfies, at execution time, specific security properties. We introduce the notion of certified web service assurance, characterizing how consumers of the service can specify the set of security properties that a service should satisfy. In addition, we illustrate a mechanism to re-check security properties when the execution context changes. To this end, we introduce the concept of a context-aware certificate and describe a dynamic, context-aware service discovery environment. / Graduate