Energy-Efficient Computing and Data Centers

The energy consumption issue in distributed computing systems raises various monetary, environmental and system performance concerns. Electricity consumption in the US doubled from 2000 to 2005. From a financial and environmental standpoint, reducing the consumption of electricity is important, yet these reforms must not lead to performance degradation of the computing systems. These contradicting constraints create a suite of complex problems that need to be resolved in order to lead to 'greener' distributed computing systems. This book brings together a group of outstanding researchers that investigate the different facets of green and energy efficient distributed computing. Key features: One of the first books of its kind Features latest research findings on emerging topics by well-known scientists Valuable research for grad students, postdocs, and researchers Research will greatly feed into other technologies and application domains

Nanomagnetic and spintronic computing devices are strong contenders for future replacements of CMOS. This is an important and rapidly evolving area with the semiconductor industry investing significantly in the study of nanomagnetic phenomena and in developing strategies to pinpoint and regulate nanomagnetic reliably with a high degree of energy efficiency. This timely book explores the recent and on-going research into nanomagnetic-based technology. Key features: Detailed background material and comprehensive descriptions of the current state-of-the-art research on each topic. Focuses on direct applications to devices that have potential to replace CMOS devices for computing applications such as memory, logic and higher order information processing. Discusses spin-based devices where the spin degree of freedom of charge carriers are exploited for device operation and ultimately information processing. Describes magnet switching methodologies to minimize energy dissipation. Comprehensive bibliographies included for each chapter enabling readers to conduct further research in this field. Written by internationally recognized experts, this book provides an overview of a rapidly burgeoning field for electronic device engineers, field-based applied physicists, material scientists and nanotechnologists. Furthermore, its clear and concise form equips readers with the basic understanding required to comprehend the present stage of development and to be able to contribute to future development. Nanomagnetic and Spintronic Devices for Energy-Efficient Memory and Computing is also an indispensable resource for students and researchers interested in computer hardware, device physics and circuits design.

Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning applications This book focuses on how to build energy-efficient hardware for neural networks with learning capabilities—and provides co-design and co-optimization methodologies for building hardware neural networks that can learn. Presenting a complete picture from high-level algorithm to low-level implementation details, Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design also covers many fundamentals and essentials in neural networks (e. g. , deep learning), as well as hardware implementation of neural networks. The book begins with an overview of neural networks. It then discusses algorithms for utilizing and training rate-based artificial neural networks. Next comes an introduction to various options for executing neural networks, ranging from general-purpose processors to specialized hardware, from digital accelerator to analog accelerator. A design example on building energy-efficient accelerator for adaptive dynamic programming with neural networks is also presented. An examination of fundamental concepts and popular learning algorithms for spiking neural networks follows that, along with a look at the hardware for spiking neural networks. Then comes a chapter offering readers three design examples (two of which are based on conventional CMOS, and one on emerging nanotechnology) to implement the learning algorithm found in the previous chapter. The book concludes with an outlook on the future of neural network hardware. Includes cross-layer survey of hardware accelerators for neuromorphic algorithms Covers the co-design of architecture and algorithms with emerging devices for much-improved computing efficiency Focuses on the co-design of algorithms and hardware, which is especially critical for using emerging devices, such as traditional memristors or diffusive memristors, for neuromorphic computing Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design is an ideal resource for researchers, scientists, software engineers, and hardware engineers dealing with the ever-increasing requirement on power consumption and response time. It is also excellent for teaching and training undergraduate and graduate students about the latest generation neural networks with powerful learning capabilities.

Over the last several years, manufacturers have expressed increasing interest in reducing their energy consumption and have begun to search for opportunities to reduce their energy usage. In this book, the authors explore a variety of opportunities to reduce the energy footprint of manufacturing. These opportunities cover the entire spatial scale of the manufacturing enterprise: from unit process-oriented approaches to enterprise-level strategies. Each chapter examines some aspect of this spatial scale, and discusses and describes the opportunities that exist at that level. Case studies demonstrate how the opportunity may be acted on with practical guidance on how to respond to these opportunities.

Highlighting sustainable catalytic processes in synthetic organic chemistry and industry, this useful guide places special emphasis on catalytic reactions carried out at room temperature. It describes the fundamentals, summarizes key advances, and covers applications in industrial processes in the field of energy generation from renewables, food science, and pollution control. Throughout, the latest research from various disciplines is combined, such as homogeneous and heterogeneous catalysis, biocatalysis, and photocatalysis. The book concludes with a chapter on future trends and energy challenges for the latter half of the 21st century. With its multidisciplinary approach this is an essential reference for academic and industrial researchers in catalysis science aiming to design more sustainable and energy-efficient processes.

The definitive guide to successfully integrating social, mobile, Big-Data analytics, cloud and IoT principles and technologies The main goal of this book is to spur the development of effective big-data computing operations on smart clouds that are fully supported by IoT sensing, machine learning and analytics systems. To that end, the authors draw upon their original research and proven track record in the field to describe a practical approach integrating big-data theories, cloud design principles, Internet of Things (IoT) sensing, machine learning, data analytics and Hadoop and Spark programming. Part 1 focuses on data science, the roles of clouds and IoT devices and frameworks for big-data computing. Big data analytics and cognitive machine learning, as well as cloud architecture, IoT and cognitive systems are explored, and mobile cloud-IoT-interaction frameworks are illustrated with concrete system design examples. Part 2 is devoted to the principles of and algorithms for machine learning, data analytics and deep learning in big data applications. Part 3 concentrates on cloud programming software libraries from MapReduce to Hadoop, Spark and TensorFlow and describes business, educational, healthcare and social media applications for those tools. The first book describing a practical approach to integrating social, mobile, analytics, cloud and IoT (SMACT) principles and technologies Covers theory and computing techniques and technologies, making it suitable for use in both computer science and electrical engineering programs Offers an extremely well-informed vision of future intelligent and cognitive computing environments integrating SMACT technologies Fully illustrated throughout with examples, figures and approximately 150 problems to support and reinforce learning Features a companion website with an instructor manual and PowerPoint slides www.wiley.com/go/hwangIOT Big-Data Analytics for Cloud, IoT and Cognitive Computing satisfies the demand among university faculty and students for cutting-edge information on emerging intelligent and cognitive computing systems and technologies. Professionals working in data science, cloud computing and IoT applications will also find this book to be an extremely useful working resource.

There is not a single industry which will not be transformed by machine learning and Internet of Things (IoT). IoT and machine learning have altogether changed the technological scenario by letting the user monitor and control things based on the prediction made by machine learning algorithms. There has been substantial progress in the usage of platforms, technologies and applications that are based on these technologies. These breakthrough technologies affect not just the software perspective of the industry, but they cut across areas like smart cities, smart healthcare, smart retail, smart monitoring, control, and others. Because of these “game changers,” governments, along with top companies around the world, are investing heavily in its research and development. Keeping pace with the latest trends, endless research, and new developments is paramount to innovate systems that are not only user-friendly but also speak to the growing needs and demands of society. This volume is focused on saving energy at different levels of design and automation including the concept of machine learning automation and prediction modeling. It also deals with the design and analysis for IoT-enabled systems including energy saving aspects at different level of operation. The editors and contributors also cover the fundamental concepts of IoT and machine learning, including the latest research, technological developments, and practical applications. Valuable as a learning tool for beginners in this area as well as a daily reference for engineers and scientists working in the area of IoT and machine technology, this is a must-have for any library.

A modern and unique perspective on solar and geothermal technologies for heating and cooling buildings This book will have a broad appeal reaching practising engineers in the industry as well as students. With introductory sections for each technology described, material includes chapters on: geothermal energy use for the heating and cooling of buildings; a chapter on electrically driven heat pumps/chillers; material on night radiative cooling, photovoltaic thermal collectors, temperature modelling and thin film photovoltaic modelling. Includes general introductory sections for each technology with market potential and applications Covers an increasingly important component of energy courses Considers a broad range of alternative renewable energy supplies relevant to the building sector, such as geothermal energy with heat pump With a special focus on solar cooling, provides detailed physical models of all technologies and example calculations Unique in covering the fundamentals of meteorological modelling

Discover grid computing-how to successfully build, implement, and manage widely distributed computing architecture With technology budgets under increasing scrutiny and system architecture becoming more and more complex, many organizations are rethinking how they manage and use technology. Keeping a strong business focus, this publication clearly demonstrates that the current ways of tying applications to dedicated hardware are no longer viable in today's competitive, bottom line-oriented environment. This evolution in distributed computing is leading a paradigm shift in leveraging widely distributed architectures to get the most processing power per IT dollar. Presenting a solid foundation of data management issues and techniques, this practical book delves into grid architecture, services, practices, and much more, including: * Why businesses should adopt grid computing * How to master the fundamental concepts and programming techniques and apply them successfully to reach objectives * How to maximize the value of existing IT investments The author has tailored this publication for two distinct audiences. Business professionals will gain a better understanding of how grid computing improves productivity and performance, what impact it can have on their organization's bottom line, and the technical foundations necessary to discuss grid computing with their IT colleagues. Following the author's expert guidance and practical examples, IT professionals, architects, and developers will be equipped to initiate and carry out successful grid computing projects within their own organizations.