...
首页> 外文期刊>International Journal of Parallel, Emergent and Distributed Systems >Optimisation to the execution performance of grid job based on distributed file system
【24h】

Optimisation to the execution performance of grid job based on distributed file system

机译:基于分布式文件系统的网格作业执行性能的优化

获取原文
获取原文并翻译 | 示例
           

摘要

In grid computing, many applications are invoked by the means of job submission. Grid job submission comprises the following scenarios: file stage-in, execution and file stage-out. Most of the grid midware provides the job submission infrastructure to help to submit jobs, and GridFTP (file downloading) is used in the file stage operations. These scenarios exit mostly in batch mode. It causes computational resources waiting for the data transfer and limits the performance of the grid application, especially when the application is data/communication intensive. Using the file sharing of distributed file system instead of GridFTP in the job submission, the scenarios of grid job submission can be processed in parallel, whereas the waiting of computational resource can be eliminated. By this way, the execution performance of the grid job is promoted. After the comparison of typical distributed file systems, network attached storage that meets the requirements of grid is picked out, and the file sharing mode for the data exchange in grid job is proposed. In the tests of a series of data-communication-intensive jobs, the performance promotion is verified. Comparing with GridFTP, the average promotion with Common Internet File System is 16.8% to single-task jobs and 19.5% to multiple-task jobs.
机译:在网格计算中,许多应用程序是通过作业提交来调用的。网格作业提交包含以下方案:文件逐步进入,执行和文件逐步退出。大多数网格中间件都提供了作业提交基础结构,以帮助提交作业,并且GridFTP(文件下载)用于文件阶段操作。这些方案大多以批处理模式退出。它导致计算资源等待数据传输,并限制了网格应用程序的性能,尤其是当应用程序需要大量数据/通信时。在作业提交中使用分布式文件系统的文件共享而不是GridFTP,可以并行处理网格作业提交的场景,而可以消除计算资源的等待。这样,可以提高网格作业的执行性能。通过比较典型的分布式文件系统,挑选出满足网格需求的网络附加存储,并提出了网格作业中数据交换的文件共享方式。在一系列数据通信密集型工作的测试中,性能提升得到了验证。与GridFTP相比,Common Internet File System的单任务平均提升为16.8%,多任务为19.5%。

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