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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.
Global warming is the most dreaded problem of new millennium.
Greenhouse gases (GHGs) are mainly responsible for global warming.
Electricity generation based on fossil fuel is major source of
emission of GHGs. In India about 52% of total electricity is
generated using coal. Indian agriculture consumes about 30% of its
total electricity. There is vast scope in minimizing the GHG
emissions caused by power consumption in agriculture by adopting
energy efficiency tool like micro irrigation. India is the world's
largest producer of banana having about 27% share of the global
output. Banana is one of the major consumers of water and energy.
Hence, a pilot study was conducted on banana crop in Jalgaon,
Maharashtra for evaluating energy efficiency micro irrigation
system (MIS) and flood irrigation (FI). The study revealed water
and electricity saving in micro irrigation against the flood
irrigation. Also, potential of gaining carbon credits by switching
from FI to MIS for 5,000 ha area under banana crop was 11,750 tCO2e
or carbon credits. Thus, micro irrigation has a definite role in
minimising impacts of climate change on Indian agriculture by
reducing consumption of water and energy.
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