...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Computers >K-Means Method for Grouping in Hybrid MapReduce Cluster
【24h】

K-Means Method for Grouping in Hybrid MapReduce Cluster

机译：K-均值在混合MapReduce集群中分组的方法

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

—In hybrid cloud computing era, hybrid clusters which are made of virtual machines and physical machines would be seen more and more generally. Hybrid clusters need more careful organization for finer resource allocations. Another problem of big data in this era is that database system can not well-handled the semi-structured and unstructured data. Luckily, MapReduce is a good weapon to solve the increasing big size and quicklyincreased data at this social computing and multimedia computing time. One of the biggest challenges in hybrid mapreduce cluster is I/O bottleneck which would be aggravated under big data computing. In this paper, we take data locality into consideration and group slave nodes with low intra-communication and high intercommunication. After introducing the architecture and implementation of our grouped hybrid mapreduce cluster(GHMC), we give our method of k-means algorithm to group in our GHMC system and evaluate it with reality environments. The results show that there is a nearly 34.9% performance improvement in our GHMC system which are deployed by our K-means algorithm. What’s more, it also shows good scalability.
机译：-IN混合云计算时代，越来越多地看到由虚拟机和物理机器制成的混合簇。混合集群需要更精细的组织进行更精细的资源分配。此时代大数据的另一个问题是数据库系统无法充分处理半结构化和非结构化数据。幸运的是，Mapreduce是解决这一社交计算和多媒体计算时间的越来越大的大尺寸和迅速的增加的数据。混合mapReduce集群中最大的挑战之一是I / O瓶颈将在大数据计算下加重。在本文中，我们考虑了数据通道，并具有低通信和高互通的组从节点。在引入我们分组混合MapReduce集群（GHMC）的架构和实现后，我们将我们的K-Means算法提供给GHMC系统中的组，并使用现实环境进行评估。结果表明，我们的GHMC系统中有近34.9％的性能改进，由我们的K-Means算法部署。更重要的是，它也显示出良好的可扩展性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号