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Resource Allocation and Optimization Based on Queuing Theory and BP Network

  • Hong Tang
  • Delu Zeng
  • Xin Liu
  • Jiabin Huang
  • Yinghao Liao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10634)

Abstract

In this article, we present a resource allocation and optimization strategy for data center based on resource utilization prediction with back-propagation (BP) neural network, aiming to improve the resource utilization. We handle resource contention among virtual machines with resource migrating to improve the resource utilization under the assumption of different functional applications integrated in each server. With the BP network predicted resources utilization and throughput rate of SFC, we adjust and optimize the resource configuration in virtual resource pool and servers, which further improves resource utilization in data center. Our experiments show that the proposed dynamic resource allocation and optimization strategy performs effectively. And also the BP network achieves more accuracy prediction compared with linear regression model.

Keywords

Resource allocation BP neural network Resource utilization prediction Network Function Virtualization 

Notes

Acknowledgments

This work was supported in part by the grants of National Science Foundation of China (No. 61571005, No. 61103121, No. 61571382, No. 61372142), the NSF of Guangdong Province (No. 2015A030313589), and the XMU President Funding (No. 20720150093).

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hong Tang
    • 1
  • Delu Zeng
    • 2
  • Xin Liu
    • 3
  • Jiabin Huang
    • 3
  • Yinghao Liao
    • 3
  1. 1.Sun Yat-sen UniversityGuangzhouChina
  2. 2.South China University of TechnologyGuangzhouChina
  3. 3.Xiamen UniversityXiamenChina

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