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 sophisticatedapplications in the cloud Improves the
overall performance and usability of cloud workflow systems"
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