基于广义模糊软集理论的云计算资源需求组合预测研究

doi:10.16381/j.cnki.issn1003-207x.2015.05.008

中国管理科学 ›› 2015, Vol. 23 ›› Issue (5): 56-64.doi: 10.16381/j.cnki.issn1003-207x.2015.05.008

基于广义模糊软集理论的云计算资源需求组合预测研究

徐达宇^1,2, 杨善林², 罗贺²

1. 浙江农林大学浙江省林业智能监测与信息技术研究重点实验室, 浙江杭州 311300;
2. 合肥工业大学过程优化与智能决策教育部重点实验室, 安徽合肥 230009

收稿日期:2012-11-20 修回日期:2013-06-17 出版日期:2015-05-20 发布日期:2015-05-20
作者简介:徐达宇(1985-),男(汉族),浙江杭州人,浙江农林大学信息工程学院讲师,研究方向:预测理论与方法、云计算.
基金资助:
国家自然科学基金资助项目(71131002,71071045);浙江农林大学校科研发展基金人才启动项目(2014FR082)

Research on Generalized Fuzzy Soft Sets Theory based Combined Model for Demanded Cloud Computing Resource Prediction

XU Da-yu^1,2, YANG Shan-lin², LUO He²

1. Zhejiang A&F University, Zhejiang Provincial Key Laboratory of Forestry Intelligent Monitoring and Information Technology Research, Hangzhou 311300, China;
2. HeFei University of Technology, Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, HeFei 230009, China

Received:2012-11-20 Revised:2013-06-17 Online:2015-05-20 Published:2015-05-20

摘要/Abstract

摘要： 论述了云计算资源需求预测的作用,提出了新的基于夹角余弦的广义模糊软集相似性度量方法,将相似性度量结果与预测精度相结合来获得各单项预测模型的权重,并针对云计算环境中资源需求所表现出的短期动态性和长期周期性特征,选用自适应神经模糊推理系统ANFIS和季节性ARIMA模型SARIMA作为单项预测模型,以此构建基于广义模糊软集理论的组合预测模型GFSS-ANFIS/SARIMA。最后将该模型用于云计算环境下的资源需求预测应用中去。实验结果表明,与其它预测模型相比,该模型能有效提高预测精度,具有良好的预测性能。本文所提方法能为云计算资源的高效调度和分配提供决策支持。

关键词: 云计算, 广义模糊软集, 相似性度量, 组合预测, 自适应神经模糊推理系统

Abstract: In order to realize high scalability, flexibility and cost-effectiveness, cloud computing platforms need to be able to quickly plan and provision resources. To this end, it calls for mechanisms to predict demanded resource effectively. Therefore, resource prediction is a crucial issue for efficient resource utilization in dynamic cloud computing environment. In this paper, the basic concept of generalized fuzzy soft sets is introduced, and a novel angle cosine is proposed based similarity measurement of generalized fuzzy soft sets. Then the similarity measurement result and the prediction accuracy from Adaptive Neuro-Fuzzy Inference System and Seasonal ARIMA model are adopted to obtain the weights of combined prediction model. On this basis,the generalized fuzzy soft sets theory based on the combination of forecasting model GFSS-ANFIS/SARIMA is constrncted. Finally, this model is explorted to predict the demanded resource in cloud computing. The experimental results show that the proposed model can significantly improve the prediction accuracy with high prediction performance. Efficient decision support for resource scheduling and allocation in cloud computing can be provided by the proposed method.

Key words: cloud computing, generalized fuzzy soft sets, combined prediction, similarity measurement, adaptive neuro-fuzzy inference system

中图分类号:

TP391

徐达宇, 杨善林, 罗贺. 基于广义模糊软集理论的云计算资源需求组合预测研究[J]. 中国管理科学, 2015, 23(5): 56-64.

XU Da-yu, YANG Shan-lin, LUO He. Research on Generalized Fuzzy Soft Sets Theory based Combined Model for Demanded Cloud Computing Resource Prediction[J]. Chinese Journal of Management Science, 2015, 23(5): 56-64.

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基于广义模糊软集理论的云计算资源需求组合预测研究

Research on Generalized Fuzzy Soft Sets Theory based Combined Model for Demanded Cloud Computing Resource Prediction

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