Library of American University of Madaba
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Khan, Samee Ullah.
Evolutionary Based Solutions for Green Computing [electronic resource] / edited by Samee Ullah Khan, Joanna Kołodziej, Juan Li, Albert Y. Zomaya. - XX, 256 p. 91 illus. online resource. - Studies in Computational Intelligence, 432 1860-949X ; .
Today’s highly parameterized large-scale distributed computing systems may be composed of a large number of various components (computers, databases, etc) and must provide a wide range of services. The users of such systems, located at different (geographical or managerial) network cluster may have a limited access to the system’s services and resources, and different, often conflicting, expectations and requirements. Moreover, the information and data processed in such dynamic environments may be incomplete, imprecise, fragmentary, and overloading. All of the above mentioned issues require some intelligent scalable methodologies for the management of the whole complex structure, which unfortunately may increase the energy consumption of such systems. This book in its eight chapters, addresses the fundamental issues related to the energy usage and the optimal low-cost system design in high performance ``green computing’’ systems. The recent evolutionary and general metaheuristic-based solutions for energy optimization in data processing, scheduling, resource allocation, and communication in modern computational grids, could and network computing are presented along with several important conventional technologies to cover the hot topics from the fundamental theory of the ‘’green computing’’ concept and to describe the basic architectures of systems. This book points out the potential application areas and provides detailed examples of application case studies in low-energy computational systems. The development trends and open research issues are also outlined. All of those technologies have formed the foundation for the green computing that we know of today.
9783642306594
Engineering.
Artificial intelligence.
Renewable energy sources.
Engineering.
Computational Intelligence.
Artificial Intelligence (incl. Robotics).
Renewable and Green Energy.
Renewable and Green Energy.
006.3
Evolutionary Based Solutions for Green Computing [electronic resource] / edited by Samee Ullah Khan, Joanna Kołodziej, Juan Li, Albert Y. Zomaya. - XX, 256 p. 91 illus. online resource. - Studies in Computational Intelligence, 432 1860-949X ; .
Today’s highly parameterized large-scale distributed computing systems may be composed of a large number of various components (computers, databases, etc) and must provide a wide range of services. The users of such systems, located at different (geographical or managerial) network cluster may have a limited access to the system’s services and resources, and different, often conflicting, expectations and requirements. Moreover, the information and data processed in such dynamic environments may be incomplete, imprecise, fragmentary, and overloading. All of the above mentioned issues require some intelligent scalable methodologies for the management of the whole complex structure, which unfortunately may increase the energy consumption of such systems. This book in its eight chapters, addresses the fundamental issues related to the energy usage and the optimal low-cost system design in high performance ``green computing’’ systems. The recent evolutionary and general metaheuristic-based solutions for energy optimization in data processing, scheduling, resource allocation, and communication in modern computational grids, could and network computing are presented along with several important conventional technologies to cover the hot topics from the fundamental theory of the ‘’green computing’’ concept and to describe the basic architectures of systems. This book points out the potential application areas and provides detailed examples of application case studies in low-energy computational systems. The development trends and open research issues are also outlined. All of those technologies have formed the foundation for the green computing that we know of today.
9783642306594
Engineering.
Artificial intelligence.
Renewable energy sources.
Engineering.
Computational Intelligence.
Artificial Intelligence (incl. Robotics).
Renewable and Green Energy.
Renewable and Green Energy.
006.3