Fr. 64.00

Large scale data processing in Hadoop MapReduce scenario - Time estimation and computation models

English, German · Paperback / Softback

Shipping usually within 2 to 3 weeks (title will be printed to order)

Description

Read more

Cloud Computing has brought a huge impact in IT industry. Computing resources are easier to get in Cloud Computing. Briefly speaking, Cloud Computing is a resource pool, which contains a masssive amount of interconnected computers. Under such background, in order to make full use of the network, Google initiated MapReduce model. This model is an implementation of Parallel Computing, which aims at processing large amount of data. Given certain computing resources and MapReduce model, this book gives some thinking about how to estimate the time consumption of a huge computation task. Based on classical Parallel Computing theories, this book proposed two models to estimate the time consumption. It also gives conclusions about what type of computation task is estimatable. The experiments in this book are easy to implement, which are very suitable references for Cloud Computing fans.

About the author










Li Jian is a master student from Agder University(UiA), who is currently working as a technology consultant in NEVER.NO AS, Norway. His main research area is Cloud Computing. He is now applying Amazon Cloud to broadcasting industry.

Product details

Authors Li Jian
Publisher LAP Lambert Academic Publishing
 
Languages English, German
Product format Paperback / Softback
Released 17.07.2012
 
EAN 9783659155161
ISBN 978-3-659-15516-1
No. of pages 68
Subject Natural sciences, medicine, IT, technology > IT, data processing > Internet

Customer reviews

No reviews have been written for this item yet. Write the first review and be helpful to other users when they decide on a purchase.

Write a review

Thumbs up or thumbs down? Write your own review.

For messages to CeDe.ch please use the contact form.

The input fields marked * are obligatory

By submitting this form you agree to our data privacy statement.