Abstract :
Service providers as we know them nowadays are the always-on static web service providers, that aim at Five9 availability (99.999%). Formal, or de-facto, standards, such as WSDL and BPEL, have become technology enablers for the easy discovery, use and coordination of such services. However, we envisage tomorrow´s services to become increasingly pervasive, being deployed within buildings, transport systems, markets, as well as people portable devices. Such services will be, by their own nature, simple and fine grained; as a consequence, service composition will become crucial to deliver rich functionalities that satisfy end-users requests. Composing services in mob.ile environments opens up significant challenges. In particular, the Five9 availability assumption no longer holds: the higher the dynamic nature of the environment, the higher the chances that services will move out-of-reach before the composition completes, causing the service as a whole to fail. We argue that, in order to enable the successful completion of compound services, the reliability of the composition must be measured and reasoned about. In order to do so, we propose to dynamically deploy a prediction model to estimate the duration of colocation between componentservices. These estimates are fed in input to a service composition semantics reasoner, which then autonomically selects those providers, within the current environment, that maximise the chances of successful compound service completion. We demonstrate the positive impact that the reliability reasoning has onto the ratio of successfully completed compound services in a typical human movement scenario.
Keywords :
Web services; mobile computing; Five9 availability assumption; component services; composite services; mobile environment; portable devices; prediction model; reliable discovery; reliable selection; service composition semantics reasoner; static Web service providers; Advertising; Availability; Bandwidth; Computer science; Context-aware services; Distributed computing; Educational institutions; Predictive models; Space technology; Web and internet services;