Generic-distributed framework for cloud services marketplace based on unified ontology

• Published in: Journal of advanced research Vol. 8; no. 6; pp. 569 - 576
• Main Authors: Hasan, Samer, Valli Kumari, V.
• Format: Journal Article
• Language: English
• Published: Egypt Elsevier B.V 01.11.2017; Elsevier
• Subjects: Cloud marketplace; Cloud service; Ontology; Original; Semantic similarity; Service discovery; Cloud service; Cloud marketplace; Semantic similarity; Service discovery; Ontology
• ISSN: 2090-1232; 2090-1224
• DOI: 10.1016/j.jare.2017.07.003

[Display omitted] Cloud computing is a pattern for delivering ubiquitous and on demand computing resources based on pay-as-you-use financial model. Typically, cloud providers advertise cloud service descriptions in various formats on the Internet. On the other hand, cloud consumers use available search engines (Google and Yahoo) to explore cloud service descriptions and find the adequate service. Unfortunately, general purpose search engines are not designed to provide a small and complete set of results, which makes the process a big challenge. This paper presents a generic-distrusted framework for cloud services marketplace to automate cloud services discovery and selection process, and remove the barriers between service providers and consumers. Additionally, this work implements two instances of generic framework by adopting two different matching algorithms; namely dominant and recessive attributes algorithm borrowed from gene science and semantic similarity algorithm based on unified cloud service ontology. Finally, this paper presents unified cloud services ontology and models the real-life cloud services according to the proposed ontology. To the best of the authors’ knowledge, this is the first attempt to build a cloud services marketplace where cloud providers and cloud consumers can trend cloud services as utilities. In comparison with existing work, semantic approach reduced the execution time by 20% and maintained the same values for all other parameters. On the other hand, dominant and recessive attributes approach reduced the execution time by 57% but showed lower value for recall.