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Datacenter Selection in Cloud Framework for Efficient Load Distribution Using a Fuzzy Approach

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Advances in Soft Computing (MICAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12468))

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Abstract

Cloud architecture delivers fast response to users using multi-tasking in several datacenters. Datacenter executes user query with virtual machine which is configured inside a host. Load balancing in datacenter is depended on utilization of cpu, mips, memory by host, and virtual machine. Prediction of resource utilization with dynamic load improves task scheduling/distribution and load balancing. We propose a cloud architecture to predict load in datacenters using fuzzy reasoning. Fuzzy based datacenter prediction analysis is used to estimate availability of datacenters with dynamic task load execution. Datacenter schedules task load in virtual machines for completing executions. Datacenters and virtual machines load distribution and physical resource utilization have been accomplished using scheduling algorithms.

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Acknowledgment

This research work is funded by Computer Innovative Research Society, West Bengal, India. Award number is “2020/CIRS/R&D/1201-06-15/DSCFELDFA”.

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Authors and Affiliations

  1. Netaji Subhash Engineering College, Kolkata, 700152, India

    Mou De & Anirban Kundu

  2. Computer Innovative Research Society, Howrah, 711103, West Bengal, India

    Mou De & Anirban Kundu

Authors
  1. Mou De
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  2. Anirban Kundu
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Corresponding author

Correspondence to Mou De .

Editor information

Editors and Affiliations

  1. Facultad de Ingeniería, Universidad Panamericana, Mexico City, Mexico

    Lourdes Martínez-Villaseñor

  2. Universidad Autónoma Metropolitana, Mexico City, Mexico

    Oscar Herrera-Alcántara

  3. Facultad de Ingeniería, Universidad Panamericana, Mexico City, Mexico

    Hiram Ponce

  4. Universidad Autónoma del Estado de Hidalgo, Hidalgo, Mexico

    Félix A. Castro-Espinoza

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De, M., Kundu, A. (2020). Datacenter Selection in Cloud Framework for Efficient Load Distribution Using a Fuzzy Approach. In: Martínez-Villaseñor, L., Herrera-Alcántara, O., Ponce, H., Castro-Espinoza, F.A. (eds) Advances in Soft Computing. MICAI 2020. Lecture Notes in Computer Science(), vol 12468. Springer, Cham. https://doi.org/10.1007/978-3-030-60884-2_32

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  • DOI: https://doi.org/10.1007/978-3-030-60884-2_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60883-5

  • Online ISBN: 978-3-030-60884-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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