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Algorithmic Approach to Virtual Machine Migration in Cloud Computing with Updated SESA Algorithm.

Amandeep Kaur1, Saurabh Kumar1, Deepali Gupta1

  • 1Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab 140401, India.

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|July 14, 2023
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Summary
This summary is machine-generated.

This study introduces an energy-efficient virtual machine (VM) migration algorithm for cloud computing. It enhances scheduling using elastic scheduling and cosine similarity to reduce power consumption and SLA violations.

Keywords:
SESAcloud computingmigrationpower consumptionvirtual machine

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Energy Efficiency

Background:

  • Cloud computing is vital for IT services, with major companies deploying global data centers.
  • Early cloud development prioritized performance over energy consumption, neglecting sustainability.
  • Advancements in algorithms and bio-inspired techniques aim to improve energy efficiency and sustainability in cloud infrastructure.

Purpose of the Study:

  • To investigate virtual machine migration approaches in cloud computing.
  • To identify and address issues within existing VM migration strategies.
  • To develop a more energy-efficient VM allocation and migration algorithm.

Main Methods:

  • Utilizing elastic scheduling, inspired by the Smart Elastic Scheduling Algorithm (SESA).
  • Incorporating cosine similarity and bandwidth utilization to enhance Quality of Service (QoS).
  • Evaluating the proposed algorithm's performance against state-of-the-art techniques.

Main Results:

  • The proposed algorithm demonstrates improved energy efficiency in cloud data centers.
  • Reduced Service Level Agreement Violations (SLA-V) were observed.
  • The algorithm effectively addresses identified issues in existing VM migration approaches.

Conclusions:

  • The developed elastic scheduling approach offers a more sustainable cloud computing solution.
  • Cosine similarity and bandwidth utilization are effective metrics for enhancing VM migration.
  • The research contributes a novel algorithm for energy-efficient VM management in cloud environments.