Frederic Magoules, Jie Pan, Fei Teng
Chapman and Hall/CRC
Published September 20, 2012
Reference - 231 Pages - 48 B/W Illustrations
ISBN 9781466507821 - CAT# K14685
Series: Chapman & Hall/CRC Numerical Analysis and Scientific Computing Series
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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.
Overview of Cloud Computing
Resource Scheduling for Cloud Computing
Game Theoretical Allocation in a Cloud Datacenter
Multidimensional Data Analysis in a Cloud Datacenter
Data-Intensive Applications with MapReduce
Large-Scale Multidimensional Data Aggregation
Multidimensional Data Analysis Optimization
Improvements by speed-up measurements
Improvements by affecting factors
Improvement by cost estimation
Compressed data structures
Real-Time Scheduling with MapReduce
Future for Cloud Computing