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An Energy-Efficient Strategy and Secure VM Placement Algorithm in Cloud Computing.

Devesh Kumar Srivastava1, Pradeep Kumar Tiwari1, Mayank Srivastava2

  • 1Manipal University Jaipur, Jaipur, India.

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Summary
This summary is machine-generated.

This study proposes a new algorithm for energy-efficient virtual machine (VM) placement in cloud computing. The algorithm optimizes resource allocation, reduces active physical machines (PMs), and minimizes task processing time (makespan).

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Area of Science:

  • Cloud Computing
  • Resource Management
  • Energy Efficiency

Background:

  • Cloud computing faces challenges in optimizing resource utilization and minimizing energy consumption.
  • Virtual machine (VM) placement, mapping virtual machines (VMs) to physical machines (PMs), is complex due to diverse cloud environments.
  • Existing consolidation methods are often tedious and problematic.

Purpose of the Study:

  • To propose an algorithm for reducing energy consumption and optimizing resource allocation in cloud computing.
  • To implement an effective virtual machine (VM) placement strategy.
  • To minimize the number of active physical machines (PMs) and optimize makespan time.

Main Methods:

  • Development of a Cloud System Model for mapping VMs to PMs and tasks.
  • Implementation of an algorithm focused on energy reduction and resource allocation.
  • Evaluation using the CloudSim Simulator to assess energy consumption and makespan time.

Main Results:

  • The proposed algorithm effectively reduces energy consumption in cloud data centers.
  • Optimized resource allocation leads to a decrease in the number of active physical machines (PMs).
  • Significant reduction in the total task processing time (makespan) was observed.

Conclusions:

  • The developed algorithm offers an energy-efficient solution for virtual machine (VM) placement.
  • The methodology successfully balances energy savings with performance optimization (makespan).
  • Graphical comparisons demonstrate the superiority of the proposed algorithm over existing energy-efficient VM placement strategies.