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This timely text/reference presents a comprehensive review of the
workflow scheduling algorithms and approaches that are rapidly
becoming essential for a range of software applications, due to
their ability to efficiently leverage diverse and distributed cloud
resources. Particular emphasis is placed on how workflow-based
automation in software-defined cloud centers and hybrid IT systems
can significantly enhance resource utilization and optimize energy
efficiency. Topics and features: describes dynamic workflow and
task scheduling techniques that work across multiple (on-premise
and off-premise) clouds; presents simulation-based case studies,
and details of real-time test bed-based implementations; offers
analyses and comparisons of a broad selection of static and dynamic
workflow algorithms; examines the considerations for the main
parameters in projects limited by budget and time constraints;
covers workflow management systems, workflow modeling and
simulation techniques, and machine learning approaches for
predictive workflow analytics. This must-read work provides
invaluable practical insights from three subject matter experts in
the cloud paradigm, which will empower IT practitioners and
industry professionals in their daily assignments. Researchers and
students interested in next-generation software-defined cloud
environments will also greatly benefit from the material in the
book.
This timely text/reference presents a comprehensive review of the
workflow scheduling algorithms and approaches that are rapidly
becoming essential for a range of software applications, due to
their ability to efficiently leverage diverse and distributed cloud
resources. Particular emphasis is placed on how workflow-based
automation in software-defined cloud centers and hybrid IT systems
can significantly enhance resource utilization and optimize energy
efficiency. Topics and features: describes dynamic workflow and
task scheduling techniques that work across multiple (on-premise
and off-premise) clouds; presents simulation-based case studies,
and details of real-time test bed-based implementations; offers
analyses and comparisons of a broad selection of static and dynamic
workflow algorithms; examines the considerations for the main
parameters in projects limited by budget and time constraints;
covers workflow management systems, workflow modeling and
simulation techniques, and machine learning approaches for
predictive workflow analytics. This must-read work provides
invaluable practical insights from three subject matter experts in
the cloud paradigm, which will empower IT practitioners and
industry professionals in their daily assignments. Researchers and
students interested in next-generation software-defined cloud
environments will also greatly benefit from the material in the
book.
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