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A Cloud Service Broker for Cost Effective Infrastructure Selection using Multiple Deployment Options

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2020
Raphael Gomes, Geovany Rodrigues, Gilberto Lobo

Raphael Gomes, Geovany Rodrigues and Gilberto Lobo. A Cloud Service Broker for Cost Effective Infrastructure Selection using Multiple Deployment Options. International Journal of Computer Applications 175(32):1-8, November 2020. BibTeX

	author = {Raphael Gomes and Geovany Rodrigues and Gilberto Lobo},
	title = {A Cloud Service Broker for Cost Effective Infrastructure Selection using Multiple Deployment Options},
	journal = {International Journal of Computer Applications},
	issue_date = {November 2020},
	volume = {175},
	number = {32},
	month = {Nov},
	year = {2020},
	issn = {0975-8887},
	pages = {1-8},
	numpages = {8},
	url = {},
	doi = {10.5120/ijca2020920874},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


The multiplicity of cloud service providers and the wide variety of resources types and regions makes selecting services a challenging task, which becomes even more complex when considering different cloud deployment models to meet the applications’ specifics that will use these resources. For this, among the criteria used in selecting cloud resources, the cost is rated one of the most essential. Given this, this paper presents a cloud service broker’s design and implementation for resource selection, taking into account different options, including the variation of cloud service providers, regions, and cloud deployment models. The proposed tool is based on other contributions that are also described in this work: 1) the design and construction of an ontology with concepts on the representation of computing resources, with associated reasoning processes; and 2) an on-premises infrastructure cost estimation strategy using a Total Cost of Ownership analysis. A qualitative evaluation considering productivity and accuracy is also presented, demonstrating the advantages of the proposed tool over other existing options.


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Cloud service selection, cost optimization, deployment models, ontology, TCO