Computation and Storage in the Cloud
Understanding the Trade-Offs
Published on 1. February 2013
Book
Paperback/Softback
128 pages
978-0-12-407767-6 (ISBN)
Description
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
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
Reviews / Votes
"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 and 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
More details
Language
English
Place of publication
United States
Target group
Professional and scholarly
Researchers, practitioners, and graduate students in scientific computing seeking guidance for managing application datasets.
Product notice
Paperback (trade)
Dimensions
Height: 228 mm
Width: 151 mm
Thickness: 15 mm
Weight
193 gr
ISBN-13
978-0-12-407767-6 (9780124077676)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
E-Book
12/2012
Elsevier
€38.95
Available for download
Persons
Dong Yuan is currently a research fellow in School of Software and Electrical Engineering at Swinburne University of Technology, Melbourne, Australia. His research interests include data management in parallel and distributed systems, scheduling and resource management, grid and cloud computing. Yun Yang is currently a full professor in School of Software and Electrical Engineering at Swinburne University of Technology, Melbourne, Australia. Prior to joining Swinburne in 1999 as an associate professor, he was a lecturer and senior lecturer at Deakin University, Australia, during 1996-1999. He has coauthored four books and published over 200 papers in journals and refereed conference proceedings. He is currently on the Editorial Board of IEEE Transactions on Cloud Computing. His current research interests include software technologies, cloud computing, p2p/grid/cloud workflow systems, and service-oriented computing. 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.
Content
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