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Soft Computing Techniques for Energy Consumption and Resource Aware Allocation on Cloud: A Progress and Systematic Review

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Advanced Soft Computing Techniques in Data Science, IoT and Cloud Computing

Part of the book series: Studies in Big Data ((SBD,volume 89))

Abstract

Cloud computing plays a vital role in storage and transfer of immense capacity data due to a rapid growth in size and the quantity of organizational tasks. There are many studies in which varied soft computing methods are applied to the cloud environment. In large data centers the cloud services indorse not only the energy consumption price of the substructure resources but also with a considerable growth in environmental costs. These subjects are significant requisites to decrease the energy cost and carbon footprint of cloud computing systems. To minimize energy consumption, the intelligent machines are required to achieve crossways numerous diverse machines, and strategies corresponding across the hardware and software layers to balance performance and energy, as well as to proficiently exploit multiple resources. Energy-efficient Cloud Organization Resource Allocation Framework is getting acceptance as it is paying operative consideration to cloud data management with an interpretation to achieve maximum revenue and minimum cost. The primary objective of the chapter is to conduct the systematic study and mapping of recent soft computing techniques to resolve the resource allocation and energy consumption problems in cloud computing. The chapter discuss the various soft computing techniques which are used in cloud environment for energy-resource allocation, workflow scheduling and performing the migration on cloud computing system. The first section of the chapter comprises of Introduction, motivation, background works which includes Framework for Energy and resource aware allocation using soft computing techniques, various issues, benefits of the work and application areas of soft computing techniques for cloud. The next section of the chapter highlighted the reported work which covers the detailed study of the researchers for energy efficiency and resource allocation using soft commuting techniques. The final section of the chapter discuss the comparative analysis which compares the work of different researchers by using various performance parameters such as execution time, power consumption, energy efficiency, resource utilization, response time and makespan.

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Author information

Authors and Affiliations

  1. Department of Computer Science & Engineering, Chandigarh Engineering College Landran, Mohali, India

    Sukhpreet Kaur

  2. Department of Computer Science & Engineering, Chandigarh Group of Colleges, Mohali, India

    Yogesh Kumar

  3. Chandigarh Engineering College, Landran, India

    Sushil Kumar

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  1. Sukhpreet Kaur
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  2. Yogesh Kumar
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  3. Sushil Kumar
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Application, North Orissa University, Bhubaneswar, India

    Sujata Dash

  2. School of Computer Science and Engineering, Orissa Engineering College, Bhubaneswar, Odisha, India

    Subhendu Kumar Pani

  3. Faculty of Electrical Engineering and Computer science, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  4. Department of Family and Community Health, University of Maryland, Baltimore, MD, USA

    Yulan Liang

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Kaur, S., Kumar, Y., Kumar, S. (2021). Soft Computing Techniques for Energy Consumption and Resource Aware Allocation on Cloud: A Progress and Systematic Review. In: Dash, S., Pani, S.K., Abraham, A., Liang, Y. (eds) Advanced Soft Computing Techniques in Data Science, IoT and Cloud Computing. Studies in Big Data, vol 89. Springer, Cham. https://doi.org/10.1007/978-3-030-75657-4_9

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  • DOI: https://doi.org/10.1007/978-3-030-75657-4_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-75656-7

  • Online ISBN: 978-3-030-75657-4

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