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Energy efficient virtual machines placement in cloud datacenters using genetic algorithm and adaptive thresholds.

Abdullah Alourani1, Aqsa Khalid2, Muhammad Tahir2

  • 1Department of Management Information Systems and Production Management, College of Business and Economics, Qassim University, Buraidah, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces a new algorithm for virtual machine placement in cloud computing. It effectively reduces energy consumption and Service Level Agreement violations by identifying underutilized hosts.

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Data Center Management

Background:

  • Cloud computing offers on-demand IT services, but energy consumption in data centers is a significant challenge.
  • Virtualization and virtual machine placement are key techniques to improve resource utilization and reduce power usage.
  • Existing virtual machine placement algorithms often involve trade-offs between different performance parameters.

Purpose of the Study:

  • To develop an algorithm for virtual machine placement that minimizes energy consumption in cloud data centers.
  • To reduce Service Level Agreement (SLA) violations without negatively impacting other crucial parameters.
  • To enhance the overall utilization of cloud computing resources.

Main Methods:

  • An novel algorithm for virtual machine placement was designed for cloud computing environments.
  • The algorithm employs adaptive thresholding to detect overutilized and underutilized physical hosts.
  • Simulations were conducted to validate the algorithm's performance and compare it with existing methods.

Main Results:

  • The proposed algorithm demonstrated a reduction in energy consumption within data centers.
  • The algorithm successfully minimized Service Level Agreement (SLA) violations.
  • Comparative analysis indicated the effectiveness of the adaptive thresholding approach.

Conclusions:

  • The developed algorithm offers an effective solution for reducing energy consumption in cloud data centers.
  • This approach balances resource utilization and SLA adherence, addressing a critical need in cloud infrastructure.
  • The findings support the adoption of adaptive strategies for optimized virtual machine placement.