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Related Experiment Video

Updated: Jun 11, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Efficient energy consumption in hybrid cloud environment using adaptive backtracking virtual machine consolidation.

S Manikandan1, E Elakiya2, K C Rajheshwari3

  • 1Department of Information Technology, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, India. profmaninvp@gmail.com.

Scientific Reports
|October 1, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces adaptive backtracking methods for Virtual Machine (VM) consolidation, significantly reducing energy consumption in hybrid cloud environments. The new approach achieves 95% accuracy while lowering energy usage by up to 32%.

Keywords:
AccuracyBacktrackingEnergy efficiencyHybrid cloudVirtual machine consolidation

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Energy Efficiency

Background:

  • Virtualization enables resource sharing via Virtual Machines (VMs), but faces challenges like high demand, infrastructure issues, and Service Level Agreement (SLA) violations.
  • Existing VM consolidation methods often increase energy consumption and overhead, necessitating more efficient solutions.

Purpose of the Study:

  • To propose adaptive backtracking methods for VM consolidation to minimize energy consumption.
  • To optimize resource utilization and reduce operational costs in hybrid cloud environments.

Main Methods:

  • Implemented adaptive backtracking algorithms, specifically Adaptive Hill Climbing and Pursuit, for VM consolidation.
  • Simulated the proposed methods in a hybrid cloud environment using Matlab.
  • Evaluated performance based on energy consumption and accuracy.

Main Results:

  • The proposed adaptive backtracking methods achieved VM consolidation with significantly lower energy consumption compared to existing methods.
  • The system demonstrated an accuracy index of 95%.
  • Energy consumption was reduced by 28%, 30%, and 32% for multiple VM consolidations.

Conclusions:

  • Adaptive backtracking methods offer an effective approach to VM consolidation for enhanced energy efficiency in hybrid clouds.
  • The proposed system provides a scalable and accurate solution for optimizing resource utilization and reducing energy footprints.