earticle

논문검색

A Mixed-Prediction based Method for Allocating Cloud Computing Resources

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

저자정보

  • 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

자료제공 : 네이버학술정보

    함께 이용한 논문

      ※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.

      0개의 논문이 장바구니에 담겼습니다.