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Computation and Storage in the Cloud: Understanding the Trade-Offs (Elsevier Insights), by Dong Yuan, Yun Yang, Jinjun Chen
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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
- Sales Rank: #2136318 in eBooks
- Published on: 2012-12-31
- Released on: 2012-12-31
- Format: Kindle eBook
Review
"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
From the Back Cover
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.
About the Author
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.
Most helpful customer reviews
0 of 0 people found the following review helpful.
A VERY COMPREHENSIVE AND SYSTEMATIC WORK!!
By COSMIC TRAVELER
Are you a cloud service provider? If you are, then this book is for you! Authors Dong Yuan, Yun Yang and Jinjun Chen, have done an outstanding job of writing a book that shows you how to reduce the cost of storing large generated application data sets in the cloud.
Authors Yuan, Yang and Chen, begin by introducing the background and key issues of computation and storage in the cloud. Next, they focus on data management in some traditional scientific application systems (grid systems), and then move on to the cloud. Then, the authors examine a motivating example: a real world scientific application for astrophysics that is used for searching for pulsars in the universe. They continue by giving a classification of the application data in the cloud, and propose an important concept of data dependency graph. Next, the authors show you how to develop novel minimum cost benchmarking approaches with algorithms for the best trade-off between computation and storage in the cloud. Then, they show you how to develop innovative cost-effective storage strategies for a user to facilitate at run-time in the cloud. The authors continue by demonstrating experiment results to evaluate their work in cloud computing simulation environments (SwimCloud). Finally, the authors summarize the new ideas presented in this book and the major contributions of their research.
This most excellent book 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. The authors also proposed innovative concepts (theorems and algorithms) in this great book that helped bring the cost down dramatically for both cloud users and service providers to run computation and data-intensive scientific applications in the cloud.
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