國網中心邀請演講資訊

Hadoop 與雲端運算

雲端運算為 2008 年重大 IT 熱門議題，而 Hadoop 為 Apache Software Foundation 所開發之自由軟體，目前已廣泛應用於 Amazon 與 Yahoo! 等雲端運算服務提供者的格網架構之上。

Devaraj Das 是 Yahoo! Bangalore Grid Computing Group 的 Engineering Manager，亦為 Apache Committer，對於 Hadoop 有多年的開發經驗。此外， Yahoo! Bangalore Grid Computing Group 著重於如何打造足以處理 Peta-bytes 資料，由數千台主機組成的格網架構，將帶給中心從事格網相關研究的同仁來自於產業界的開發經驗分享。

講者簡歷

Devaraj Das (ddas@…) is the Engineering Manager of the Grid Computing group at Yahoo! Bangalore. He graduated with a Masters degree in Computer Science from Indian Institute of Science, Bangalore. Prior to Yahoo!, Devaraj was with HP. Devaraj is an Apache committer.

The Grid Computing group at Yahoo! Bangalore focuses on Grid frameworks that scale to thousands of machines and handle peta-bytes of data. The group is especially involved in the development of the Open Source Hadoop platform and its deployment within Yahoo!.

2008-11-04

• 時間：11/04 星期二　上午　11:00 - 12:30
• 地點：北群多媒體教室（南群如有需視訊連線，請與王耀聰連絡）
• 講員：Devaraj Das，Yahoo! Bangalore Grid Computing Group 的 Engineering Manager，亦為 Apache Committer
• 主題：Introduction to Hadoop and Cloud Computing

演講摘要

Hadoop (​http://hadoop.apache.org/), an open source volunteer project under the Apache Software Foundation, is a framework for running applications on large clusters built of commodity hardware. It lets one easily write and run applications that process vast amounts of data (terabytes to petabytes).

Hadoop implements a computational paradigm named Map-Reduce, where the application is divided into many small fragments of work, each of which may be executed or reexecuted on any node in the cluster. In addition, it provides a distributed file system (HDFS) that stores data on the compute nodes, providing very high aggregate bandwidth across the cluster. Yahoo! is one of the main contributors to Hadoop and uses it extensively to manage large clusters of machines.

I hope to engage the open-source community on Hadoop and encourage participation in its development. I will present an overview of Hadoop and its architecture with a focus on the Map-Reduce component. I will describe the engineering challenges and briefly talk about how Hadoop clusters are used in Yahoo!.