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.

Cloud Computing: Data-Intensive Computing and Scheduling

Computers & Technology

Magoules, Frédéric

Pan, Jie

Teng, Fei

Computer Science

Chapman and Hall/CRC

Introduction to Grid Computing (Chapman & Hall/CRC Numerical Analysis and Scientific Computing Series)

A Thorough Overview of the Next Generation in Computing
 Poised to follow in the footsteps of the Internet, grid computing is on the verge of becoming more robust and accessible to the public in the near future. Focusing on this novel, yet already powerful, technology, Introduction to Grid Computing explores state-of-the-art grid projects, core grid technologies, and applications of the grid.
 After comparing the grid with other distributed systems, the book covers two important aspects of a grid system: scheduling of jobs and resource discovery and monitoring in grid. It then discusses existing and emerging security technologies, such as WS-Security and OGSA security, as well as the functions of grid middleware at a conceptual level. The authors also describe famous grid projects, demonstrate the pricing of European options through the use of the Monte Carlo method on grids, and highlight different parallelization possibilities on the grid.
 Taking a tutorial approach, this concise book provides a complete introduction to the components of the grid architecture and applications of grid computing. It expertly shows how grid computing can be used in various areas, from computational mechanics to risk management in financial institutions.

Tan, Kiat-An

Kumar, Abhinit

CRC-Press

Grid technology offers the potential for providing secure access to remote services, thereby promoting scientific collaborations in an unprecedented scale. Grid Resource Management: Toward Virtual and Services Compliant Grid Computing presents a comprehensive account of the architectural issues of grid technology, such as security, data management, logging, and aggregation of services, as well as related technologies. After covering grid usages, grid systems, and the evolution of grid computing, the book discusses operational issues associated with web services and service-oriented architecture. It also explores technical and business topics relevant to data management, the development and characteristics of P2P systems, and a grid-enabled virtual file system (GRAVY) that integrates underlying heterogeneous file systems into a unified location-transparent file system of the grid. The book covers scheduling algorithms, strategies, problems, and architectures as well as workflow management systems and semantic technologies. In addition, the authors describe how to deploy scientific applications into a grid environment. They also explain grid engineering and grid service programming. Examining both data and execution management in grid computing, this book chronicles the current trend of grid developments toward a more service-oriented approach that exposes grid protocols using web services standards.

Grid Resource Management: Toward Virtual and Services Compliant Grid Computing (Chapman & Hall/CRC Numerical Analysis and Scientific Computing Series)

Yu, Lei

Nguyen, Thi-Mai-Huong

Sports & Outdoors

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.

Cloud Computing: Data-Intensive Computing and Scheduling (Chapman & Hall/CRC Numerical Analysis and Scientific Computing)

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. The text also simulates energy consumption profiles for single and multiple buildings. Based on these datasets, Support Vector Machine (SVM) models are trained and tested to do the prediction. Suitable for novice, intermediate, and advanced readers, this is a vital resource for building designers, engineers, and students.

Data Mining and Machine Learning in Building Energy Analysis (Computer Engineering)

Zhao, Hai-Xiang

Wiley-ISTE

Scientific computing has become an indispensable tool in numerous fields, such as physics, mechanics, biology,finance and industry. For example, it enables us, thanks to efficient algorithms adapted to current computers, tosimulate, without the help of models or experimentations, the deflection of beams in bending, the sound level in a theater room or a fluid flowing around an aircraft wing.This book presents the scientific computing techniques applied to parallel computing for the numerical simulation of large-scale problems; these problems result from systems modeled by partial differential equations. Computing concepts will be tackled via examples.Implementation and programming techniques resulting from the finite element method will be presented for direct solvers, iterative solvers and domain decomposition methods, along with an introduction to MPI and OpenMP.

Parallel Scientific Computing (Computer Engineering)

Houzeaux, Guillaume

Roux, François-Xavier

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Books by Magoules, Frédéric