A novel methodology for capitalizing on cloud storage through a big data-as-a-service framework

Georgios Skourletopoulos, Constandinos X. Mavromoustakis, Periklis Chatzimisios, George Mastorakis, Jordi Mongay Batalla

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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 cloudinspired quantitative cost and benefits analysis models under the assumption that the demand curves are linear. The proposed modelling approach is evaluated against the conventional highperformance data warehouse appliances on the necessity of possible upgradation of the storage.

Original languageEnglish
Title of host publication2016 IEEE Globecom Workshops, GC Wkshps 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509024827
DOIs
Publication statusPublished - 2016
Event2016 IEEE Globecom Workshops, GC Wkshps 2016 - Washington, United States
Duration: 4 Dec 20168 Dec 2016

Other

Other2016 IEEE Globecom Workshops, GC Wkshps 2016
Country/TerritoryUnited States
CityWashington
Period4/12/168/12/16

Keywords

  • Big Data-as-a-Service
  • Cloud storage
  • Cost-benefit analysis
  • Data warehouse
  • Linear modelling
  • Mobile cloud computing

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