Cloud computing can provide virtually unlimited scalable high performance computing resources. Cloud workflows often underlie many large scale data/computation intensive e-science applications such as earthquake modelling, weather forecasting and astrophysics. During application modelling, these sophisticated processes are redesigned as cloud workflows, and at runtime, the models are executed by employing the supercomputing and data sharing ability of the underlying cloud computing infrastructures. Temporal QOS Management in Scientific Cloud Workflow Systems focuses on real world scientific applications which often must be completed by satisfying a set of temporal constraints such as milestones and deadlines. Meanwhile, activity duration, as a measurement of system performance, often needs to be monitored and controlled. This book demonstrates how to guarantee on-time completion of most, if not all, workflow applications. Offering a comprehensive framework to support the lifecycle of time-constrained workflow applications, this book will enhance the overall performance and usability of scientific cloud workflow systems.
<ul>
<li>Explains how to reduce the cost to detect and handle temporal violations while delivering high quality of service (QoS) </li>
<li>Offers new concepts, innovative strategies and algorithms to support large-scale sophisticated applications in the cloud </li>
<li>Improves the overall performance and usability of cloud workflow systems</li>
</ul>

Temporal QOS Management in Scientific Cloud Workflow Systems (Elsevier Insights)

Computers & Technology

Liu, Xiao

Yang Yun

Chen, Jinjun

Computer Science

Elsevier

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. <ul>
<li>Covers cost models and benchmarking that explain the necessary tradeoffs for both cloud providers and users</li>
<li>Describes several novel strategies for storing application datasets in the cloud</li>
<li>Includes real-world case studies of scientific research applications</li> </ul>
<ul>
<li>Covers cost models and benchmarking that explain the necessary tradeoffs for both cloud providers and users</li> <li>Describes several novel strategies for storing application datasets in the cloud</li> <li>Includes real-world case studies of scientific research applications</li> </ul>

Computation and Storage in the Cloud: Understanding the Trade-Offs (Elsevier Insights)

Yuan, Dong

With the rapid growth of Cloud computing, the size of Cloud data is expanding at a dramatic speed. A huge amount of data is generated and processed by Cloud applications, putting a higher demand on cloud storage. While data reliability should already be a requirement, data in the Cloud needs to be stored in a highly cost-effective manner. This book focuses on the trade-off between data storage cost and data reliability assurance for big data in the Cloud. Throughout the whole Cloud data lifecycle, four major features are presented: first, a novel generic data reliability model for describing data reliability in the Cloud; second, a minimum replication calculation approach for meeting a given data reliability requirement to facilitate data creation; third, a novel cost-effective data reliability assurance mechanism for big data maintenance, which could dramatically reduce the storage space needed in the Cloud; fourth, a cost-effective strategy for facilitating data creation and recovery, which could significantly reduce the energy consumption during data transfer.

Reliability Assurance of Big Data in the Cloud: Cost-Effective Replication-Based Storage

Li, Wenhao

Morgan Kaufmann

Cloud computing is the latest market-oriented computing paradigm which brings software design and development into a new era characterized by "XaaS", i.e. everything as a service. Cloud workflows, as typical software applications in the cloud, are composed of a set of partially ordered cloud software services to achieve specific goals. However, due to the low QoS (quality of service) nature of the cloud environment, the design of workflow systems in the cloud becomes a challenging issue for the delivery of high quality cloud workflow applications. To address such an issue, this book presents a systematic investigation to the three critical aspects for the design of a cloud workflow system, viz. system architecture, system functionality and quality of service. Specifically, the system architecture for a cloud workflow system is designed based on the general four-layer cloud architecture, viz. application layer, platform layer, unified resources layer and fabric layer. The system functionality for a cloud workflow system is designed based on the general workflow reference model but with significant extensions to accommodate software services in the cloud. The support of QoS is critical for the quality of cloud workflow applications. This book presents a generic framework to facilitate a unified design and development process for software components that deliver lifecycle support for different QoS requirements. While the general QoS requirements for cloud workflow applications can have many dimensions, this book mainly focuses on three of the most important ones, viz. performance, reliability and security. In this book, the architecture, functionality and QoS management of our SwinDeW-C prototype cloud workflow system are demonstrated in detail as a case study to evaluate our generic design for cloud workflow systems. To conclude, this book offers a general overview of cloud workflow systems and provides comprehensive i

The Design of Cloud Workflow Systems (SpringerBriefs in Computer Science)

HE QIANG

Zhang, Gaofeng

Cao, Dahai

Networking & Cloud Computing

Springer

Gold Fish Boy (Korean Edition)

Lisa Thompson

black hole

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