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Temporal QOS management in scientific cloud workflow systems

ผู้แต่ง: Xiao Liu; Jinjun Chen; Yun Yang
สำนักพิมพ์: Waltham, MA : Elsevier, 2012.
ชุด: Elsevier insights.
ครั้งที่พิมพ์/รูปแบบ:   หนังสืออีเล็กทรอนิกส์ : เอกสาร : Englishดูครั้งที่พิมพ์และรูปแบบ
ฐานข้อมูล:WorldCat
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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  อ่านมากขึ้น…
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ประเภท/แบบฟอร์ม Electronic books
รูปแบบทางกายภาพเพิ่มเติม Print version:
(OCoLC)ocn761843513
ขนิดวัสดุ: เอกสาร, ทรัพยากรอินเตอร์เน็ต
ประเภทของเอกสาร: แหล่งข้อมูลอินเทอร์เน็ต, ไฟล์คอมพิวเตอร์
ผู้เขียนทั้งหมด : ผู้เขียนร่วม Xiao Liu; Jinjun Chen; Yun Yang
ISBN: 9780123970107 0123970105 9780123972958 0123972957
OCLC Number: 778786621
คำอธิบาย: 1 online resource (xiv, 140 p.)
สารบัญ: 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.
หัวข้อชุด: Elsevier insights.
ความรับผิดชอบ Xiao Liu, Jinjun Chen, Yun Yang.

บทคัดย่อ:

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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