Computation and Storage in the Cloud

Understanding the Trade-Offs
Erscheinungsjahr 2013
ISBN: 978-0-12-407767-6
Verlag: William Andrew Publishing

Buch, Englisch, 128 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 180 g

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

Zielgruppe

<p>Researchers, practitioners, and graduate students in scientific computing seeking guidance for managing application datasets.</p>

Autoren/Hrsg.

Yuan, Dong

Yang, Yun

Chen, Jinjun

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbanken & das Web
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Cloud-Computing, Grid-Computing

Inhaltsverzeichnis

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

Über Autor(innen)

Yuan, Dong
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.

Yang, Yun
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.

Chen, Jinjun
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.