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Knowledge Based Cloud FE Simulation of Sheet Metal Forming Processes
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Energy-efficient cloud systems: Virtual machine consolidation with -robustness optimization.

Xinming Han1, Jianxiao Wang2, Jiaxi Wu3

  • 1Department of Industrial Engineering and Management, College of Engineering, Peking University, Beijing 100871, China.

Iscience
|March 19, 2025
PubMed
Summary
This summary is machine-generated.

This study optimizes virtual machine (VM) placement in cloud computing for better resource use and energy efficiency. A new robust model and heuristic algorithm significantly improve performance and reduce energy consumption.

Keywords:
Computer scienceEnergy engineeringEngineering

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Area of Science:

  • Cloud Computing
  • Operations Research
  • Energy Efficiency

Background:

  • Cloud computing environments face challenges in optimizing virtual machine (VM) placement.
  • Inefficient VM placement leads to underutilized resources and increased energy consumption.
  • Uncertainty in VM workload demands complicates optimal resource allocation.

Purpose of the Study:

  • To develop a robust optimization model for virtual machine placement.
  • To enhance resource utilization and energy efficiency in cloud data centers.
  • To address uncertainties in virtual machine usage patterns.

Main Methods:

  • Formulation of a mixed integer linear programming (MILP) model.
  • Integration of -robustness theory to handle workload uncertainties.
  • Development of a heuristic algorithm for large-scale VM allocation.

Main Results:

  • The proposed MILP model effectively optimizes VM placement under uncertainty.
  • Significant improvements observed in resource utilization metrics.
  • Demonstrated substantial gains in energy efficiency in experimental data.

Conclusions:

  • The robust optimization approach provides an effective solution for VM placement.
  • The heuristic algorithm is suitable for practical, large-scale cloud environments.
  • The study confirms the benefits of robust optimization for cloud resource management.