Computation and Storage in the Cloud: Understanding the Trade-Offs

— —

Dong Yuan (Swinburne University of Technology, Melbourne, Australia) Yun Yang (Swinburne University of Technology, Melbourne, Australia)

Computation and Storage in the Cloud

Understanding the Trade-Offs

Dong Yuan (Swinburne University of Technology, Melbourne, Australia), Yun Yang (Swinburne University of Technology, Melbourne, Australia), Jinjun Chen (University of Technology, Sydney, Australia) , Jinjun Chen (University of Technology, Sydney, Australia)

$63.95

Paperback

Not in-store but you can order this
How long will it take?

Availability Information

We source books from suppliers in Australia and overseas. For books we don't currently have in stock, the time it takes to get them from our suppliers can vary widely - from a few days to a few months - so we check each book with each supplier to determine the expected time it will take to be supplied to us.

We then advise you accordingly. If the time taken to get any book is too long for you, you can let us know and we will cancel or adjust your order, and refund as required.

To find out the anticipated arrival time for specific items prior to ordering, please contact us by phone or email:

Phone +61 2 9264 3111, or 1800 4 BOOKS (1800 4 26657) if outside Sydney:
option 1 Abbey's Bookshop (Crime, History, Science, Kids & more) • info@abbeys.com.au
option 2 Language Book Centre (ESL & Foreign Languages) • language@abbeys.com.au
option 3 Galaxy Bookshop (Sci-fi, Fantasy, Romance, Graphic Novels) • sf@galaxybooks.com.au

QTY:

English

Elsevier Science Publishing Co Inc
01 February 2013

Parallel processing

Computation and Storage in the Cloud is the first comprehensive and systematic work investigating the issue of computation and storage trade-off in the cloud in order to reduce the overall application cost. Scientific applications are usually computation and data intensive, where complex computation tasks take a long time for execution and the generated datasets are often terabytes or petabytes in size. Storing valuable generated application datasets can save their regeneration cost when they are reused, not to mention the waiting time caused by regeneration. However, the large size of the scientific datasets is a big challenge for their storage. By proposing innovative concepts, theorems and algorithms, this book will help bring the cost down dramatically for both cloud users and service providers to run computation and data intensive scientific applications in the cloud.

Covers cost models and benchmarking that explain the necessary tradeoffs for both cloud providers and users

Describes several novel strategies for storing application datasets in the cloud

Includes real-world case studies of scientific research applications

By: Dong Yuan (Swinburne University of Technology Melbourne Australia), Yun Yang (Swinburne University of Technology, Melbourne, Australia), Jinjun Chen (University of Technology, Sydney, Australia), Jinjun Chen (University of Technology, Sydney, Australia), Jinjun Chen (University of Technology, Sydney, Australia)
Imprint: Elsevier Science Publishing Co Inc
Country of Publication: United States
Dimensions: Height: 229mm, Width: 152mm, Spine: 7mm
Weight: 180g
ISBN: 9780124077676
ISBN 10: 0124077676
Pages: 128
Publication Date: 01 February 2013
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

1. Introduction 2. Data management and cost-effectiveness 3. Motivating example and research 4. Cost model of dataset storage in the cloud 5. Minimum cost benchmarking approaches 6. Cost-effective dataset storage strategies 7. Evaluations8. Conclusions

Dong Yuan is a postdoctoral research fellow in the Centre for Computing and Engineering Software Systems, Swinburne University of Technology. His research interests include: Parallel and Distributed Computing; Cloud and Grid Computing; Data Management; Workflow Technology; Scientific Applications and E-Science; Service Computing and BPM. Yun Yang received a Master of Engineering degree from The University of Science and Technology of China, Hefei, China, in 1987, and a PhD degree from The University of Queensland, Brisbane, Australia, in 1992, all in computer science. He is currently a full Professor in the Faculty of Information and Communication Technologies at Swinburne University of Technology, Melbourne, Australia. Prior to joining Swinburne as an Associate Professor in late 1999, he was a Lecturer and Senior Lecturer at Deakin University during 1996-1999. Before that, he was a Research Scientist at DSTC - Cooperative Research Centre for Distributed Systems Technology during 1993-1996. He also worked at Beihang University in China during 1987-1988. He has published about 200 papers on journals and refereed conferences. His research interests include software engineering; p2p, grid and cloud computing; workflow systems; service-oriented computing; Internet computing applications; and CSCW. Jinjun Chen received his PhD degree in Computer Science and Software Engineering from Swinburne University of Technology, Melbourne, Australia in 2007. He is currently an Associate Professor in the Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia. His research interests include Scientific workflow management and applications, workflow management and applications in Web service or SOC environments, workflow management and applications in grid (service)/cloud computing environments, software verification and validation in workflow systems, QoS and resource scheduling in distributed computing systems such as cloud computing, service oriented computing, semantics and knowledge management, cloud computing.

Reviews for Computation and Storage in the Cloud: Understanding the Trade-Offs

Cloud computing systems charge for both data storage and for calculating, say Yuan, Yang.and Chen., so there is a trade-off between storing large data sets in the cloud or deleting them and regenerating then each time they are needed. They suggest some approaches to figuring out which is cheaper. they cover motivating example and research issues, a cost model of data set storage in the cloud, minimum cost benchmarking approaches,. --ProtoView.com, January 2014 Cloud computing systems charge for both data storage and for calculating, say Yuan, Yang..and Chen.so there is a trade-off between storing large data sets in the cloud or deleting them and regenerating then each time they are needed. They suggest some approaches to figuring out which is cheaper. --Reference & Research Book News, December 2013 .this book does a good job at tackling a variety of complex subjects. It brings forward state-of-the-art concepts and elaborate algorithms, illustrates issues related to cost-effectiveness, and helps both cloud providers and users get a grip on the intricate world of cloud computing. --Help Net Security online, August 28, 2013