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Temporal QOS management in scientific cloud workflow systems

Author: Xiao Liu; Jinjun Chen; Yun Yang
Publisher: Waltham, MA : Elsevier, 2012.
Series: Elsevier insights.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
(OCoLC)ocn761843513
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Xiao Liu; Jinjun Chen; Yun Yang
ISBN: 9780123970107 0123970105 9780123972958 0123972957
OCLC Number: 778786621
Description: 1 online resource (xiv, 140 p.)
Contents: Chapter 1 Introduction --
Chapter 2 Literature Review and Problem Analysis --
Chapter 3 A Scientific Cloud Workflow System --
Chapter 4 Novel Probabilistic Temporal Framework --
Chapter 5 Forecasting Scientific Cloud Workflow Activity Duration Intervals --
Chapter 6 Temporal Constraint Setting --
Chapter 7 Temporal Checkpoint Selection and Temporal Verification --
Chapter 8 Temporal Violation Handling Point Selection --
Chapter 9 Temporal Violation Handling --
Chapter 10 Conclusions and Contribution Bibliography.
Series Title: Elsevier insights.
Responsibility: Xiao Liu, Jinjun Chen, Yun Yang.

Abstract:

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. Explains how to reduce the cost to detect and handle temporal violations while delivering high quality of service (QoS) Offers new concepts, innovative strategies and algorithms to support large-scale sophisticated applications in the cloud Improves the overall performance and usability of cloud workflow systems.

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