.png){kind=icon}
返回
Using Grid services to parallelize IBM's Generic Log Adapter
.png){kind=icon}
PRE
AI
DOI：10.1016/j.jss.2010.08.029.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Since their definition in the Open Grid Services Architecture, Grid services has been used in many Grid-enabled applications to leverage the computational power offered by Grid Systems. An important research issue addressed in this regard is how to increase the efficiency of the Grid services for a massive processing and scientific computing computations arising in data intensive computations, for example the processing of large log data files arising in problem determination in today's IT computing environments. In this paper we present an approach that uses Grid services to efficiently parallelize the IBM's Generic Log Adapter (GLA). GLA is a generic parsing engine shipped with the IBM's Autonomic Computing Toolkit that has been conceived to convert proprietary log data into a standard log data event-based format in real time. However, in order to provide generic support for parsing the majority of today's unstructured log data formats the GLA makes heavy use of regular expressions that incur in performance limitations. Until now all the approaches that have been proposed to increase GLA's performance have revolved around fine-tuning the set of regular expressions used to configure the GLA for a particular log data format or writing specific parsing code. In this work we propose a new approach consisting in transparently parallelizing the GLA by taking advantage of its internal architecture and the fact that structuring log data is a task that lends itself very well to parallelization. We present a Master-Worker strategy that uses Grid services to parallelize GLA efficiently and in a completely transparent way for the user. (C) 2010 Elsevier Inc. All rights reserved.
Keyword:
Grid services
Generic Log Adapter
Parallel processing
Log data files
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.1
论文数:
5.5K
被引数:
8.4K
机构
查看更多机构
引用论文
暂无论文信息