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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.3/201452
- Title
- Temporal QoS management in scientific cloud workflow systems
- Author(s)
- Liu, Xiao; Yang, Yun; Chen, Jinjun
- 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.
- Publication type
- Book
- Research centre
- Swinburne University of Technology. Faculty of Information and Communication Technologies. Centre for Computing and Engineering Software Systems
- Publication year
- 2012
- Keyword(s)
- Costs; Management; QoS; Quality of service; Scientific cloud workflow systems
- Publisher
- Elsevier
- ISBN
- 9780123970107
- Publisher URL
- http://store.elsevier.com/Temporal-QOS-Management-in-Scientific-Cloud-Workflow-Systems/Xiao-Liu/isbn-9780123970107/
- Copyright
- Copyright © 2012 Elsevier Inc.
- Research Projects
-
Novel cloud computing based workflow technology for managing large numbers of process instances, Australian Research Council grant number LP0990393
Cost effective storage of massive intermediate data in cloud computing applications, Australian Research Council grant number DP110101340
- Peer reviewed
