Exploiting resource profiling mechanism for large-scale scientific computing on grids

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• 作者：Md. Azam Hossain ; Cao Ngoc Nguyen ; Jik-Soo Kim ; Soonwook Hwang
• 关键词：Resource profiling ; Job scheduling ; Grid computing ; Scout ; Scientific computing
• 刊名：Cluster Computing
• 出版年：2016
• 出版时间：September 2016
• 年：2016
• 卷：19
• 期：3
• 页码：1527-1539
• 全文大小：2,491 KB
• 刊物类别：Computer Science
• 刊物主题：Processor Architectures
  Operating Systems
  Computer Communication Networks
  
• 出版者：Springer Netherlands
• ISSN：1573-7543
• 卷排序：19

文摘

Large-scale scientific applications from various scientific domains (e.g., astronomy, physics, pharmaceuticals, chemistry, etc.) usually require substantial amounts of computing resources and storage space. International Grid computing resources can be a viable choice for supporting these challenging applications so that effectively locating suitable computing resources with minimal allocation overhead can be crucial. However, Grid resource availability is highly unstable and current Grid Information Service (GIS) cannot provide accurate state information. This can make it very difficult for users to schedule the jobs on the Grid system and to map tasks on appropriate available resources. In this paper, we present SCOUT system that can periodically profile Grid computing elements based on available number of CPU cores and average response time, and monitor the performance of each CE in the Virtual Organizations (VO). Micro-benchmark experimental results demonstrate that leveraging profiled data by SCOUT can improve the success rate of task executions and reduce the average response time.