Towards the evaluation of a big data-as-a-service model: A decision theoretic approach

Abstract

The rise of large data centers has created new business models, where businesses can lease storage and computing capacity and pay only for the storage they actually use, rather than making the large capital investments needed to construct and provision large-scale computer installations. In this context, investments in big-data computing are rapidly gaining ground, having extraordinary near-term and long-term benefits. The mobile cloud can be considered as a marketplace, where the storage and computing capabilities of the mobile cloud-based system architectures can be leased off. However, cloud storage is not less expensive, only that it incurs operating rather than capital expenses. This paper elaborates on a novel cost analysis model, adopting a non-linear and asymmetric approach. The proposed modelling aims to evaluate the adoption of a big data-as-a-service business model against the traditional high-performance data warehouse appliances that exist in the market in order to inform effective and strategic decision making. The lease of cloud storage is investigated, when developing the mathematical formulas, and the research approach is examined with respect to the cost that derives from the unused storage. Possible upgradation of the storage and the risk of entering into new and accumulated costs in the future are also considered in this study. A quantification tool has been also developed as a proof of concept (PoC), implementing the proposed quantitative model and intending to shed light on the adoption of big data-as-a-service business models.