摘要
为实现海量大数据的高效访问与处理,在资源与服务发现的基础上,探讨了一种云格环境下使用网络距离预测的方法来完成计算资源服务节点的选择机制.并从提高系统性能的角度,针对用户偏好,提出了一种可计算资源与服务的高效调配模型,来实现密集型数据的高性能计算;最后实验验证了本调配模型具有的可行性与针对性.
In order to implement massive big data access and process with high efficiency, based on the discovery of resource and service, a mechanism on selection of service nodes for the computational resources is explored by using the method of networking distance prediction under GLOUD. Then considering the indicator of system performance including response time, resources utilization and access cost and others, a resources and services provisioning model is proposed to implement data intensive computing with high efficiency from the view of user's preference. Finally experiment shows that the provisioning model is available and pertinently.
出处
《浙江大学学报(理学版)》
CAS
CSCD
2014年第3期353-357,共5页
Journal of Zhejiang University(Science Edition)
基金
国家863基金资助项目(2009AA12Z222)
国家自然科学基金资助项目(41001227
41101356)
惠州学院博士启动基金资助项目(C513.0201)
关键词
云格环境
网络距离预测
资源与服务
调配模型
GLOUD environment
networking distance predict
resources and services
provisioning model