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Dynamic Graph Mining Method in Minimum Energy Consumption Optimization Cloud

CHEN Liping  a,GUO Xin  b   

  1. (a.Zhangjiajie College; b.Key Laboratory for Ecotourism of Hunan Province,Jishou University,Zhangjiajie 427000,China)
  • Received:2014-11-10 Online:2015-08-15 Published:2015-08-15

最小能耗优化云模型中的动态图挖掘方法

陈丽平a,郭鑫b   

  1. (吉首大学 a.张家界学院; b.生态旅游湖南省重点实验室,湖南 张家界 427000)
  • 作者简介:陈丽平(1980-),女,讲师、硕士,主研方向:云计算,数据挖掘;郭鑫,讲师、博士研究生、CCF会员。
  • 基金资助:
    湖南省工业支撑计划基金资助重点项目(2012GK2006);湖南省教育厅科学研究计划优秀青年基金资助项目(14B143);生态旅游湖南省重点实验室开放基金资助项目(2014-8)。

Abstract: With respect to the problems of idle consumption and luxury consumption existing in the present cloud computing platform,and the current situation that the traditional graph mining method can not satisfy the massive data mining,this paper puts forward a dynamic graph mining method for minimum energy consumption optimization cloud.It offers the cloud computing energy measurement formula,and analyzes the rationality of two types of task scheduling policy theoretically.And it considers at one time the problems of system energy optimization and system operation efficiency.Under the condition that the system operates well,it converts the problem of system energy optimization into system cost control,and proposes the total cost of the objective function,based on which to design a model for computing adaptive allocation algorithm and the minimum energy consumption optimization cloud.The paper changes the traditional graph mining serial executive mode,and raises a large-scale dynamic graph mining method based on Map Reduce mode and applies this method into minimum energy consumption optimization cloud mode to improve the integrated utilizing efficiency for the entire system.Experimental results show that the method is effective and feasible,which operates with rather high efficiency.In addition,the whole mining system energy consumption lowers obviously compared with before,especially in the case of big graph.

Key words: big data, data mining, cloud computing, energy consumption optimization, dynamic graph

摘要: 为满足海量数据挖掘的需求,提出一种新的动态图挖掘方法。给出云计算平台能耗度量公式,分析任务调度策略的合理性,综合考虑系统能耗优化与系统运行效率问题,在保证系统运行效率良好的前提下减少能耗,将系统能耗优化问题转化成系统成本控制问题,并得出总消耗成本目标函数,基于该函数设计出计算任务自适应分配算法与最小能耗优化云模型。改变传统图挖掘算法的串行执行方式,提出一种基于MapReduce模型的大规模动态图挖掘算法,并将其应用于最小能耗优化云模型中以提升整个系统综合利用效率。实验结果表明,该方法具有较高的运行效率,能够降低整个挖掘体系的能源消耗,特别是在大图情况下效果明显。

关键词: 大数据, 数据挖掘, 云计算, 能耗优化, 动态图

CLC Number: