Automatic Service Search and Composability Analysis in Large Scale Service Networks

Currently, software and hardware system components are trending toward modularized and virtualized as atomic services on the cloud. A number of cloud platforms or marketplaces are available where everybody can provide their system components as services. In this situation, service composition is essential, because the functionalities offered by a single atomic service might not satisfy users‘ complex requirements. Since there are already a large number of available services and significant increase in the number of new services over time, manual service composition is impractical.

In our research, we propose computer-aided methods to help find and compose appropriate services to fulfill users‘ requirement in large scale service networks. For this purpose, we explore the following methods. First, we develop a method for formally representing a service in term of composability by considering various functional and non-functional characteristics of services. Second, we develop a method for aiding the development of the reference ontologies that are crucial for representing a service. We explore a bottom-up-based statistical method for the ontology development. Third, we architect a framework that encompasses the reference models, effective strategy, and necessary procedures for the services search and composition. Finally, we develop a graph-based algorithm that is highly specialized for services search and composition. Experimental comparative performance analysis against existing automatic services composition methods is also provided.

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University of Maryland, Baltimore County

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