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Towards an adaptive human-centric computing resource management framework based on resource prediction and multi-objective genetic algorithm

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Abstract

The complexity, scale and dynamic of data source in the human-centric computing bring great challenges to maintainers. It is problem to be solved that how to reduce manual intervention in large scale human-centric computing, such as cloud computing resource management so that system can automatically manage according to configuration strategies. To address the problem, a resource management framework based on resource prediction and multi-objective optimization genetic algorithm resource allocation (RPMGA-RMF) was proposed. It searches for optimal load cluster as training sample based on load similarity. The neural network (NN) algorithm was used to predict resource load. Meanwhile, the model also built virtual machine migration request in accordance with obtained predicted load value. The multi-objective genetic algorithm (GA) based on hybrid group encoding algorithm was introduced for virtual machine (VM) resource management, so as to provide optimal VM migration strategy, thus achieving adaptive optimization configuration management of resource. Experimental resource based on CloudSim platform shows that the RPMGA-RMF can decrease VM migration times while reduce physical node simultaneously. The system energy consumption can be reduced and load balancing can be achieved either.

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Authors and Affiliations

  1. International School of Software, Wuhan University, Wuhan, China

    Si Zheng & GuoBin Zhu

  2. Jianghan University, Wuhan, China

    Jie Zhang

  3. Wuhan Media and Communications College of Huazhong Normal University, Wuhan, China

    Wei Feng

Authors
  1. Si Zheng
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  2. GuoBin Zhu
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  3. Jie Zhang
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  4. Wei Feng
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Correspondence to Si Zheng.

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Zheng, S., Zhu, G., Zhang, J. et al. Towards an adaptive human-centric computing resource management framework based on resource prediction and multi-objective genetic algorithm. Multimed Tools Appl 76, 17821–17838 (2017). https://doi.org/10.1007/s11042-015-3096-1

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  • DOI: https://doi.org/10.1007/s11042-015-3096-1

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