A Novel Methodology for Capitalizing on Cloud Storage through a Big Data-as-a-Service Framework

The Big Data-as-a-Service (BDaaS) framework exploits the elastic scalability and analytical data processing capabilities delivered via the cloud, minimizing the complexity and capital expense of on-premises data infrastructure. Since the cloud can be considered as a marketplace, small and large enterprises lease storage and computing capacity based on a negotiated cost approach. In this context, this research work examines a novel methodology for capitalizing earnings on cloud storage level through a big data-as-a-service framework and proposes cloud- inspired quantitative cost and benefits analysis models under the assumption that the demand curves are linear. The proposed modelling approach is evaluated against the conventional high- performance data warehouse appliances on the necessity of possible upgradation of the storage.