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Utilizing power consumption and SLA violations using dynamic VM consolidation in cloud data centers

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  • Arshad, Umer
  • Aleem, Muhammad
  • Srivastava, Gautam
  • Lin, Jerry Chun-Wei

Abstract

Cloud Computing services can be accessed anytime, anywhere via the Internet. The overwhelming growth of cloud data centers over the past decade has increased their costs as energy demands have risen. As a result, higher carbon dioxide emissions and other greenhouse gasses are putting a strain on our ecosystem. The main objective of this study is to reduce the power consumption in cloud computing with no or negligible trade-offs in quality of service. This paper presents a new algorithm called the energy efficiency heuristic using virtual machine consolidation to minimize the high energy consumption in the cloud. By setting two thresholds, hosts are classified into three main classes. The designed model reallocates virtual machines from one physical host to another to minimize energy consumption. The results of the proposed algorithm have been obtained in terms of virtual machine migrations, performance degradation caused by migration, service level agreement violations, and execution time, showing a significant improvement over state-of-the-art techniques.

Suggested Citation

  • Arshad, Umer & Aleem, Muhammad & Srivastava, Gautam & Lin, Jerry Chun-Wei, 2022. "Utilizing power consumption and SLA violations using dynamic VM consolidation in cloud data centers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    • Handle: RePEc:eee:rensus:v:167:y:2022:i:c:s1364032122006669
    DOI: 10.1016/j.rser.2022.112782
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    References listed on IDEAS

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    1. Mueen Uddin & Mohammed Hamdi & Abdullah Alghamdi & Mesfer Alrizq & Mohammad Sulleman Memon & Maha Abdelhaq & Raed Alsaqour, 2021. "Server consolidation: A technique to enhance cloud data center power efficiency and overall cost of ownership," International Journal of Distributed Sensor Networks, , vol. 17(3), pages 15501477219, March.
    2. Altaf Hussain & Muhammad Aleem, 2018. "GoCJ: Google Cloud Jobs Dataset for Distributed and Cloud Computing Infrastructures," Data, MDPI, vol. 3(4), pages 1-12, September.
    3. Anders S. G. Andrae & Tomas Edler, 2015. "On Global Electricity Usage of Communication Technology: Trends to 2030," Challenges, MDPI, vol. 6(1), pages 1-41, April.
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    Cited by:

    1. Zeng, Bo & Zhou, Yinyu & Xu, Xinzhu & Cai, Danting, 2024. "Bi-level planning approach for incorporating the demand-side flexibility of cloud data centers under electricity-carbon markets," Applied Energy, Elsevier, vol. 357(C).
    2. Han, Ouzhu & Ding, Tao & Yang, Miao & Jia, Wenhao & He, Xinran & Ma, Zhoujun, 2024. "A novel 4-level joint optimal dispatch for demand response of data centers with district autonomy realization," Applied Energy, Elsevier, vol. 358(C).

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