Video Processing in the Cloud
As computer systems evolve, the volume of data to be processed increases significantly, either as a consequence of the expanding amount of available information, or due to the possibility of performing highly complex operations that were not feasible in t
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Series Editors Stan Zdonik Peng Ning Shashi Shekhar Jonathan Katz Xindong Wu Lakhmi C Jain David Padua Xuemin Shen Borko Furht
For further volumes: http://www.springer.com/series/10028
Rafael Silva Pereira Karin K. Breitman •
Video Processing in the Cloud
123
Rafael Silva Pereira Webmedia, Globo.com Rio de Janeiro Brazil e-mail: [email protected]
ISSN 2191-5768 ISBN 978-1-4471-2136-7 DOI 10.1007/978-1-4471-2137-4
Dr. Karin K. Breitman Department of Informatics Pontifcia Universidade Catlica do Rio de Janeiro Rio de Janeiro Brazil e-mail: [email protected]
e-ISSN 2191-5776 e-ISBN 978-1-4471-2137-4
Springer London Dordrecht Heidelberg New York British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Ó Rafael Silva Pereira 2011 Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licenses issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the publishers. The use of registered names, trademarks, etc., in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free for general use. The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made. Cover design: eStudio Calamar, Berlin/Figueres Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
The Map Reduce approach, proposed by Dean and Ghemawat [10], is an efficient way for processing very large datasets using a computer cluster and, more recently, cloud infrastructures. Traditional Map Reduce implementations, however, provide neither the necessary flexibility (to choose among different encoding techniques in the mapping stage) nor control (to specify how to organize results in the reducing stage), required to process video files. The Split&Merge tool, presented in this book, generalizes the Map Reduce paradigm, and provides an efficient solution that contemplates relevant aspects of intense processing video applications.
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Contents
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Introduction . . . . . . . . 1.1 Context . . . . . . . . 1.2 Goals . . . . . . . . . . 1.3 Main Contributions
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Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Cloud Computing Paradigms. . . . . . . . . . . . . .
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