Abstract
Cloud computing is not only referred as synonym of on-demand usage of computing resources and services, but as the most promising paradigm to provide infinite scalability by using virtual infrastructures. In the other hand mobile technologies are scaling up to encompass every day a growing number of real and virtual objects in order to provide large-scale data applications, e.g. sensor-based intelligent communications networks, smart grid computing applications, etc. In those complex scenarios, cloud-based computing systems need to cope with diverse service demands in order to enable dynamic composition based on particular user’s demands, variations in collected data broadband, fluctuation of data quality and to satisfy ad-hoc usage for personalized applications. Thus essential characteristics from cloud-native systems i.e. elasticity and multi-tenancy are fundamental requirements into large-scale data processing systems. In this paper we have investigated common practices on information sharing and domain ontological modelling to enable service composition of cloud computing service provisioning. This approach exploits the potential of semantic models in supporting service and application linkage by studying links between the complementary services. By using semantic modelling and knowledge engineering we can enable the composition of services. We discuss what implications this approach imposes on architectural design terms and also how virtual infrastructures and cloud-based systems can benefit from this ontological modelling approach. Research results about information sharing and information modelling by using semantic annotations are discussed. An introductory application scenario is depicted.
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Serrano, M., Shi, L., Foghlú, M.Ó., Donnelly, W. (2013). Cloud Services Composition Support by Using Semantic Annotation and Linked Data. In: Fred, A., Dietz, J.L.G., Liu, K., Filipe, J. (eds) Knowledge Discovery, Knowledge Engineering and Knowledge Management. IC3K 2011. Communications in Computer and Information Science, vol 348. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37186-8_18
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