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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
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Chaotic Salp Swarm Optimization-Based Energy-Aware VMP Technique for Cloud Data Centers.

S Parthiban1, A Harshavardhan2, S Neelakandan3

  • 1Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India.

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|May 23, 2022
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Summary

This study introduces a novel energy-aware virtual machine placement (VMP) technique for Cloud Data Centers (CDCs) using a Disordered Salp Swarm Optimization Algorithm. The method significantly reduces energy consumption and improves resource utilization by optimizing server activity.

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Area of Science:

  • Cloud Computing
  • Data Center Energy Efficiency
  • Optimization Algorithms

Background:

  • Increasing energy consumption in Cloud Data Centers (CDCs) necessitates efficient resource management.
  • Virtual Machine Placement (VMP) is crucial for reducing energy usage but is an NP-hard problem.
  • Metaheuristic Optimization Algorithms are commonly used for VMP challenges.

Purpose of the Study:

  • To introduce a novel energy-aware VMP technique for CDCs.
  • To enhance energy efficiency in CDCs through optimized virtual machine placement.
  • To improve resource operation balancing (CPU, RAM, Bandwidth) and reduce waste.

Main Methods:

  • Developed an Energy-Aware VMP technique based on the Disordered Salp Swarm Optimization Algorithm (EAVMP-CSSA).
  • Integrated chaotic maps with the Salp Swarm Optimization Algorithm (SSA) to create CSSA for improved performance and reduced costs.
  • Reduced CDC energy consumption by minimizing active servers supporting virtual machines.

Main Results:

  • The EAVMP-CSSA technique achieved a maximum service rate of 98.12%.
  • Outperformed existing methods like Random (74.40%), FFD (78.80%), ACO (90.70%), and AP-ACO (96.31%).
  • Demonstrated superior performance across various assessment metrics.

Conclusions:

  • The proposed EAVMP-CSSA technique effectively reduces energy consumption in CDCs.
  • The method enhances resource utilization and operational efficiency.
  • EAVMP-CSSA represents a significant advancement in VMP strategies for energy-efficient data centers.