Earticle

초록

영어

On demand resource forecasting in cloud computing is an crucial guarantee for achieving effective management of all virtualized resources and reducing data center energy consumption. According to single forecasting model cannot integrate all the valid information which leads to the decline in prediction accuracy. This paper proposed an optimal combination prediction model for cloud computing resource requirement. This model is based on generalized Dice coefficient and the induced ordered weighted geometric mean (IOWGA) operator, as well as improved Elman neural network and grey forecasting model. It is able to accurately reflect the random information and trend information in cloud computing load thus will enhance the overall prediction accuracy. The experiment results show this method is feasible and effective.

목차

Abstract
 1. Introduction
 2. Clouds Computing Resource Management Structure and Operating System
 3. Instant Analyses
  3.1. Improved Elman Wavelet Neural Network
  3.2. GM(1.1)
  3.3. IOWGA Operator Optimization Assembled Prediction Model Based On Generalized Dice Coefficient
 4. Instant Analyses
 5. Conclusions
 References

키워드

저자

• Lin Li [ School of Management, He Fei University of Technology, Anhui PR China, Anhui finance &trade vocational college, Anhui PR China ] Corresponding Author
• Aiguo Zhang [ School of Management, AnQing teachers college, Anhui PR 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