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Related Experiment Video

Updated: Jul 19, 2025

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A Harris Hawk Optimisation system for energy and resource efficient virtual machine placement in cloud data centers.

Madhusudhan H S1, Satish Kumar T2, Punit Gupta3

  • 1Department of Computer Science & Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.

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|August 11, 2023
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Summary
This summary is machine-generated.

This study introduces a Harris Hawk Optimization (HHO) model for efficient virtual machine placement in cloud data centers. The HHO model significantly reduces power consumption and improves resource utilization for optimized cloud infrastructure.

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Optimization Algorithms

Background:

  • Cloud data centers face increasing energy demands due to service migration and expanding infrastructure.
  • Efficient resource allocation is crucial for optimizing power usage and operational costs in cloud environments.
  • Virtual machine placement strategies directly impact cloud resource utilization and energy consumption.

Purpose of the Study:

  • To present a novel virtual machine placement technique using the Harris Hawk Optimization (HHO) model.
  • To optimize virtual machine placement for reduced load and power consumption on cloud data center hosts.
  • To evaluate the effectiveness of the HHO model against existing methods in dynamic cloud environments.

Main Methods:

  • Developed a virtual machine placement technique based on the Harris Hawk Optimization (HHO) model.
  • Utilized PlanetLab's real-time workload traces for performance evaluation.
  • Compared the HHO model with Particle Swarm Optimization (PSO) and Best Fit Decreasing (BFD) algorithms.

Main Results:

  • The HHO algorithm improved execution time by 4% and resource utilization by 17%.
  • Achieved a 27% reduction in power consumption and a 16% decrease in Service Level Agreement (SLA) violations.
  • Demonstrated superior performance in handling underloaded and overloaded conditions through simulation scenarios.

Conclusions:

  • The proposed HHO model offers an efficient and effective solution for virtual machine placement in cloud data centers.
  • HHO is suitable for real-world cloud infrastructures due to its effectiveness in dynamic and uncertain environments.
  • Optimized virtual machine placement using HHO leads to significant energy savings and improved operational efficiency.