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Virtual Machine Resource Allocation Optimization in Cloud Computing Based on Multiobjective Genetic Algorithm.

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Optimizing virtual machine (VM) allocation in cloud computing is crucial for efficiency. This study introduces a novel multiobjective optimization method, MOGA-C, enhancing VM distribution stability and faster convergence for Infrastructure as a Service (IaaS) environments.

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Area of Science:

  • Cloud Computing
  • Distributed Systems
  • Virtualization Technology

Background:

  • Infrastructure as a Service (IaaS) is a key cloud computing model providing resources as virtual machines (VMs).
  • Efficient VM allocation is essential for optimizing resource utilization, reducing costs, and saving user computing time.
  • Existing methods like GA-NN focus on energy saving and redistribution overhead, potentially impacting VM distribution stability.

Purpose of the Study:

  • To develop a multiobjective optimization method for dynamic resource allocation in multivirtual machine distribution, prioritizing stability.
  • To improve upon existing algorithms by considering both current and predicted application load data.
  • To enhance the stability and efficiency of VM placement in IaaS environments.

Main Methods:

  • A multiobjective optimization genetic algorithm (MOGANS) was initially designed to address dynamic VM allocation.
  • The MOGANS algorithm considers VM relocation costs and the stability of new VM placements.
  • A subsequent method, MOGA-C based on MOEA/D, was proposed to improve convergence speed and stability.

Main Results:

  • MOGANS demonstrated longer virtual machine distribution stability compared to GA-NN.
  • The MOGA-C method, based on MOEA/D, showed faster convergence.
  • MOGA-C achieved comparable multiobjective optimization results with a similar computational scale.

Conclusions:

  • Dynamic resource allocation in IaaS requires advanced optimization techniques for stability and efficiency.
  • The proposed MOGA-C method offers a faster and effective approach for multiobjective VM distribution.
  • Further research can explore integrating predictive analytics for even more robust VM allocation strategies.