Earticle

초록

영어

There are some problems in the existing cloud platform resource allocation methods, such as low rates of resource utilization and the lack of accurate prediction of the trend changes of resources, etc. To solve these problems, MPRA（Mixed Prediction Based Resource Allocation）was proposed. According to the periodic and non-periodic characteristics of service resources demand, MPRA first adopts FFT(Fast Fourier Transform) to judge the periodic characteristics. For resource allocation without periodic characteristics, it uses Markov process to predict, and obtains the higher resource utilization and prediction accuracy, thus, to ensure the user experience. The experimental results show that MPRA can accurately predict the change trend of service resource requirements, and then can allocate the virtual machine resources self-adaptively according to the prediction results. Obviously, it has improved the virtual machine resources utilization, reduced the occupation number of physical machines and effectively reduced the violation times in SLA (Service-level Agreement).

목차

Abstract
 1. Introduction
 2. MPRA：the Model of Cloud Platform Resource Allocation based on Mixed Predictions
 3. Adaptive Allocation Algorithm of Resources
 4. The Experiment and Analysis
  4.1 The Virtual Machine Resource Allocation Experiment
  4.2 MPRA Simulation Analysis based on CloudSim
 5. Conclusion
 References

키워드

저자

• Hong Qi [ Information and Computer Engineering College, Northeast Forestry University, Harbin, 150040, China ]
• Honge Ren [ Information and Computer Engineering College, Northeast Forestry University, Harbin, 150040, China ] Corresponding author
• Guanglei Zhang [ Information and Computer Engineering College, Northeast Forestry University, Harbin, 150040, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605