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Operation of the Collaborative Composite Manufacturing CCM System
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Multi-resource collaborative optimization for adaptive virtual machine placement.

Zhihua Li1, Meini Pan1, Lei Yu2

  • 1Department of Computer Science and Technology, Jiangnan University, Wuxi, Jiangsu, China.

Peerj. Computer Science
|February 3, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a multi-resource collaborative optimization control (MCOC) mechanism for virtual machine (VM) migration to balance cloud data center resource utilization. The adaptive Gaussian model-based VMs consolidation (AGM-VMC) method effectively improves resource efficiency and reduces energy consumption while maintaining quality of service (QoS).

Keywords:
Collaborative optimization controlGaussian modelResource utilization imbalanceVM placement

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Area of Science:

  • Cloud Computing
  • Data Center Optimization
  • Resource Management

Background:

  • Unbalanced resource utilization in cloud data centers leads to wasted resources, workload imbalance, and degraded Quality of Service (QoS).
  • Effective management of physical machine (PM) resources is crucial for efficient cloud operations.

Purpose of the Study:

  • To propose a multi-resource collaborative optimization control (MCOC) mechanism for virtual machine (VM) migration.
  • To enhance resource utilization, reduce energy consumption, and maintain QoS in cloud data centers.

Main Methods:

  • Adaptive estimation of PM multi-resource utilization balance status using a Gaussian model.
  • Development of adaptive Gaussian model-based VMs placement (AGM-VMP) and VMs consolidation (AGM-VMC) algorithms for live VM migration.
  • Selection algorithms for source and destination hosts based on estimated balance probability.

Main Results:

  • The proposed AGM-VMC method effectively achieves load balance across physical machines.
  • Significant improvements in overall resource utilization were observed.
  • Demonstrated reduction in data center energy consumption.
  • Quality of Service (QoS) was maintained throughout the migration process.

Conclusions:

  • The MCOC mechanism, particularly the AGM-VMC method, offers an effective solution for optimizing resource utilization in cloud data centers.
  • The approach successfully balances workloads, conserves energy, and ensures QoS.
  • Gaussian modeling provides an adaptive and efficient way to manage VM migrations.