Bøger af Frederic Magoules

As more and more data is generated at a faster-than-ever rate, processing large volumes of data is becoming a challenge for data analysis software. Addressing performance issues, Cloud Computing: Data-Intensive Computing and Scheduling explores the evolution of classical techniques and describes completely new methods and innovative algorithms. The book delineates many concepts, models, methods, algorithms, and software used in cloud computing.After a general introduction to the field, the text covers resource management, including scheduling algorithms for real-time tasks and practical algorithms for user bidding and auctioneer pricing. It next explains approaches to data analytical query processing, including pre-computing, data indexing, and data partitioning. Applications of MapReduce, a new parallel programming model, are then presented. The authors also discuss how to optimize multiple group-by query processing and introduce a MapReduce real-time scheduling algorithm.A useful reference for studying and using MapReduce and cloud computing platforms, this book presents various technologies that demonstrate how cloud computing can meet business requirements and serve as the infrastructure of multidimensional data analysis applications.

Focusing on up-to-date artificial intelligence models to solve building energy problems, Artificial Intelligence for Building Energy Analysis reviews recently developed models for solving these issues, including detailed and simplified engineering methods, statistical methods, and artificial intelligence methods.

This book presents various technologies that demonstrate how cloud computing can meet business requirements and serve as the infrastructure of multidimensional data analysis applications. Addressing performance issues, it explores the evolution of classical techniques and describes completely new methods and innovative algorithms. The text covers scheduling algorithms for real-time tasks and practical algorithms for user bidding and auctioneer pricing. It explains approaches to data analytical query processing, including pre-computing, data indexing, and data partitioning. Applications of MapReduce, a new parallel programming model, are also presented.

Parallel Computing presents scientific computing techniques applied to parallel computing for the numerical simulation of large-scale problems, which result from systems modeled by partial differential equations.

An overview of the next generation in computing. It explores grid projects, core grid technologies, and applications of the grid. It covers virtual organizations, scheduling algorithms, fault tolerance strategies, grid workflow management systems, and security technologies.