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Dynamic Virtual Machine Provisioning in Cloud Computing Using Knowledge-Based Reduction Method

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Book cover Next Generation Information Processing System

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1162 ))

Abstract

Cloud infrastructure performance extremely depends ahead on the task scheduling and load balancing. The recent growth of cloud computing and service provider’s key challenge is scheming proficient mechanism for managing the restricted resources shared by different applications. Resource administration method has to do efficient assignment of resources for virtual machines by ensuring optimal resource exploitation of available physical machines. This paper proposes the application of rough-set model for provisioning of virtual machines. The proposed method uses knowledge-based reduction technique, and it generates the rules to reduce unnecessary attributes of the virtual machines. These rules help virtual machine managers for making effective administration of restricted resources.

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Author information

Authors and Affiliations

  1. Department of CSE, DBIT, Bengaluru, India

    R. Bhaskar

  2. Department of ISE, Dr. AIT, Bengaluru, India

    B. S. Shylaja

Authors
  1. R. Bhaskar
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  2. B. S. Shylaja
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Corresponding author

Correspondence to R. Bhaskar .

Editor information

Editors and Affiliations

  1. Department of Computer Engineering, Dr. Babasaheb Ambedkar Technological University, Lonere, Maharashtra, India

    Prachi Deshpande

  2. Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  3. Department of Electronics and Telecommunication Engineering, Dr. Babasaheb Ambedkar Technological University, Lonere, Maharashtra, India

    Brijesh Iyer

  4. School of Information Science and Engineering, University of Jinan, Jinan, Shandong, China

    Kun Ma

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Bhaskar, R., Shylaja, B.S. (2021). Dynamic Virtual Machine Provisioning in Cloud Computing Using Knowledge-Based Reduction Method. In: Deshpande, P., Abraham, A., Iyer, B., Ma, K. (eds) Next Generation Information Processing System. Advances in Intelligent Systems and Computing, vol 1162 . Springer, Singapore. https://doi.org/10.1007/978-981-15-4851-2_21

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