Towards an efficient federated cloud service selection to support workflow big data requirements

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Cloud Computing is considered nowadays an attractive solution to serve the Big Data storage, processing, and analytics needs. Given the high complexity of Big Data workflows and their contingent requirements, a single cloud provider might not be able alone to satisfy these needs. A multitude of cloud providers that offer myriad of cloud services and resources can be selected. However, such selection is not straightforward since it has to deal with the scaling of Big Data requirements, and the dynamic cloud resources fluctuation. This work proposes a novel cloud service selection approach which evaluates Big Data requirements, matches them in real time to most suitable cloud services, after which suggests the best matching services satisfying various Big Data processing requests. Our proposed selection scheme is performed throughout three phases: 1) capture Big Data workflow requirements using a Big Data task profile and map these to a set of QoS attributes, and prioritize cloud service providers (CSPs) that best fulfil these requirements, 2) rely on the pool of selected providers by phase 1 to then choose the suitable cloud services from a single provider to satisfy the Big Data task requirements, and 3) implement multiple providers selection to better satisfy requirements of Big Data workflow composed of multiples tasks. To cope with the multi-criteria selection problem, we extended the Analytic Hierarchy Process (AHP) to better provide more accurate rankings. We develop a set of experimental scenarios to evaluate our 3-phase selection schemes while verifying key properties such as scalability and selection accuracy. We also compared our selection approach to well-known selection schemes in the literature. The obtained results demonstrate that our approach perform very well compared to the other approaches and efficiently select the most suitable cloud services that guarantee Big Data tasks and workflow QoS requirements.

Original languageEnglish
Pages (from-to)235-247
Number of pages13
JournalAdvances in Science, Technology and Engineering Systems
Volume3
Issue number5
DOIs
Publication statusPublished - 2018

Keywords

  • Big data
  • Cloud
  • QoS
  • Service selection
  • Workflow

ASJC Scopus subject areas

  • Engineering (miscellaneous)
  • Physics and Astronomy (miscellaneous)
  • Management of Technology and Innovation

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