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Energy-efficient Nature-Inspired techniques in Cloud computing datacenters

Author

Listed:
  • Mohammed Joda Usman

    (Universiti Teknology Malaysia
    Bauchi State University Gadau)

  • Abdul Samad Ismail

    (Universiti Teknology Malaysia)

  • Gaddafi Abdul-Salaam

    (Kwame Nkrumah University of Science and Technology)

  • Hassan Chizari

    (University of Gloucestershire)

  • Omprakash Kaiwartya

    (Nottingham Trent University)

  • Abdulsalam Yau Gital

    (Abubakar Tafawa Balewa Bauchi)

  • Muhammed Abdullahi

    (Ahmadu Bello University Zaria)

  • Ahmed Aliyu

    (Universiti Teknology Malaysia
    Bauchi State University Gadau)

  • Salihu Idi Dishing

    (Ahmadu Bello University Zaria)

Abstract

Cloud computing is a systematic delivery of computing resources as services to the consumers via the Internet. Infrastructure as a Service (IaaS) is the capability provided to the consumer by enabling smarter access to the processing, storage, networks, and other fundamental computing resources, where the consumer can deploy and run arbitrary software including operating systems and applications. The resources are sometimes available in the form of Virtual Machines (VMs). Cloud services are provided to the consumers based on the demand, and are billed accordingly. Usually, the VMs run on various datacenters, which comprise of several computing resources consuming lots of energy resulting in hazardous level of carbon emissions into the atmosphere. Several researchers have proposed various energy-efficient methods for reducing the energy consumption in datacenters. One such solutions are the Nature-Inspired algorithms. Towards this end, this paper presents a comprehensive review of the state-of-the-art Nature-Inspired algorithms suggested for solving the energy issues in the Cloud datacenters. A taxonomy is followed focusing on three key dimension in the literature including virtualization, consolidation, and energy-awareness. A qualitative review of each techniques is carried out considering key goal, method, advantages, and limitations. The Nature-Inspired algorithms are compared based on their features to indicate their utilization of resources and their level of energy-efficiency. Finally, potential research directions are identified in energy optimization in data centers. This review enable the researchers and professionals in Cloud computing datacenters in understanding literature evolution towards to exploring better energy-efficient methods for Cloud computing datacenters.

Suggested Citation

  • Mohammed Joda Usman & Abdul Samad Ismail & Gaddafi Abdul-Salaam & Hassan Chizari & Omprakash Kaiwartya & Abdulsalam Yau Gital & Muhammed Abdullahi & Ahmed Aliyu & Salihu Idi Dishing, 2019. "Energy-efficient Nature-Inspired techniques in Cloud computing datacenters," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 71(2), pages 275-302, June.
  • Handle: RePEc:spr:telsys:v:71:y:2019:i:2:d:10.1007_s11235-019-00549-9
    DOI: 10.1007/s11235-019-00549-9
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    References listed on IDEAS

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    1. Dingde Jiang & Zhengzheng Xu & Jindi Liu & Wenhui Zhao, 2016. "An optimization-based robust routing algorithm to energy-efficient networks for cloud computing," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 63(1), pages 89-98, September.
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    5. Saurabh Singh & Pradip Kumar Sharma & Seo Yeon Moon & Jong Hyuk Park, 2017. "EH-GC: An Efficient and Secure Architecture of Energy Harvesting Green Cloud Infrastructure," Sustainability, MDPI, vol. 9(4), pages 1-18, April.
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    Cited by:

    1. S. M. Reza Dibaj & Ali Miri & SeyedAkbar Mostafavi, 2020. "A cloud dynamic online double auction mechanism (DODAM) for sustainable pricing," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 75(4), pages 461-480, December.
    2. Adeel Abro & Zhongliang Deng & Kamran Ali Memon & Asif Ali Laghari & Khalid Hussain Mohammadani & Noor ul Ain, 2019. "A Dynamic Application-Partitioning Algorithm with Improved Offloading Mechanism for Fog Cloud Networks," Future Internet, MDPI, vol. 11(7), pages 1-16, June.
    3. Teresa Murino & Roberto Monaco & Per Sieverts Nielsen & Xiufeng Liu & Gianluigi Esposito & Carlo Scognamiglio, 2023. "Sustainable Energy Data Centres: A Holistic Conceptual Framework for Design and Operations," Energies, MDPI, vol. 16(15), pages 1-14, August.
    4. Andrzej Lis & Agata Sudolska & Ilona Pietryka & Adam Kozakiewicz, 2020. "Cloud Computing and Energy Efficiency: Mapping the Thematic Structure of Research," Energies, MDPI, vol. 13(16), pages 1-21, August.

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