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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.
Frédéric Magoulès is a professor at École Centrale Paris, where he leads the high performance computing research group. His research focuses on the algorithmic interface between parallel computing and the numerical analysis of PDEs and algebraic differential equations. He earned a Ph.D. in applied mathematics from Université Pierre et Marie Curie. Jie Pan is a Java developer at the Klee Group Company. She earned a Ph.D. in applied mathematics. During her doctoral work, she focused on large-scale data analysis on distributed systems. Fei Teng is a researcher in the Key Lab of Cloud Computing and Intelligent Technology at Southwest Jiaotong University. Her research interests are mainly in cloud computing, data mining, resource allocation, and distributed scheduling algorithms.
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. Real-Time Scheduling with MapReduce. Future for Cloud Computing. Bibliography. Index.
