Earticle

초록

영어

Properly allocation of virtual machines is important for computing infrastructures scheduling. This paper presents systemic method on virtual machine array optimization control based on artificial intelligence and matrix control theory. According to request service data from users to provide proper VMs roughly via intelligent pattern recognition based on RBFNN, the data is sent to a multiple-targets optimization process to produce VMs allocation matrix precisely, thus enable to minimize the cast and enhance efficiency of the whole array to achieve low consumption optimization and ensure the stability of the system. Simulation experiments confirmed the effectiveness of this model and adaption ability in online dynamics.

목차

Abstract
 1. Introduction
 2. Preliminary Results
  2.1. Energy Consumption of VMs
  2.2. Matrix Expression of Feedback Control System
  2.3. Radial Basis Function Neural Networks (RBFNN)
 3. Optimization Modeling
  3.1. State Space Expression of VMs
  3.2. Learning Mechanism of RBFNN
  3.3. Energy Consumption Model: Multiple-target Function
 4. Model Simulation
 6. Conclusions
 7. Related Works
 Acknowledgements
 References

키워드

저자

• Wei Wu [ College of Information Science and Technology, Hainan University, China, Marine Communication and Network Research Center of Hainan Province, China ]
• Wencai Du [ College of Information Science and Technology, Hainan University, China, Marine Communication and Network Research Center of Hainan Province, China ]
• Hui Zhou [ College of Information Science and Technology, Hainan University, China ]
• Jiezhuo Zhong [ College of Information Science and Technology, Hainan University, China ]
• Zhen Guo [ College of Information Science and Technology, Hainan University, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605