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.