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A distributed parallel genetic algorithm of placement strategy for virtual machines deployment on cloud platform.

Yu-Shuang Dong1, Gao-Chao Xu2, Xiao-Dong Fu1

  • 1College of Computer Science and Technology, Jilin University, Changchun 130012, China.

Thescientificworldjournal
|August 7, 2014
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Summary
This summary is machine-generated.

This study introduces a distributed parallel genetic algorithm (DPGA) for virtual machine deployment on cloud platforms. The DPGA optimizes resource allocation to enhance performance and reduce energy costs, ensuring quality of service (QoS).

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Area of Science:

  • Computer Science
  • Cloud Computing
  • Artificial Intelligence

Background:

  • Cloud computing centers increasingly offer infrastructure as a service, necessitating efficient resource utilization.
  • Virtualization enables resource sharding to meet user demands and reduce operational costs.
  • Balancing user Quality of Service (QoS) with provider cost savings, including energy efficiency, is crucial.

Purpose of the Study:

  • To propose a novel placement strategy for virtual machine (VM) deployment on cloud platforms.
  • To optimize VM placement for both user-perceived performance (QoS) and reduced energy consumption.
  • To enhance the efficiency and energy savings of cloud resource management.

Main Methods:

  • Development of a distributed parallel genetic algorithm (DPGA) for VM placement.
  • A two-stage genetic algorithm execution: parallel distributed execution followed by a refined second stage.
  • Utilizing solutions from the first stage as the initial population for the second stage to find the optimal placement.

Main Results:

  • The proposed DPGA placement strategy effectively ensures QoS for cloud users.
  • Experimental results demonstrate superior effectiveness compared to existing VM placement strategies.
  • The strategy significantly improves energy efficiency in cloud data centers.

Conclusions:

  • The DPGA offers an effective and energy-efficient solution for VM deployment in cloud environments.
  • This approach successfully balances user QoS requirements with the economic and environmental goals of cloud providers.
  • The findings highlight the potential of advanced algorithms for optimizing cloud infrastructure management.