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Green Cloud Software Engineering for Big Data Processing

Author

Listed:
  • Madhubala Ganesan

    (School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds LS1 3HE, UK)

  • Ah-Lian Kor

    (School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds LS1 3HE, UK)

  • Colin Pattinson

    (School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds LS1 3HE, UK)

  • Eric Rondeau

    (CRAN—Université de Lorraine, Campus Sciences, BP 70239 54506 Vandœuvre-lès-Nancy, France)

Abstract

Internet of Things (IoT) coupled with big data analytics is emerging as the core of smart and sustainable systems which bolsters economic, environmental and social sustainability. Cloud-based data centers provide high performance computing power to analyze voluminous IoT data to provide invaluable insights to support decision making. However, multifarious servers in data centers appear to be the black hole of superfluous energy consumption that contributes to 23% of the global carbon dioxide (CO 2 ) emissions in ICT (Information and Communication Technology) industry. IoT-related energy research focuses on low-power sensors and enhanced machine-to-machine communication performance. To date, cloud-based data centers still face energy–related challenges which are detrimental to the environment. Virtual machine (VM) consolidation is a well-known approach to affect energy-efficient cloud infrastructures. Although several research works demonstrate positive results for VM consolidation in simulated environments, there is a gap for investigations on real, physical cloud infrastructure for big data workloads. This research work addresses the gap of conducting real physical cloud infrastructure-based experiments. The primary goal of setting up a real physical cloud infrastructure is for the evaluation of dynamic VM consolidation approaches which include integrated algorithms from existing relevant research. An open source VM consolidation framework, Openstack NEAT is adopted and experiments are conducted on a Multi-node Openstack Cloud with Apache Spark as the big data platform. Open sourced Openstack has been deployed because it enables rapid innovation, and boosts scalability as well as resource utilization. Additionally, this research work investigates the performance based on service level agreement (SLA) metrics and energy usage of compute hosts. Relevant results concerning the best performing combination of algorithms are presented and discussed.

Suggested Citation

  • Madhubala Ganesan & Ah-Lian Kor & Colin Pattinson & Eric Rondeau, 2020. "Green Cloud Software Engineering for Big Data Processing," Sustainability, MDPI, vol. 12(21), pages 1-24, November.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:21:p:9255-:d:441386
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    References listed on IDEAS

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    1. Maria Avgerinou & Paolo Bertoldi & Luca Castellazzi, 2017. "Trends in Data Centre Energy Consumption under the European Code of Conduct for Data Centre Energy Efficiency," Energies, MDPI, vol. 10(10), pages 1-18, September.
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    Cited by:

    1. Ahmed Farouk Kineber & Ayodeji Emmanuel Oke & Ashraf Alyanbaawi & Abdurrahman Salihu Abubakar & Mohammed Magdy Hamed, 2022. "Exploring the Cloud Computing Implementation Drivers for Sustainable Construction Projects—A Structural Equation Modeling Approach," Sustainability, MDPI, vol. 14(22), pages 1-31, November.

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