Earticle

초록

영어

Due to lacking valid prediction for resource requirement, the existing application approaches for cloud computing resource could hardly achieve a high efficiency. According to this point, we propose a mixed-prediction based resource allocation approach in this paper, which is abbreviated as MPRA. This proposed MPRA employs FFT (Fast Fourier Transform) theory to determine the cyclical attribution. If there is no such attribution existed, the Markov chain is alternatively used to predict the tendency of resource requirement. The experimental results show that the proposed MPRA could predict the future resource requirement more precisely. Moreover, based on the prediction result, it could also allocate the virtual machine resource adaptively, decrease the number of occupied physical machines, and reduce the probability of violating the SLA (Service-level Agreement).

목차

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

키워드

저자

• Qian Zhao [ School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China ]
• Guangsheng Feng [ College of Computer Science and Technology, Harbin Engineering University, Harbin, China ]
• Rui Gao [ College of Computer Science and Technology, Harbin Engineering University, Harbin, China ]
• Ke Han [ School of Computer and Information Engineering, Harbin University of Commerce, Harbin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605